diff --git a/gtfs_funnel/stop_times_with_direction.py b/gtfs_funnel/stop_times_with_direction.py
index 57a4679fb..d8d78c982 100644
--- a/gtfs_funnel/stop_times_with_direction.py
+++ b/gtfs_funnel/stop_times_with_direction.py
@@ -9,6 +9,8 @@
 import numpy as np
 import pandas as pd
 
+from typing import Literal
+
 from calitp_data_analysis import utils
 from shared_utils import rt_utils
 from segment_speed_utils import helpers, wrangle_shapes
@@ -79,15 +81,31 @@ def find_prior_stop(
             .groupby("trip_instance_key")
             .stop_sequence
             .shift(1)
+        ),
+        subseq_stop_sequence = (
+            prior_stop
+            .sort_values(["trip_instance_key", "stop_sequence"])
+            .groupby("trip_instance_key")
+            .stop_sequence
+            .shift(-1)        
         )
     )
     
-    prior_stop_geom = stop_times[
-        ["trip_instance_key", "stop_sequence", "geometry"]
-    ].rename(columns = {
-        "stop_sequence": "prior_stop_sequence",
-        "geometry": "prior_geometry"
-    }).set_geometry("prior_geometry").repartition(npartitions=1)
+    def renamed_geom_stop_times(
+        stop_times: dd.DataFrame, 
+        suffix: Literal["prior", "subseq"]
+    ) -> dd.DataFrame:
+    
+        renamed_stop_geom = stop_times[
+            ["trip_instance_key", "stop_sequence", "geometry"]
+        ].rename(columns = {
+            "stop_sequence": f"{suffix}_stop_sequence",
+            "geometry": f"{suffix}_geometry"
+        }).set_geometry(f"{suffix}_geometry").repartition(npartitions=1)
+        
+        return renamed_stop_geom
+    
+    prior_stop_geom = renamed_geom_stop_times(stop_times, suffix="prior")
     
     stop_times_with_prior = dd.merge(
         stop_times,
@@ -101,7 +119,10 @@ def find_prior_stop(
         prior_stop_geom,
         on = ["trip_instance_key", "prior_stop_sequence"],
         how = "left"
-    ).astype({"prior_stop_sequence": "Int64"})
+    ).astype({
+        "prior_stop_sequence": "Int64",
+        "subseq_stop_sequence": "Int64"
+    })
     
     return stop_times_with_prior_geom
 
diff --git a/gtfs_funnel/update_vars.py b/gtfs_funnel/update_vars.py
index 8faa8bb12..8ccbc59cd 100644
--- a/gtfs_funnel/update_vars.py
+++ b/gtfs_funnel/update_vars.py
@@ -3,12 +3,15 @@
 from shared_utils import rt_dates
 
 months = [
+    "mar", "apr", "may",
+    "jun", "jul", "aug",
+    "sep",
     "oct"
 ]         
 
 analysis_date_list = [
-    rt_dates.DATES["oct2023a"],
-    rt_dates.DATES["oct2023b"]
+    #rt_dates.DATES["sep2023"],
+    rt_dates.DATES[f"{m}2023"] for m in months
 ]
 
 CONFIG_PATH = Path("config.yml")
diff --git a/rt_segment_speeds/23_project_all_vp_explore.ipynb b/rt_segment_speeds/23_project_all_vp_explore.ipynb
index 190bc613d..4f682dd7f 100644
--- a/rt_segment_speeds/23_project_all_vp_explore.ipynb
+++ b/rt_segment_speeds/23_project_all_vp_explore.ipynb
@@ -17,13 +17,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "73ced776-d521-4467-beb4-8d67d147aea4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DataTransformerRegistry.enable('default')"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import altair as alt\n",
-    "import dask.dataframe as dd\n",
     "import geopandas as gpd\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -31,13 +41,16 @@
     "from segment_speed_utils import helpers\n",
     "from segment_speed_utils.project_vars import SEGMENT_GCS, PROJECT_CRS\n",
     "from shared_utils import rt_dates\n",
+    "from calitp_data_analysis import calitp_color_palette as cp\n",
+    "\n",
+    "analysis_date = rt_dates.DATES[\"sep2023\"]\n",
     "\n",
-    "analysis_date = rt_dates.DATES[\"sep2023\"]"
+    "alt.data_transformers.disable_max_rows()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "c289f689-c0fe-4ea2-9e14-4e80f77566ae",
    "metadata": {},
    "outputs": [],
@@ -62,7 +75,8 @@
     "# Find whether it's loop or inlining\n",
     "shapes_loop_inlining = pd.read_parquet(\n",
     "    f\"{SEGMENT_GCS}stop_segments_{analysis_date}.parquet\",\n",
-    "    columns = [\"shape_array_key\", \"loop_or_inlining\"]\n",
+    "    columns = [\"shape_array_key\", \"loop_or_inlining\"],\n",
+    "    filters = [[(\"loop_or_inlining\", \"==\", 1)]]\n",
     ").drop_duplicates().merge(\n",
     "    trip_to_shape,\n",
     "    on = \"shape_array_key\",\n",
@@ -72,185 +86,1171 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1b0ba620-e24e-4197-b665-c6111ad03307",
+   "execution_count": 3,
+   "id": "b95e35da-cf34-4696-80a1-0ab3a9e99ebf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ok_trips = (shapes_loop_inlining[\n",
-    "    shapes_loop_inlining.loop_or_inlining==0]\n",
-    "            .sample(25).trip_instance_key.tolist()\n",
-    "           )\n",
-    "\n",
-    "ok_trips"
+    "loopy_trips = shapes_loop_inlining.trip_instance_key.tolist()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b95e35da-cf34-4696-80a1-0ab3a9e99ebf",
+   "execution_count": 4,
+   "id": "20eb7d88-63e5-4695-82fd-141e8712396d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "loopy_trips = (shapes_loop_inlining[\n",
-    "    shapes_loop_inlining.loop_or_inlining==1]\n",
-    "            .sample(25).trip_instance_key.tolist()\n",
-    "           )\n",
+    "def merge_vp_with_projected_shape_meters(\n",
+    "    analysis_date,\n",
+    "    vp_filters: tuple\n",
+    "):\n",
+    "    projected_shape_meters = pd.read_parquet(\n",
+    "        f\"{SEGMENT_GCS}projection/vp_projected_{analysis_date}.parquet\"\n",
+    "    )\n",
     "\n",
-    "loopy_trips"
+    "    vp = pd.read_parquet(\n",
+    "        f\"{SEGMENT_GCS}vp_usable_{analysis_date}\",\n",
+    "        filters = vp_filters,\n",
+    "        columns = [\"gtfs_dataset_key\", \"gtfs_dataset_name\", \n",
+    "                   \"vp_idx\", \"trip_instance_key\", \"trip_id\"]\n",
+    "    ).merge(\n",
+    "        projected_shape_meters,\n",
+    "        on = \"vp_idx\",\n",
+    "        how = \"inner\"\n",
+    "    )\n",
+    "    \n",
+    "    return vp"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "276cab58-94ff-4f72-a651-88a4f1ea890a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "subset_trips = ok_trips + loopy_trips\n",
-    "\n",
-    "projected_shape_meters = pd.read_parquet(\n",
-    "    f\"{SEGMENT_GCS}projection/vp_projected_{analysis_date}.parquet\"\n",
-    ")\n",
-    "\n",
-    "vp = pd.read_parquet(\n",
-    "    f\"{SEGMENT_GCS}vp_usable_{analysis_date}\",\n",
-    "    filters = [[(\"trip_instance_key\", \"in\", subset_trips)]]\n",
-    ").merge(\n",
-    "    projected_shape_meters,\n",
-    "    on = \"vp_idx\",\n",
-    "    how = \"inner\"\n",
-    ").drop(columns = \"location_timestamp\")"
+    "vp = merge_vp_with_projected_shape_meters(\n",
+    "    analysis_date,\n",
+    "    vp_filters = [[(\"trip_instance_key\", \"in\", loopy_trips)]]\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "9d8f339f-f832-45f2-9f2c-f96125799a38",
+   "execution_count": 6,
+   "id": "1fbfe823-6a98-40b8-8c71-87cf686e5830",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_shape_meters(df: pd.DataFrame, one_trip: str):\n",
-    "    \"\"\"\n",
-    "    Plot how the projected shape meters looks for one trip.\n",
-    "    \n",
-    "    vp_idx is ordered by timestamp, use as x.\n",
-    "    \"\"\"\n",
-    "    subset_df = df[df.trip_instance_key==one_trip]\n",
-    "    \n",
-    "    print(f\"{subset_df.gtfs_dataset_name.iloc[0]}\")\n",
-    "    print(f\"trip_instance_key: {one_trip}, trip_id: {subset_df.trip_id.iloc[0]}\")\n",
+    "#https://stackoverflow.com/questions/10226551/whats-the-most-pythonic-way-to-calculate-percentage-changes-on-a-list-of-numbers\n",
+    "def pct_change(arr):\n",
+    "    if not isinstance(arr, np.ndarray):\n",
+    "        arr = np.asarray(arr)\n",
+    "        \n",
+    "    pct = np.zeros_like(arr)\n",
+    "    pct[1:] = np.diff(arr) / np.abs(arr[:-1])\n",
     "    \n",
-    "    chart = (alt.Chart(subset_df)\n",
-    "             .mark_line()\n",
-    "             .encode(\n",
-    "                 x=\"vp_idx\",\n",
-    "                 y=\"shape_meters:Q\"\n",
-    "             )\n",
-    "            )\n",
-    "       \n",
-    "    display(chart)\n"
+    "    return pct * 100\n",
+    "\n",
+    "def number_of_elements_over_threshold(\n",
+    "    arr, \n",
+    "    pct_change_threshold\n",
+    "):\n",
+    "    return len((pct_change(arr) > pct_change_threshold).nonzero()[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "6aaf6652-307b-41ba-9059-72ae010b7928",
+   "execution_count": 7,
+   "id": "de934f1c-f5db-4c89-ba5a-d84a48b18ce3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_871/1096090739.py:7: RuntimeWarning: divide by zero encountered in divide\n",
+      "  pct[1:] = np.diff(arr) / np.abs(arr[:-1])\n",
+      "/tmp/ipykernel_871/1096090739.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  pct[1:] = np.diff(arr) / np.abs(arr[:-1])\n"
+     ]
+    }
+   ],
    "source": [
-    "for t in ok_trips:\n",
-    "    plot_shape_meters(vp, t)"
+    "# Find most jumpy trips\n",
+    "vp2 = (vp.groupby(\"trip_instance_key\")\n",
+    "       .agg({\"shape_meters\": lambda x: list(x)})\n",
+    "       .reset_index()\n",
+    "      )\n",
+    "\n",
+    "vp2 = vp2.assign(\n",
+    "    big_jumps = vp2.apply(\n",
+    "        lambda x: \n",
+    "        number_of_elements_over_threshold(\n",
+    "            x.shape_meters, 20), axis=1\n",
+    "    )\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "284fb053-ffc9-411a-ab46-82f6cfb08af1",
+   "execution_count": 27,
+   "id": "29a91576-ae53-4464-b0fa-40291431aa16",
    "metadata": {},
    "outputs": [],
    "source": [
-    "for t in loopy_trips:\n",
-    "    plot_shape_meters(vp, t)"
+    "all_colors = (cp.CALITP_CATEGORY_BRIGHT_COLORS + cp.CALITP_CATEGORY_BOLD_COLORS + \n",
+    "              cp.CALITP_DIVERGING_COLORS + cp.CALITP_SEQUENTIAL_COLORS\n",
+    "             )\n",
+    "\n",
+    "\n",
+    "final_trip_keys = {\n",
+    "    \"afaf17f1c2816652f0e4522a5c7f206b\": cp.CALITP_CATEGORY_BRIGHT_COLORS[1], # anaheim orange\n",
+    "    \"a880d82a382929aa1de15be733f10a51\": cp.CALITP_CATEGORY_BOLD_COLORS[2], # kings green\n",
+    "    \"d628de22f56dbb4c0e3f8242a2fe78d3\": cp.CALITP_CATEGORY_BRIGHT_COLORS[0], # vine blue\n",
+    "    \"f1a0a79baa78db2c26b3248a30662a7b\": cp.CALITP_CATEGORY_BRIGHT_COLORS[5], # presidgo purple\n",
+    "    \"26146503b5bf0235c0c8fe98dcd8d90b\": cp.CALITP_SEQUENTIAL_COLORS[4], # dumbarton navy\n",
+    "    \"4f76b1c357ee534ac931a5c1bd1cbb87\": cp.CALITP_CATEGORY_BOLD_COLORS[2], # santa maria yellow\n",
+    "    \"e5bcf460be0899f437b1e53b7f3feced\": cp.CALITP_CATEGORY_BOLD_COLORS[4], # tcrta gray blue\n",
+    "    \"18cc0764a463566e8690f0d44c32c199\": cp.CALITP_CATEGORY_BRIGHT_COLORS[3], # sd green\n",
+    "    \"45d8634a92be1fa10ae4f4aa5aa6d5b9\": cp.CALITP_CATEGORY_BRIGHT_COLORS[4], # foothill light blue\n",
+    "    \"aa851696959462180fe04f189dc75584\": cp.CALITP_CATEGORY_BRIGHT_COLORS[3], # big blue bus green\n",
+    "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ced6dbff-008e-4ccf-b284-cc1d79d3e801",
+   "execution_count": 28,
+   "id": "6c7f04b1-9966-4c6c-980d-a63e61c814f1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "speed = pd.read_parquet(\n",
-    "    f\"{SEGMENT_GCS}speeds_comparison_{analysis_date}.parquet\"\n",
+    "shapes = helpers.import_scheduled_shapes(\n",
+    "    analysis_date, \n",
+    "    columns = [\"shape_array_key\", \"shape_id\", \"geometry\"],\n",
+    "    get_pandas = True,\n",
+    "    crs = \"EPSG:4326\"\n",
+    ").merge(\n",
+    "    shapes_loop_inlining[\n",
+    "        shapes_loop_inlining.trip_instance_key.isin(final_trip_keys.keys())],\n",
+    "    on = \"shape_array_key\",\n",
+    "    how = \"inner\"\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "8243b13f-323b-4b0f-8fff-75cd2837d165",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trip = \"10096002510743-JUNE23\"\n",
-    "speed[speed.trip_id==trip]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "394956b9-a02c-4b7d-b604-3775c4e55a51",
+   "execution_count": 29,
+   "id": "2339c9ab-2241-4087-abf8-cee4d1b2a9cc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "metro_trip = helpers.import_scheduled_trips(\n",
-    "    analysis_date,\n",
-    "    columns = [\"trip_instance_key\", \"trip_id\"],\n",
-    "    filters = [[(\"trip_id\", \"==\", trip)]],\n",
-    "    get_pandas = True\n",
+    "gdf = pd.merge(\n",
+    "    shapes,\n",
+    "    vp,\n",
+    "    on = \"trip_instance_key\",\n",
+    "    how = \"inner\"\n",
     ")\n",
     "\n",
-    "trip_key = metro_trip.trip_instance_key.iloc[0]"
+    "# assign colors\n",
+    "gdf = gdf.assign(\n",
+    "    color = gdf.trip_instance_key.map(final_trip_keys),\n",
+    "    gtfs_dataset_name = gdf.gtfs_dataset_name.str.replace(\"VehiclePositions\", \"Vehicle Positions\")\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5c5b8f1d-6571-4072-a9ea-dc1c36173453",
+   "execution_count": 30,
+   "id": "9d8f339f-f832-45f2-9f2c-f96125799a38",
    "metadata": {},
    "outputs": [],
    "source": [
-    "vp_pared = pd.read_parquet(\n",
-    "    f\"{SEGMENT_GCS}vp_pared_stops_{analysis_date}\",\n",
-    "    filters = [[(\"trip_instance_key\", \"==\", trip_key)]])"
+    "def make_chart_map(df: pd.DataFrame, one_trip: str):\n",
+    "    \"\"\"\n",
+    "    Plot how the projected shape meters looks for one trip.\n",
+    "    \n",
+    "    vp_idx is ordered by timestamp, use as x.\n",
+    "    \"\"\"\n",
+    "    subset_df = df[df.trip_instance_key==one_trip].drop(columns = \"geometry\")\n",
+    "    subset_gdf = df[df.trip_instance_key==one_trip][[\n",
+    "        \"shape_id\", \"geometry\"]].drop_duplicates()\n",
+    "    \n",
+    "    GTFS_DATA = subset_df.gtfs_dataset_name.iloc[0]\n",
+    "    TRIP_ID = subset_df.trip_id.iloc[0]\n",
+    "    COLOR = subset_df.color.iloc[0]\n",
+    "    \n",
+    "    grid_bool = False\n",
+    "    grid_opacity = 0\n",
+    "    WIDTH = 400\n",
+    "    HEIGHT = 400\n",
+    "    \n",
+    "    chart = (alt.Chart(subset_df)\n",
+    "             .mark_line(color=COLOR)\n",
+    "             .encode(\n",
+    "                 x=alt.X(\"vp_idx\", \n",
+    "                         title = \"\", \n",
+    "                         axis=alt.Axis(labels=False, \n",
+    "                                       gridOpacity=grid_opacity, \n",
+    "                                       tickOpacity=grid_opacity)\n",
+    "                        ),\n",
+    "                 y=alt.Y(\"shape_meters:Q\", \n",
+    "                         title = \"\",\n",
+    "                         axis=alt.Axis(labels=False, \n",
+    "                                       gridOpacity=grid_opacity, \n",
+    "                                       tickOpacity=grid_opacity)\n",
+    "                        ),\n",
+    "             ).properties(\n",
+    "                 title = {\"text\": [GTFS_DATA], \n",
+    "                         \"subtitle\": f\"trip_id: {TRIP_ID}\"}\n",
+    "             )\n",
+    "            )\n",
+    "       \n",
+    "    chart = (chart\n",
+    "             .configure_axis(grid=grid_bool, labelFontSize=0)\n",
+    "             .configure_axis(gridOpacity=grid_opacity, \n",
+    "                             domainOpacity=grid_opacity)\n",
+    "             .configure_axisBand(grid=grid_bool)\n",
+    "             .configure_view(strokeOpacity=grid_opacity)\n",
+    "             .properties(width = WIDTH*1.2, height = HEIGHT)\n",
+    "            )\n",
+    "    \n",
+    "    display(chart)\n",
+    "    \n",
+    "    m = subset_gdf.explore(\n",
+    "        \"shape_id\", \n",
+    "        tiles = \"CartoDB Positron\",\n",
+    "        legend = False,\n",
+    "        style_kwds = {\n",
+    "            \"color\": COLOR, \n",
+    "        },\n",
+    "        map_kwds = {\n",
+    "            \"dragging\": False,\n",
+    "            \"scrollWheelZoom\": False,\n",
+    "            \"tileOpacity\": 0\n",
+    "        },\n",
+    "        zoom_control = False,\n",
+    "        width = WIDTH, height = HEIGHT\n",
+    "    )\n",
+    "    \n",
+    "    display(m)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "c83f420e-99a2-40d8-b3ef-5d13f68b4226",
+   "execution_count": 31,
+   "id": "284fb053-ffc9-411a-ab46-82f6cfb08af1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-8c8fb76f8cac4704b16c6d233ad034af.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-8c8fb76f8cac4704b16c6d233ad034af.vega-embed details,\n",
+       "  #altair-viz-8c8fb76f8cac4704b16c6d233ad034af.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-8c8fb76f8cac4704b16c6d233ad034af\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-8c8fb76f8cac4704b16c6d233ad034af\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-8c8fb76f8cac4704b16c6d233ad034af\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.14.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.14.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"strokeOpacity\": 0}, \"axis\": {\"domainOpacity\": 0, \"gridOpacity\": 0}, \"axisBand\": {\"grid\": false}}, \"data\": {\"name\": \"data-f454ea8c6021ba4becaf50e03a1f3504\"}, \"mark\": {\"type\": \"line\", \"color\": \"#EB9F3C\"}, \"encoding\": {\"x\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"vp_idx\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"shape_meters\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 400, \"title\": {\"text\": [\"Anaheim Resort Vehicle Positions\"], \"subtitle\": \"trip_id: 540b930a-c9b2-4db2-a67c-a1d24ff86295:1\"}, \"width\": 480.0, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.14.1.json\", \"datasets\": {\"data-f454ea8c6021ba4becaf50e03a1f3504\": [{\"shape_array_key\": \"e84b6bb335eb10070cbc6f84ad548b46\", 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+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7fbe2b373970>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-98f3b63848814beba91e0d94a9c8ef08.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-98f3b63848814beba91e0d94a9c8ef08.vega-embed details,\n",
+       "  #altair-viz-98f3b63848814beba91e0d94a9c8ef08.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-98f3b63848814beba91e0d94a9c8ef08\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-98f3b63848814beba91e0d94a9c8ef08\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-98f3b63848814beba91e0d94a9c8ef08\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.14.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.14.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"strokeOpacity\": 0}, \"axis\": {\"domainOpacity\": 0, \"gridOpacity\": 0}, \"axisBand\": {\"grid\": false}}, \"data\": {\"name\": \"data-2f64098b789e12163e5d592933cfad70\"}, \"mark\": {\"type\": \"line\", \"color\": \"#F6BF16\"}, \"encoding\": {\"x\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"vp_idx\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"shape_meters\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 400, \"title\": {\"text\": [\"Kings Vehicle Positions\"], \"subtitle\": \"trip_id: 263\"}, \"width\": 480.0, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.14.1.json\", \"datasets\": {\"data-2f64098b789e12163e5d592933cfad70\": [{\"shape_array_key\": \"3a7090515c21f619ac2dbece31900492\", \"shape_id\": \"70\", \"loop_or_inlining\": 1, 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+       "    width: 100%;\n",
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+       "  }\n",
+       "\n",
+       "  #altair-viz-ebf0e26ef3e743f099be8d03922ae6ba.vega-embed details,\n",
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+       "<div id=\"altair-viz-ebf0e26ef3e743f099be8d03922ae6ba\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
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+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
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+       "    };\n",
+       "\n",
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+       "          s.async = true;\n",
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+       "          s.src = paths[lib];\n",
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+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
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+       "      maybeLoadScript(\"vega\", \"5\")\n",
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+       "        .then(() => displayChart(vegaEmbed));\n",
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+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7fbe2b36abb0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-17345f97fc4c443ba022c161791bd399.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-17345f97fc4c443ba022c161791bd399.vega-embed details,\n",
+       "  #altair-viz-17345f97fc4c443ba022c161791bd399.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-17345f97fc4c443ba022c161791bd399\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-17345f97fc4c443ba022c161791bd399\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-17345f97fc4c443ba022c161791bd399\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.14.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.14.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"strokeOpacity\": 0}, \"axis\": {\"domainOpacity\": 0, \"gridOpacity\": 0}, \"axisBand\": {\"grid\": false}}, \"data\": {\"name\": \"data-03fde63317ba6ec034284ca7a4185179\"}, \"mark\": {\"type\": \"line\", \"color\": \"#9487C0\"}, \"encoding\": {\"x\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"vp_idx\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"shape_meters\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 400, \"title\": {\"text\": [\"PresidiGo Vehicle Positions\"], \"subtitle\": \"trip_id: PH1400\"}, \"width\": 480.0, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.14.1.json\", \"datasets\": {\"data-03fde63317ba6ec034284ca7a4185179\": [{\"shape_array_key\": \"a58fa2512f7f84bdaef2dce7b699ea5c\", \"shape_id\": \"20010\", 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+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.14.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"strokeOpacity\": 0}, \"axis\": {\"domainOpacity\": 0, \"gridOpacity\": 0}, \"axisBand\": {\"grid\": false}}, \"data\": {\"name\": \"data-6cb04dbc03318c2fc029dc8e5989d359\"}, \"mark\": {\"type\": \"line\", \"color\": \"#0B405B\"}, \"encoding\": {\"x\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"vp_idx\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"shape_meters\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 400, \"title\": {\"text\": [\"Bay Area 511 Dumbarton Express Vehicle Positions\"], \"subtitle\": \"trip_id: 9412221\"}, \"width\": 480.0, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.14.1.json\", \"datasets\": {\"data-6cb04dbc03318c2fc029dc8e5989d359\": [{\"shape_array_key\": \"6c1d815d86e8a29ff2345d43bfadc66c\", \"shape_id\": 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+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7fbe2b32f8e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-382f08c657ce4608bd3825be6e0b0b35.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-382f08c657ce4608bd3825be6e0b0b35.vega-embed details,\n",
+       "  #altair-viz-382f08c657ce4608bd3825be6e0b0b35.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-382f08c657ce4608bd3825be6e0b0b35\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-382f08c657ce4608bd3825be6e0b0b35\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-382f08c657ce4608bd3825be6e0b0b35\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.14.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.14.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"strokeOpacity\": 0}, \"axis\": {\"domainOpacity\": 0, \"gridOpacity\": 0}, \"axisBand\": {\"grid\": false}}, \"data\": {\"name\": \"data-849763184b349ed7de27cadc09baf4e2\"}, \"mark\": {\"type\": \"line\", \"color\": \"#F6BF16\"}, \"encoding\": {\"x\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"vp_idx\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"shape_meters\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 400, \"title\": {\"text\": [\"Santa Maria Vehicle Positions\"], \"subtitle\": \"trip_id: 44b6080d-58a6-4325-b039-6603be104fa6\"}, \"width\": 480.0, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.14.1.json\", \"datasets\": {\"data-849763184b349ed7de27cadc09baf4e2\": [{\"shape_array_key\": \"9dcbfab40207f66d5a2dd0c3e531ef87\", 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+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7fbe2b35d400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-cf1a0b1e01544988be49fd6a7c7dd1cd.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-cf1a0b1e01544988be49fd6a7c7dd1cd.vega-embed details,\n",
+       "  #altair-viz-cf1a0b1e01544988be49fd6a7c7dd1cd.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-cf1a0b1e01544988be49fd6a7c7dd1cd\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-cf1a0b1e01544988be49fd6a7c7dd1cd\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-cf1a0b1e01544988be49fd6a7c7dd1cd\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.14.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.14.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"strokeOpacity\": 0}, \"axis\": {\"domainOpacity\": 0, \"gridOpacity\": 0}, \"axisBand\": {\"grid\": false}}, \"data\": {\"name\": \"data-28ad6546d43137ea3fe6a9a0b5c360c2\"}, \"mark\": {\"type\": \"line\", \"color\": \"#7790A3\"}, \"encoding\": {\"x\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"vp_idx\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"shape_meters\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 400, \"title\": {\"text\": [\"TCRTA Vehicle Positions\"], \"subtitle\": \"trip_id: 46b97d14-b273-44a3-b48b-8452f4a0f702\"}, \"width\": 480.0, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.14.1.json\", \"datasets\": {\"data-28ad6546d43137ea3fe6a9a0b5c360c2\": [{\"shape_array_key\": \"7650f9b63828d5e5cf2c2e3d8236f350\", \"shape_id\": 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+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7fbe2b35df40>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-1469d994b13a4acba0fcccbfe7b63de5.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-1469d994b13a4acba0fcccbfe7b63de5.vega-embed details,\n",
+       "  #altair-viz-1469d994b13a4acba0fcccbfe7b63de5.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-1469d994b13a4acba0fcccbfe7b63de5\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-1469d994b13a4acba0fcccbfe7b63de5\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-1469d994b13a4acba0fcccbfe7b63de5\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.14.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.14.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"strokeOpacity\": 0}, \"axis\": {\"domainOpacity\": 0, \"gridOpacity\": 0}, \"axisBand\": {\"grid\": false}}, \"data\": {\"name\": \"data-ccc23675e414a96abfd9b91aeb215257\"}, \"mark\": {\"type\": \"line\", \"color\": \"#51BF9D\"}, \"encoding\": {\"x\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"vp_idx\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"shape_meters\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 400, \"title\": {\"text\": [\"San Diego Vehicle Positions\"], \"subtitle\": \"trip_id: 17217221\"}, \"width\": 480.0, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.14.1.json\", \"datasets\": {\"data-ccc23675e414a96abfd9b91aeb215257\": [{\"shape_array_key\": \"055bde51f22efec6b667d6e7bbdc59e7\", \"shape_id\": \"S2_992_9_56\", 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+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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+       "<folium.folium.Map at 0x7fbe2b309d00>"
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+       "  #altair-viz-32bb7d3af04c4443bc9fa5023464af53.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-32bb7d3af04c4443bc9fa5023464af53.vega-embed details,\n",
+       "  #altair-viz-32bb7d3af04c4443bc9fa5023464af53.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-32bb7d3af04c4443bc9fa5023464af53\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-32bb7d3af04c4443bc9fa5023464af53\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-32bb7d3af04c4443bc9fa5023464af53\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.14.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
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+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
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+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
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+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
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+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
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+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7fbe2b31b880>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-ea4a9c8b0580475dbbdb320253b2a0c5.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-ea4a9c8b0580475dbbdb320253b2a0c5.vega-embed details,\n",
+       "  #altair-viz-ea4a9c8b0580475dbbdb320253b2a0c5.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-ea4a9c8b0580475dbbdb320253b2a0c5\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-ea4a9c8b0580475dbbdb320253b2a0c5\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-ea4a9c8b0580475dbbdb320253b2a0c5\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.14.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.14.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"strokeOpacity\": 0}, \"axis\": {\"domainOpacity\": 0, \"gridOpacity\": 0}, \"axisBand\": {\"grid\": false}}, \"data\": {\"name\": \"data-14c3cabf38e50a94008d321d93510b9d\"}, \"mark\": {\"type\": \"line\", \"color\": \"#51BF9D\"}, \"encoding\": {\"x\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"vp_idx\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"gridOpacity\": 0, \"labels\": false, \"tickOpacity\": 0}, \"field\": \"shape_meters\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 400, \"title\": {\"text\": [\"Big Blue Bus Vehicle Positions\"], \"subtitle\": \"trip_id: 909253\"}, \"width\": 480.0, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.14.1.json\", \"datasets\": {\"data-14c3cabf38e50a94008d321d93510b9d\": [{\"shape_array_key\": \"c5813d4463cf5f0c5d5a0e3f66565301\", \"shape_id\": \"26509\", 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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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03682%5D%2C%20%5B-118.48907%2C%2034.036419%5D%2C%20%5B-118.48798%2C%2034.03547%5D%2C%20%5B-118.4864%2C%2034.03412%5D%2C%20%5B-118.484809%2C%2034.03277%5D%2C%20%5B-118.483809%2C%2034.031899%5D%2C%20%5B-118.483679%2C%2034.03179%5D%2C%20%5B-118.48353%2C%2034.03166%5D%2C%20%5B-118.48321%2C%2034.03139%5D%2C%20%5B-118.482819%2C%2034.03106%5D%2C%20%5B-118.48277%2C%2034.031019%5D%2C%20%5B-118.482009%2C%2034.030379%5D%2C%20%5B-118.481619%2C%2034.03005%5D%2C%20%5B-118.480579%2C%2034.02916%5D%2C%20%5B-118.48033%2C%2034.028939%5D%2C%20%5B-118.48001%2C%2034.028659%5D%2C%20%5B-118.479589%2C%2034.028299%5D%2C%20%5B-118.47884%2C%2034.027659%5D%2C%20%5B-118.47848%2C%2034.02736%5D%2C%20%5B-118.478069%2C%2034.02701%5D%2C%20%5B-118.477289%2C%2034.026339%5D%2C%20%5B-118.47691%2C%2034.02601%5D%2C%20%5B-118.47722%2C%2034.025759%5D%2C%20%5B-118.47737%2C%2034.02564%5D%2C%20%5B-118.477439%2C%2034.025579%5D%2C%20%5B-118.47786%2C%2034.025249%5D%2C%20%5B-118.47849%2C%2034.02473%5D%2C%20%5B-118.47881%2C%2034.02447%5D%2C%20%5B-118.47974%2C%2034.023709%5D%2C%20%5B-118.47939%2C%2034.02341%5D%2C%20%5B-118.47781%2C%2034.02206%5D%2C%20%5B-118.478779%2C%2034.021279%5D%2C%20%5B-118.479449%2C%2034.020729%5D%2C%20%5B-118.479669%2C%2034.020549%5D%2C%20%5B-118.47975%2C%2034.020479%5D%2C%20%5B-118.479859%2C%2034.020389%5D%2C%20%5B-118.480359%2C%2034.01999%5D%2C%20%5B-118.480659%2C%2034.019749%5D%2C%20%5B-118.48052%2C%2034.019639%5D%2C%20%5B-118.480169%2C%2034.01935%5D%2C%20%5B-118.479989%2C%2034.019199%5D%2C%20%5B-118.47984%2C%2034.01907%5D%2C%20%5B-118.479399%2C%2034.018689%5D%2C%20%5B-118.479099%2C%2034.01844%5D%2C%20%5B-118.477879%2C%2034.01737%5D%2C%20%5B-118.476569%2C%2034.016259%5D%2C%20%5B-118.47632%2C%2034.016079%5D%2C%20%5B-118.476129%2C%2034.01593%5D%2C%20%5B-118.47601%2C%2034.015989%5D%2C%20%5B-118.475549%2C%2034.016229%5D%2C%20%5B-118.47524%2C%2034.016399%5D%2C%20%5B-118.474229%2C%2034.016939%5D%2C%20%5B-118.473749%2C%2034.0172%5D%2C%20%5B-118.473109%2C%2034.01754%5D%2C%20%5B-118.47277%2C%2034.01772%5D%2C%20%5B-118.47182%2C%2034.018239%5D%5D%2C%20%22type%22%3A%20%22LineString%22%7D%2C%20%22id%22%3A%20%221262%22%2C%20%22properties%22%3A%20%7B%22__folium_color%22%3A%20%22%231f77b4%22%2C%20%22shape_id%22%3A%20%2226509%22%7D%2C%20%22type%22%3A%20%22Feature%22%7D%5D%2C%20%22type%22%3A%20%22FeatureCollection%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20geo_json_e9ccfe7bf5244534910a5d346fdd2f0b.bindTooltip%28%0A%20%20%20%20function%28layer%29%7B%0A%20%20%20%20let%20div%20%3D%20L.DomUtil.create%28%27div%27%29%3B%0A%20%20%20%20%0A%20%20%20%20let%20handleObject%20%3D%20feature%3D%3Etypeof%28feature%29%3D%3D%27object%27%20%3F%20JSON.stringify%28feature%29%20%3A%20feature%3B%0A%20%20%20%20let%20fields%20%3D%20%5B%22shape_id%22%5D%3B%0A%20%20%20%20let%20aliases%20%3D%20%5B%22shape_id%22%5D%3B%0A%20%20%20%20let%20table%20%3D%20%27%3Ctable%3E%27%20%2B%0A%20%20%20%20%20%20%20%20String%28%0A%20%20%20%20%20%20%20%20fields.map%28%0A%20%20%20%20%20%20%20%20%28v%2Ci%29%3D%3E%0A%20%20%20%20%20%20%20%20%60%3Ctr%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cth%3E%24%7Baliases%5Bi%5D%7D%3C/th%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Ctd%3E%24%7BhandleObject%28layer.feature.properties%5Bv%5D%29%7D%3C/td%3E%0A%20%20%20%20%20%20%20%20%3C/tr%3E%60%29.join%28%27%27%29%29%0A%20%20%20%20%2B%27%3C/table%3E%27%3B%0A%20%20%20%20div.innerHTML%3Dtable%3B%0A%20%20%20%20%0A%20%20%20%20return%20div%0A%20%20%20%20%7D%0A%20%20%20%20%2C%7B%22className%22%3A%20%22foliumtooltip%22%2C%20%22sticky%22%3A%20true%7D%29%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0A%3C/script%3E onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7fbe2b3717c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# 27:13, 27:56  *  29:14, 29:52, * 30:13\n",
-    "vp_pared[vp_pared.stop_sequence==36]"
+    "for t in final_trip_keys.keys():\n",
+    "    make_chart_map(gdf, t)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "145ac1ce-93a7-4abb-b72b-977541a98163",
+   "id": "9763b29b-381b-4f76-9e88-a117e3d82a5a",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "metro_trip"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0a950ad3-a1bd-4f6f-b7ff-6ee95ffbf8e2",
+   "id": "71b15470-8c6d-4370-adab-96f3b3edb78b",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/rt_segment_speeds/logs/avg_speeds.log b/rt_segment_speeds/logs/avg_speeds.log
index ca492f4c4..7d59ff9f7 100644
--- a/rt_segment_speeds/logs/avg_speeds.log
+++ b/rt_segment_speeds/logs/avg_speeds.log
@@ -1,30 +1,43 @@
-2023-08-14 10:45:18.623 | INFO     | __main__:<module>:143 - Analysis date: 2023-07-12
-2023-08-14 10:52:06.516 | INFO     | __main__:<module>:165 - execution time: 0:06:47.892861
-2023-08-14 10:54:13.170 | INFO     | __main__:<module>:143 - Analysis date: 2023-06-14
-2023-08-14 11:01:01.301 | INFO     | __main__:<module>:165 - execution time: 0:06:48.130654
-2023-08-14 11:12:26.319 | INFO     | __main__:<module>:143 - Analysis date: 2023-05-17
-2023-08-14 11:20:25.045 | INFO     | __main__:<module>:165 - execution time: 0:07:58.724683
-2023-08-14 11:21:03.494 | INFO     | __main__:<module>:143 - Analysis date: 2023-04-12
-2023-08-14 11:28:45.925 | INFO     | __main__:<module>:165 - execution time: 0:07:42.429806
-2023-08-14 11:33:17.821 | INFO     | __main__:<module>:143 - Analysis date: 2023-03-15
-2023-08-14 11:40:28.316 | INFO     | __main__:<module>:165 - execution time: 0:07:10.494542
-2023-08-18 14:48:01.479 | INFO     | __main__:<module>:143 - Analysis date: 2023-08-16
-2023-08-18 14:53:19.222 | INFO     | __main__:<module>:165 - execution time: 0:05:17.742302
-2023-08-24 14:56:29.366 | INFO     | __main__:<module>:143 - Analysis date: 2023-08-15
-2023-08-24 15:02:21.592 | INFO     | __main__:<module>:165 - execution time: 0:05:52.222770
-2023-09-21 14:37:32.197 | INFO     | __main__:<module>:167 - Analysis date: 2023-09-13
-2023-09-21 14:43:35.784 | INFO     | __main__:<module>:189 - execution time: 0:06:03.584427
-2023-09-21 15:49:19.146 | INFO     | __main__:<module>:167 - Analysis date: 2023-06-14
-2023-09-21 15:54:51.743 | INFO     | __main__:<module>:189 - execution time: 0:05:32.595873
-2023-09-21 17:54:03.991 | INFO     | __main__:<module>:167 - Analysis date: 2023-05-17
-2023-09-21 18:00:12.730 | INFO     | __main__:<module>:189 - execution time: 0:06:08.738161
-2023-09-21 18:40:52.924 | INFO     | __main__:<module>:159 - Analysis date: 2023-04-12
-2023-09-21 18:47:14.179 | INFO     | __main__:<module>:181 - execution time: 0:06:21.254666
-2023-09-21 19:28:44.034 | INFO     | __main__:<module>:159 - Analysis date: 2023-03-15
-2023-09-21 19:34:39.639 | INFO     | __main__:<module>:181 - execution time: 0:05:55.604394
-2023-09-21 19:36:10.116 | INFO     | __main__:<module>:159 - Analysis date: 2023-07-12
-2023-09-21 19:41:33.957 | INFO     | __main__:<module>:181 - execution time: 0:05:23.840096
-2023-09-21 19:43:00.511 | INFO     | __main__:<module>:159 - Analysis date: 2023-08-15
-2023-09-21 19:49:14.412 | INFO     | __main__:<module>:181 - execution time: 0:06:13.900161
-2023-10-17 18:34:48.535 | INFO     | __main__:<module>:159 - Analysis date: 2023-10-11
-2023-10-17 18:40:48.183 | INFO     | __main__:<module>:181 - execution time: 0:05:59.631201
+2023-11-06 15:57:16.305 | INFO     | __main__:<module>:249 - Analysis date: 2023-10-11
+2023-11-06 15:59:18.120 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:02:01.813076
+2023-11-06 15:59:26.899 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:08.655715
+2023-11-06 15:59:26.911 | INFO     | __main__:<module>:249 - Analysis date: 2023-09-13
+2023-11-06 16:01:24.513 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:01:57.601884
+2023-11-06 16:01:32.899 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:08.277026
+2023-11-06 16:01:32.910 | INFO     | __main__:<module>:249 - Analysis date: 2023-08-15
+2023-11-06 16:03:34.836 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:02:01.925195
+2023-11-06 16:03:43.726 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:08.779732
+2023-11-06 16:03:43.739 | INFO     | __main__:<module>:249 - Analysis date: 2023-07-12
+2023-11-06 16:05:28.192 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:01:44.451632
+2023-11-06 16:05:37.199 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:08.899107
+2023-11-06 16:05:37.210 | INFO     | __main__:<module>:249 - Analysis date: 2023-06-14
+2023-11-06 16:07:20.946 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:01:43.736212
+2023-11-06 16:07:30.071 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:09.023577
+2023-11-06 16:07:30.083 | INFO     | __main__:<module>:249 - Analysis date: 2023-05-17
+2023-11-06 16:09:26.014 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:01:55.930926
+2023-11-06 16:09:35.119 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:09.005461
+2023-11-06 16:09:35.131 | INFO     | __main__:<module>:249 - Analysis date: 2023-04-12
+2023-11-06 16:14:19.095 | INFO     | __main__:<module>:249 - Analysis date: 2023-10-11
+2023-11-06 16:16:23.123 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:02:04.027868
+2023-11-06 16:16:32.544 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:09.299333
+2023-11-06 16:16:32.557 | INFO     | __main__:<module>:249 - Analysis date: 2023-09-13
+2023-11-06 16:18:31.595 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:01:59.037028
+2023-11-06 16:18:39.862 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:08.143427
+2023-11-06 16:18:39.874 | INFO     | __main__:<module>:249 - Analysis date: 2023-08-15
+2023-11-06 16:20:44.117 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:02:04.241983
+2023-11-06 16:20:52.968 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:08.742420
+2023-11-06 16:20:52.980 | INFO     | __main__:<module>:249 - Analysis date: 2023-07-12
+2023-11-06 16:22:41.130 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:01:48.149524
+2023-11-06 16:22:50.098 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:08.871341
+2023-11-06 16:22:50.111 | INFO     | __main__:<module>:249 - Analysis date: 2023-06-14
+2023-11-06 16:24:38.042 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:01:47.930386
+2023-11-06 16:24:47.008 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:08.854655
+2023-11-06 16:24:47.021 | INFO     | __main__:<module>:249 - Analysis date: 2023-05-17
+2023-11-06 16:26:48.437 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:02:01.414883
+2023-11-06 16:26:57.608 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:09.058372
+2023-11-06 16:26:57.622 | INFO     | __main__:<module>:249 - Analysis date: 2023-04-12
+2023-11-06 16:29:01.767 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:02:04.143738
+2023-11-06 16:29:10.778 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:08.902052
+2023-11-06 16:29:10.791 | INFO     | __main__:<module>:249 - Analysis date: 2023-03-15
+2023-11-06 16:31:05.067 | INFO     | __main__:speeds_with_segment_geom:130 - segment averages execution time: 0:01:54.274974
+2023-11-06 16:31:14.376 | INFO     | __main__:avg_trip_speeds_with_time_of_day:231 - trip summary execution time: 0:00:09.212622
diff --git a/rt_segment_speeds/logs/cut_road_segments.log b/rt_segment_speeds/logs/cut_road_segments.log
index e935a62ed..a0c97e1bf 100644
--- a/rt_segment_speeds/logs/cut_road_segments.log
+++ b/rt_segment_speeds/logs/cut_road_segments.log
@@ -1,34 +1,3 @@
-2023-10-04 14:49:11.624 | INFO     | __main__:<module>:337 - cut primary/secondary roads: 0:05:12.326594
-2023-10-04 14:57:49.674 | INFO     | __main__:<module>:353 - cut local roads base: 0:08:38.050766
-2023-10-04 14:57:49.675 | INFO     | __main__:<module>:356 - execution time: 0:13:50.378197
-2023-10-04 15:01:32.642 | INFO     | __main__:<module>:99 - Analysis date: 2023-09-13
-2023-10-04 15:03:04.641 | INFO     | __main__:<module>:106 - add local linearids for this month: 0:01:31.998829
-2023-10-04 15:03:55.610 | INFO     | __main__:<module>:149 - concatenate road segments: 0:00:50.969056
-2023-10-04 15:03:55.611 | INFO     | __main__:<module>:150 - execution time: 0:02:22.967885
-2023-10-04 15:04:29.207 | INFO     | __main__:<module>:99 - Analysis date: 2023-08-15
-2023-10-04 15:06:03.121 | INFO     | __main__:<module>:106 - add local linearids for this month: 0:01:33.913428
-2023-10-04 15:06:53.739 | INFO     | __main__:<module>:149 - concatenate road segments: 0:00:50.618163
-2023-10-04 15:06:53.741 | INFO     | __main__:<module>:150 - execution time: 0:02:24.531591
-2023-10-04 15:07:31.183 | INFO     | __main__:<module>:99 - Analysis date: 2023-07-12
-2023-10-04 15:08:56.917 | INFO     | __main__:<module>:106 - add local linearids for this month: 0:01:25.733453
-2023-10-04 15:09:47.263 | INFO     | __main__:<module>:149 - concatenate road segments: 0:00:50.346193
-2023-10-04 15:09:47.265 | INFO     | __main__:<module>:150 - execution time: 0:02:16.079646
-2023-10-10 13:17:21.899 | INFO     | __main__:<module>:337 - cut primary/secondary roads: 0:02:55.449737
-2023-10-10 13:21:41.094 | INFO     | __main__:<module>:353 - cut local roads base: 0:04:19.194981
-2023-10-10 13:21:41.094 | INFO     | __main__:<module>:356 - execution time: 0:07:14.645388
-2023-10-10 13:26:23.986 | INFO     | __main__:<module>:99 - Analysis date: 2023-09-13
-2023-10-10 13:27:20.525 | INFO     | __main__:<module>:106 - add local linearids for this month: 0:00:56.537886
-2023-10-10 13:27:49.172 | INFO     | __main__:<module>:149 - concatenate road segments: 0:00:28.647259
-2023-10-10 13:27:49.172 | INFO     | __main__:<module>:150 - execution time: 0:01:25.185145
-2023-10-10 14:49:09.851 | INFO     | __main__:<module>:99 - Analysis date: 2023-08-15
-2023-10-10 14:50:13.055 | INFO     | __main__:<module>:106 - add local linearids for this month: 0:01:03.202748
-2023-10-10 14:50:40.658 | INFO     | __main__:<module>:149 - concatenate road segments: 0:00:27.603282
-2023-10-10 14:50:40.659 | INFO     | __main__:<module>:150 - execution time: 0:01:30.806030
-2023-10-10 15:06:20.252 | INFO     | __main__:<module>:96 - Analysis date: 2023-07-12
-2023-10-10 15:07:19.611 | INFO     | __main__:<module>:103 - add local linearids for this month: 0:00:59.359330
-2023-10-10 15:07:46.994 | INFO     | __main__:<module>:146 - concatenate road segments: 0:00:27.382778
-2023-10-10 15:07:46.995 | INFO     | __main__:<module>:147 - execution time: 0:01:26.742108
-2023-10-12 11:09:05.353 | INFO     | __main__:<module>:96 - Analysis date: 2023-10-11
-2023-10-12 11:10:33.776 | INFO     | __main__:<module>:103 - add local linearids for this month: 0:01:28.401366
-2023-10-12 11:11:07.558 | INFO     | __main__:<module>:146 - concatenate road segments: 0:00:33.781990
-2023-10-12 11:11:07.559 | INFO     | __main__:<module>:147 - execution time: 0:02:02.183356
+2023-11-01 13:54:23.167 | INFO     | __main__:<module>:280 - cut primary/secondary roads: 0:02:48.274895
+2023-11-01 13:58:41.143 | INFO     | __main__:<module>:295 - cut local roads base: 0:04:17.975700
+2023-11-01 13:58:41.143 | INFO     | __main__:<module>:298 - execution time: 0:07:06.251281
diff --git a/rt_segment_speeds/logs/interpolate_stop_arrival.log b/rt_segment_speeds/logs/interpolate_stop_arrival.log
index 6178d8156..c6452bd27 100644
--- a/rt_segment_speeds/logs/interpolate_stop_arrival.log
+++ b/rt_segment_speeds/logs/interpolate_stop_arrival.log
@@ -2,3 +2,31 @@
 2023-10-31 18:11:18.958 | INFO     | __main__:<module>:134 - set up df with nearest / subseq vp info: 0:01:18.690602
 2023-10-31 18:12:06.833 | INFO     | __main__:<module>:139 - interpolate stop arrival: 0:00:47.874819
 2023-10-31 18:12:14.756 | INFO     | __main__:<module>:145 - execution time: 0:02:14.488207
+2023-11-03 13:50:00.660 | INFO     | __main__:<module>:153 - Analysis date: 2023-10-11
+2023-11-03 13:51:28.630 | INFO     | __main__:main:124 - set up df with nearest / subseq vp info: 0:01:27.969585
+2023-11-03 13:52:31.928 | INFO     | __main__:main:129 - interpolate stop arrival: 0:01:03.298093
+2023-11-03 13:52:41.114 | INFO     | __main__:main:135 - execution time: 0:02:40.453737
+2023-11-03 15:59:50.277 | INFO     | __main__:<module>:153 - Analysis date: 2023-08-15
+2023-11-03 16:01:18.175 | INFO     | __main__:main:124 - set up df with nearest / subseq vp info: 0:01:27.896964
+2023-11-03 16:02:21.641 | INFO     | __main__:main:129 - interpolate stop arrival: 0:01:03.466104
+2023-11-03 16:02:31.047 | INFO     | __main__:main:135 - execution time: 0:02:40.768508
+2023-11-03 16:02:31.231 | INFO     | __main__:<module>:153 - Analysis date: 2023-07-12
+2023-11-03 16:03:55.074 | INFO     | __main__:main:124 - set up df with nearest / subseq vp info: 0:01:23.841921
+2023-11-03 16:04:51.717 | INFO     | __main__:main:129 - interpolate stop arrival: 0:00:56.643267
+2023-11-03 16:05:00.003 | INFO     | __main__:main:135 - execution time: 0:02:28.771041
+2023-11-03 16:05:00.125 | INFO     | __main__:<module>:153 - Analysis date: 2023-06-14
+2023-11-03 16:06:28.304 | INFO     | __main__:main:124 - set up df with nearest / subseq vp info: 0:01:28.178540
+2023-11-03 16:07:24.310 | INFO     | __main__:main:129 - interpolate stop arrival: 0:00:56.005986
+2023-11-03 16:07:33.890 | INFO     | __main__:main:135 - execution time: 0:02:33.764494
+2023-11-03 16:07:34.155 | INFO     | __main__:<module>:153 - Analysis date: 2023-05-17
+2023-11-03 16:08:58.194 | INFO     | __main__:main:124 - set up df with nearest / subseq vp info: 0:01:24.038301
+2023-11-03 16:09:56.769 | INFO     | __main__:main:129 - interpolate stop arrival: 0:00:58.574171
+2023-11-03 16:10:05.405 | INFO     | __main__:main:135 - execution time: 0:02:31.248634
+2023-11-03 16:10:05.597 | INFO     | __main__:<module>:153 - Analysis date: 2023-04-12
+2023-11-03 16:11:35.069 | INFO     | __main__:main:124 - set up df with nearest / subseq vp info: 0:01:29.471108
+2023-11-03 16:12:36.102 | INFO     | __main__:main:129 - interpolate stop arrival: 0:01:01.033072
+2023-11-03 16:12:44.655 | INFO     | __main__:main:135 - execution time: 0:02:39.056691
+2023-11-03 16:12:44.788 | INFO     | __main__:<module>:153 - Analysis date: 2023-03-15
+2023-11-03 16:14:10.249 | INFO     | __main__:main:124 - set up df with nearest / subseq vp info: 0:01:25.460793
+2023-11-03 16:15:11.212 | INFO     | __main__:main:129 - interpolate stop arrival: 0:01:00.962506
+2023-11-03 16:15:19.953 | INFO     | __main__:main:135 - execution time: 0:02:35.163939
diff --git a/rt_segment_speeds/logs/nearest_vp.log b/rt_segment_speeds/logs/nearest_vp.log
index d8ce12b9d..7fbeeca39 100644
--- a/rt_segment_speeds/logs/nearest_vp.log
+++ b/rt_segment_speeds/logs/nearest_vp.log
@@ -1,7 +1,32 @@
-2023-10-31 17:45:52.135 | INFO     | __main__:<module>:332 - Analysis date: 2023-09-13
-2023-10-31 17:51:23.974 | INFO     | __main__:find_nearest_vp_to_stop:277 - map partitions to transform vp: 0:05:31.838490
-2023-10-31 17:51:25.093 | INFO     | __main__:find_nearest_vp_to_stop:309 - map partitions to find nearest vp to stop: 0:00:01.118975
 2023-10-31 17:57:10.858 | INFO     | __main__:<module>:337 - Analysis date: 2023-09-13
 2023-10-31 18:03:30.506 | INFO     | __main__:find_nearest_vp_to_stop:282 - map partitions to transform vp: 0:06:19.646465
 2023-10-31 18:03:31.676 | INFO     | __main__:find_nearest_vp_to_stop:314 - map partitions to find nearest vp to stop: 0:00:01.170538
 2023-10-31 18:08:58.296 | INFO     | __main__:<module>:344 - execution time: 0:11:47.436826
+2023-11-03 13:36:23.516 | INFO     | __main__:<module>:337 - Analysis date: 2023-10-11
+2023-11-03 13:43:22.702 | INFO     | __main__:find_nearest_vp_to_stop:281 - map partitions to transform vp: 0:06:59.134110
+2023-11-03 13:43:23.916 | INFO     | __main__:find_nearest_vp_to_stop:313 - map partitions to find nearest vp to stop: 0:00:01.213424
+2023-11-03 13:49:42.958 | INFO     | __main__:<module>:344 - execution time: 0:13:19.390402
+2023-11-03 14:46:33.465 | INFO     | __main__:<module>:339 - Analysis date: 2023-08-15
+2023-11-03 14:52:52.919 | INFO     | __main__:find_nearest_vp_to_stop:281 - map partitions to transform vp: 0:06:19.452868
+2023-11-03 14:52:54.211 | INFO     | __main__:find_nearest_vp_to_stop:313 - map partitions to find nearest vp to stop: 0:00:01.292626
+2023-11-03 14:58:59.779 | INFO     | __main__:find_nearest_vp_to_stop:323 - execution time: 0:12:26.312844
+2023-11-03 14:59:00.212 | INFO     | __main__:<module>:339 - Analysis date: 2023-07-12
+2023-11-03 15:05:48.714 | INFO     | __main__:find_nearest_vp_to_stop:281 - map partitions to transform vp: 0:06:48.501285
+2023-11-03 15:05:49.885 | INFO     | __main__:find_nearest_vp_to_stop:313 - map partitions to find nearest vp to stop: 0:00:01.170919
+2023-11-03 15:11:33.748 | INFO     | __main__:find_nearest_vp_to_stop:323 - execution time: 0:12:33.535236
+2023-11-03 15:11:34.299 | INFO     | __main__:<module>:339 - Analysis date: 2023-06-14
+2023-11-03 15:18:00.501 | INFO     | __main__:find_nearest_vp_to_stop:281 - map partitions to transform vp: 0:06:26.201462
+2023-11-03 15:18:01.751 | INFO     | __main__:find_nearest_vp_to_stop:313 - map partitions to find nearest vp to stop: 0:00:01.250351
+2023-11-03 15:23:48.451 | INFO     | __main__:find_nearest_vp_to_stop:323 - execution time: 0:12:14.151951
+2023-11-03 15:23:48.847 | INFO     | __main__:<module>:339 - Analysis date: 2023-05-17
+2023-11-03 15:30:10.910 | INFO     | __main__:find_nearest_vp_to_stop:281 - map partitions to transform vp: 0:06:22.061854
+2023-11-03 15:30:12.094 | INFO     | __main__:find_nearest_vp_to_stop:313 - map partitions to find nearest vp to stop: 0:00:01.184575
+2023-11-03 15:35:50.028 | INFO     | __main__:find_nearest_vp_to_stop:323 - execution time: 0:12:01.179564
+2023-11-03 15:35:50.589 | INFO     | __main__:<module>:339 - Analysis date: 2023-04-12
+2023-11-03 15:42:09.950 | INFO     | __main__:find_nearest_vp_to_stop:281 - map partitions to transform vp: 0:06:19.358835
+2023-11-03 15:42:11.157 | INFO     | __main__:find_nearest_vp_to_stop:313 - map partitions to find nearest vp to stop: 0:00:01.207248
+2023-11-03 15:47:59.635 | INFO     | __main__:find_nearest_vp_to_stop:323 - execution time: 0:12:09.044200
+2023-11-03 15:48:00.252 | INFO     | __main__:<module>:339 - Analysis date: 2023-03-15
+2023-11-03 15:53:58.273 | INFO     | __main__:find_nearest_vp_to_stop:281 - map partitions to transform vp: 0:05:58.019210
+2023-11-03 15:53:59.500 | INFO     | __main__:find_nearest_vp_to_stop:313 - map partitions to find nearest vp to stop: 0:00:01.226720
+2023-11-03 15:59:30.250 | INFO     | __main__:find_nearest_vp_to_stop:323 - execution time: 0:11:29.995820
diff --git a/rt_segment_speeds/logs/prep_stop_segments.log b/rt_segment_speeds/logs/prep_stop_segments.log
index 2598c728c..04b599d8d 100644
--- a/rt_segment_speeds/logs/prep_stop_segments.log
+++ b/rt_segment_speeds/logs/prep_stop_segments.log
@@ -1,24 +1,24 @@
-2023-10-17 13:14:46.275 | INFO     | __main__:<module>:256 - Analysis date: 2023-03-15
-2023-10-17 13:15:24.496 | INFO     | __main__:<module>:263 - Prep stop segment df: 0:00:38.214426
-2023-10-17 13:15:29.098 | INFO     | __main__:<module>:271 - execution time: 0:00:42.816073
-2023-10-17 13:15:29.099 | INFO     | __main__:<module>:256 - Analysis date: 2023-04-12
-2023-10-17 13:15:57.485 | INFO     | __main__:<module>:263 - Prep stop segment df: 0:00:28.385523
-2023-10-17 13:16:02.499 | INFO     | __main__:<module>:271 - execution time: 0:00:33.400098
-2023-10-17 13:16:02.500 | INFO     | __main__:<module>:256 - Analysis date: 2023-05-17
-2023-10-17 13:16:31.425 | INFO     | __main__:<module>:263 - Prep stop segment df: 0:00:28.904607
-2023-10-17 13:16:35.854 | INFO     | __main__:<module>:271 - execution time: 0:00:33.333007
-2023-10-17 13:16:35.854 | INFO     | __main__:<module>:256 - Analysis date: 2023-06-14
-2023-10-17 13:17:04.723 | INFO     | __main__:<module>:263 - Prep stop segment df: 0:00:28.867982
-2023-10-17 13:17:09.278 | INFO     | __main__:<module>:271 - execution time: 0:00:33.423347
-2023-10-17 13:17:09.279 | INFO     | __main__:<module>:256 - Analysis date: 2023-07-12
-2023-10-17 13:17:44.243 | INFO     | __main__:<module>:263 - Prep stop segment df: 0:00:34.964086
-2023-10-17 13:17:48.637 | INFO     | __main__:<module>:271 - execution time: 0:00:39.358247
-2023-10-17 13:17:48.639 | INFO     | __main__:<module>:256 - Analysis date: 2023-08-15
-2023-10-17 13:18:36.553 | INFO     | __main__:<module>:263 - Prep stop segment df: 0:00:47.913322
-2023-10-17 13:18:41.395 | INFO     | __main__:<module>:271 - execution time: 0:00:52.755389
-2023-10-24 10:03:59.640 | INFO     | __main__:<module>:252 - Analysis date: 2023-09-13
-2023-10-24 10:04:30.868 | INFO     | __main__:<module>:259 - Prep stop segment df: 0:00:31.227214
-2023-10-24 10:04:36.329 | INFO     | __main__:<module>:267 - execution time: 0:00:36.688195
-2023-10-24 10:04:36.330 | INFO     | __main__:<module>:252 - Analysis date: 2023-10-11
-2023-10-24 10:05:12.397 | INFO     | __main__:<module>:259 - Prep stop segment df: 0:00:36.067067
-2023-10-24 10:05:18.498 | INFO     | __main__:<module>:267 - execution time: 0:00:42.167738
+2023-11-01 15:40:53.915 | INFO     | __main__:<module>:252 - Analysis date: 2023-10-11
+2023-11-01 15:41:23.984 | INFO     | __main__:<module>:259 - Prep stop segment df: 0:00:30.068163
+2023-11-01 15:41:28.115 | INFO     | __main__:<module>:267 - execution time: 0:00:34.200081
+2023-11-01 15:41:28.116 | INFO     | __main__:<module>:252 - Analysis date: 2023-09-13
+2023-11-01 15:41:55.989 | INFO     | __main__:<module>:259 - Prep stop segment df: 0:00:27.872887
+2023-11-01 15:42:00.094 | INFO     | __main__:<module>:267 - execution time: 0:00:31.978206
+2023-11-01 15:42:00.095 | INFO     | __main__:<module>:252 - Analysis date: 2023-08-15
+2023-11-01 15:42:28.584 | INFO     | __main__:<module>:259 - Prep stop segment df: 0:00:28.488285
+2023-11-01 15:42:32.818 | INFO     | __main__:<module>:267 - execution time: 0:00:32.722970
+2023-11-01 15:42:32.819 | INFO     | __main__:<module>:252 - Analysis date: 2023-07-12
+2023-11-01 15:43:01.069 | INFO     | __main__:<module>:259 - Prep stop segment df: 0:00:28.250004
+2023-11-01 15:43:04.835 | INFO     | __main__:<module>:267 - execution time: 0:00:32.016018
+2023-11-01 15:43:04.837 | INFO     | __main__:<module>:252 - Analysis date: 2023-06-14
+2023-11-01 15:43:32.841 | INFO     | __main__:<module>:259 - Prep stop segment df: 0:00:28.004113
+2023-11-01 15:43:39.634 | INFO     | __main__:<module>:267 - execution time: 0:00:34.796661
+2023-11-01 15:43:39.634 | INFO     | __main__:<module>:252 - Analysis date: 2023-05-17
+2023-11-01 15:44:07.928 | INFO     | __main__:<module>:259 - Prep stop segment df: 0:00:28.293002
+2023-11-01 15:44:13.436 | INFO     | __main__:<module>:267 - execution time: 0:00:33.800758
+2023-11-01 15:44:13.436 | INFO     | __main__:<module>:252 - Analysis date: 2023-04-12
+2023-11-01 15:44:49.328 | INFO     | __main__:<module>:259 - Prep stop segment df: 0:00:35.870067
+2023-11-01 15:44:53.144 | INFO     | __main__:<module>:267 - execution time: 0:00:39.686215
+2023-11-01 15:44:53.145 | INFO     | __main__:<module>:252 - Analysis date: 2023-03-15
+2023-11-01 15:45:20.814 | INFO     | __main__:<module>:259 - Prep stop segment df: 0:00:27.669192
+2023-11-01 15:45:24.707 | INFO     | __main__:<module>:267 - execution time: 0:00:31.561927
diff --git a/rt_segment_speeds/logs/shapely_project_vp.log b/rt_segment_speeds/logs/shapely_project_vp.log
new file mode 100644
index 000000000..bf632164b
--- /dev/null
+++ b/rt_segment_speeds/logs/shapely_project_vp.log
@@ -0,0 +1,27 @@
+2023-11-03 13:29:37.759 | INFO     | __main__:<module>:59 - Analysis date: 2023-10-11
+2023-11-03 13:30:34.396 | INFO     | __main__:<module>:108 - map partitions: 0:00:56.633350
+2023-11-03 13:36:05.124 | INFO     | __main__:<module>:115 - compute and export: 0:05:30.728361
+2023-11-03 14:06:48.833 | INFO     | __main__:<module>:125 - Analysis date: 2023-08-15
+2023-11-03 14:07:35.513 | INFO     | __main__:project_usable_vp_one_day:98 - map partitions: 0:00:46.678890
+2023-11-03 14:12:57.494 | INFO     | __main__:project_usable_vp_one_day:105 - compute and export: 0:05:21.981347
+2023-11-03 14:12:57.497 | INFO     | __main__:project_usable_vp_one_day:106 - execution time: 0:06:08.660237
+2023-11-03 14:12:57.511 | INFO     | __main__:<module>:125 - Analysis date: 2023-07-12
+2023-11-03 14:13:45.518 | INFO     | __main__:project_usable_vp_one_day:98 - map partitions: 0:00:48.005825
+2023-11-03 14:19:44.076 | INFO     | __main__:project_usable_vp_one_day:105 - compute and export: 0:05:58.557858
+2023-11-03 14:19:44.078 | INFO     | __main__:project_usable_vp_one_day:106 - execution time: 0:06:46.563683
+2023-11-03 14:19:44.088 | INFO     | __main__:<module>:125 - Analysis date: 2023-06-14
+2023-11-03 14:21:06.750 | INFO     | __main__:project_usable_vp_one_day:98 - map partitions: 0:01:22.660793
+2023-11-03 14:25:55.035 | INFO     | __main__:project_usable_vp_one_day:105 - compute and export: 0:04:48.284841
+2023-11-03 14:25:55.036 | INFO     | __main__:project_usable_vp_one_day:106 - execution time: 0:06:10.945634
+2023-11-03 14:25:55.045 | INFO     | __main__:<module>:125 - Analysis date: 2023-05-17
+2023-11-03 14:26:58.867 | INFO     | __main__:project_usable_vp_one_day:98 - map partitions: 0:01:03.820596
+2023-11-03 14:34:19.919 | INFO     | __main__:project_usable_vp_one_day:105 - compute and export: 0:07:21.052399
+2023-11-03 14:34:19.919 | INFO     | __main__:project_usable_vp_one_day:106 - execution time: 0:08:24.872995
+2023-11-03 14:34:19.928 | INFO     | __main__:<module>:125 - Analysis date: 2023-04-12
+2023-11-03 14:35:19.182 | INFO     | __main__:project_usable_vp_one_day:98 - map partitions: 0:00:59.253906
+2023-11-03 14:40:20.944 | INFO     | __main__:project_usable_vp_one_day:105 - compute and export: 0:05:01.761581
+2023-11-03 14:40:20.944 | INFO     | __main__:project_usable_vp_one_day:106 - execution time: 0:06:01.015487
+2023-11-03 14:40:20.953 | INFO     | __main__:<module>:125 - Analysis date: 2023-03-15
+2023-11-03 14:41:13.621 | INFO     | __main__:project_usable_vp_one_day:98 - map partitions: 0:00:52.667374
+2023-11-03 14:46:15.016 | INFO     | __main__:project_usable_vp_one_day:105 - compute and export: 0:05:01.394757
+2023-11-03 14:46:15.017 | INFO     | __main__:project_usable_vp_one_day:106 - execution time: 0:05:54.062131
diff --git a/rt_segment_speeds/logs/sjoin_shapes_roads.log b/rt_segment_speeds/logs/sjoin_shapes_roads.log
new file mode 100644
index 000000000..e69de29bb
diff --git a/rt_segment_speeds/logs/sjoin_vp_segments.log b/rt_segment_speeds/logs/sjoin_vp_segments.log
deleted file mode 100644
index 2ce93e885..000000000
--- a/rt_segment_speeds/logs/sjoin_vp_segments.log
+++ /dev/null
@@ -1,69 +0,0 @@
-2023-08-13 15:32:42.979 | INFO     | __main__:<module>:211 - Analysis date: 2023-07-12
-2023-08-13 15:34:48.942 | INFO     | __main__:sjoin_vp_to_segments:176 - prep vp and persist: 0:02:05.928211
-2023-08-13 15:34:49.037 | INFO     | __main__:sjoin_vp_to_segments:191 - sjoin with map_partitions: 0:00:00.094836
-2023-08-13 15:41:29.266 | INFO     | __main__:sjoin_vp_to_segments:200 - export partitioned results: 0:06:40.228903
-2023-08-13 15:41:33.115 | INFO     | __main__:<module>:223 - execution time: 0:08:50.105129
-2023-08-13 21:21:05.325 | INFO     | __main__:<module>:211 - Analysis date: 2023-06-14
-2023-08-13 21:23:03.599 | INFO     | __main__:sjoin_vp_to_segments:176 - prep vp and persist: 0:01:58.270024
-2023-08-13 21:23:03.616 | INFO     | __main__:sjoin_vp_to_segments:191 - sjoin with map_partitions: 0:00:00.016758
-2023-08-13 21:28:45.980 | INFO     | __main__:sjoin_vp_to_segments:200 - export partitioned results: 0:05:42.364145
-2023-08-13 21:28:49.685 | INFO     | __main__:<module>:223 - execution time: 0:07:44.360210
-2023-08-13 22:04:16.808 | INFO     | __main__:<module>:211 - Analysis date: 2023-05-17
-2023-08-13 22:06:20.536 | INFO     | __main__:sjoin_vp_to_segments:176 - prep vp and persist: 0:02:03.724408
-2023-08-13 22:06:20.559 | INFO     | __main__:sjoin_vp_to_segments:191 - sjoin with map_partitions: 0:00:00.022602
-2023-08-13 22:12:19.877 | INFO     | __main__:sjoin_vp_to_segments:200 - export partitioned results: 0:05:59.317878
-2023-08-13 22:12:23.653 | INFO     | __main__:<module>:223 - execution time: 0:08:06.844146
-2023-08-13 22:40:05.238 | INFO     | __main__:<module>:211 - Analysis date: 2023-04-12
-2023-08-13 22:42:09.037 | INFO     | __main__:sjoin_vp_to_segments:176 - prep vp and persist: 0:02:03.793820
-2023-08-13 22:42:09.061 | INFO     | __main__:sjoin_vp_to_segments:191 - sjoin with map_partitions: 0:00:00.023704
-2023-08-13 22:48:10.880 | INFO     | __main__:sjoin_vp_to_segments:200 - export partitioned results: 0:06:01.819976
-2023-08-13 22:48:14.912 | INFO     | __main__:<module>:223 - execution time: 0:08:09.673477
-2023-08-14 07:54:28.210 | INFO     | __main__:<module>:211 - Analysis date: 2023-03-15
-2023-08-14 07:56:58.975 | INFO     | __main__:sjoin_vp_to_segments:176 - prep vp and persist: 0:02:30.645154
-2023-08-14 07:56:59.002 | INFO     | __main__:sjoin_vp_to_segments:191 - sjoin with map_partitions: 0:00:00.026733
-2023-08-14 08:07:24.889 | INFO     | __main__:sjoin_vp_to_segments:200 - export partitioned results: 0:10:25.887360
-2023-08-14 08:07:29.336 | INFO     | __main__:<module>:223 - execution time: 0:13:01.059573
-2023-08-18 14:12:59.926 | INFO     | __main__:<module>:211 - Analysis date: 2023-08-16
-2023-08-18 14:15:43.911 | INFO     | __main__:sjoin_vp_to_segments:176 - prep vp and persist: 0:02:43.951955
-2023-08-18 14:15:43.969 | INFO     | __main__:sjoin_vp_to_segments:191 - sjoin with map_partitions: 0:00:00.057488
-2023-08-18 14:22:15.411 | INFO     | __main__:sjoin_vp_to_segments:200 - export partitioned results: 0:06:31.441789
-2023-08-18 14:22:19.302 | INFO     | __main__:<module>:223 - execution time: 0:09:19.346578
-2023-08-24 14:23:28.328 | INFO     | __main__:<module>:211 - Analysis date: 2023-08-15
-2023-08-24 14:25:38.717 | INFO     | __main__:sjoin_vp_to_segments:176 - prep vp and persist: 0:02:10.371530
-2023-08-24 14:25:38.767 | INFO     | __main__:sjoin_vp_to_segments:191 - sjoin with map_partitions: 0:00:00.050300
-2023-08-24 14:32:56.013 | INFO     | __main__:sjoin_vp_to_segments:200 - export partitioned results: 0:07:17.246282
-2023-08-24 14:33:01.424 | INFO     | __main__:<module>:223 - execution time: 0:09:33.082155
-2023-09-09 13:26:39.979 | INFO     | __main__:<module>:457 - Analysis date: 2023-07-12
-2023-09-09 13:34:38.631 | INFO     | __main__:<module>:465 - remove erroneous sjoin results: 0:07:58.650611
-2023-09-14 12:54:12.157 | INFO     | __main__:<module>:211 - Analysis date: 2023-09-13
-2023-09-14 12:56:46.821 | INFO     | __main__:sjoin_vp_to_segments:176 - prep vp and persist: 0:02:34.658525
-2023-09-14 12:56:46.912 | INFO     | __main__:sjoin_vp_to_segments:191 - sjoin with map_partitions: 0:00:00.091034
-2023-09-14 13:06:11.390 | INFO     | __main__:sjoin_vp_to_segments:200 - export partitioned results: 0:09:24.477597
-2023-09-14 13:06:15.758 | INFO     | __main__:<module>:223 - execution time: 0:12:03.599629
-2023-09-14 13:06:31.913 | INFO     | __main__:<module>:457 - Analysis date: 2023-09-13
-2023-09-14 13:14:08.656 | INFO     | __main__:<module>:465 - remove erroneous sjoin results: 0:07:36.742381
-2023-09-21 10:58:58.711 | INFO     | __main__:<module>:457 - Analysis date: 2023-09-13
-2023-09-21 11:05:58.377 | INFO     | __main__:<module>:465 - remove erroneous sjoin results: 0:06:59.626650
-2023-09-21 12:11:55.875 | INFO     | __main__:<module>:456 - Analysis date: 2023-08-15
-2023-09-21 12:19:13.216 | INFO     | __main__:<module>:464 - remove erroneous sjoin results: 0:07:17.337816
-2023-09-21 12:58:44.489 | INFO     | __main__:<module>:456 - Analysis date: 2023-07-12
-2023-09-21 13:07:33.610 | INFO     | __main__:<module>:464 - remove erroneous sjoin results: 0:08:49.120980
-2023-09-21 15:22:02.693 | INFO     | __main__:<module>:456 - Analysis date: 2023-06-14
-2023-09-21 15:29:14.363 | INFO     | __main__:<module>:464 - remove erroneous sjoin results: 0:07:11.668827
-2023-09-21 17:26:50.733 | INFO     | __main__:<module>:456 - Analysis date: 2023-05-17
-2023-09-21 17:33:37.420 | INFO     | __main__:<module>:464 - remove erroneous sjoin results: 0:06:46.686094
-2023-09-21 18:03:52.838 | INFO     | __main__:<module>:456 - Analysis date: 2023-04-12
-2023-09-21 18:11:04.091 | INFO     | __main__:<module>:464 - remove erroneous sjoin results: 0:07:11.251722
-2023-09-21 18:55:33.230 | INFO     | __main__:<module>:456 - Analysis date: 2023-03-15
-2023-09-21 19:05:26.800 | INFO     | __main__:<module>:464 - remove erroneous sjoin results: 0:09:53.569193
-2023-10-10 16:26:03.401 | INFO     | __main__:<module>:189 - Analysis date: 2023-09-13
-2023-10-10 16:26:54.759 | INFO     | __main__:sjoin_vp_to_segments:111 - import vp and segments: 0:00:51.353998
-2023-10-10 16:26:55.499 | INFO     | __main__:sjoin_vp_to_segments:125 - sjoin with map_partitions: 0:00:00.739841
-2023-10-10 16:29:18.592 | INFO     | __main__:sjoin_vp_to_segments:136 - export partitioned results: 0:02:23.093345
-2023-10-17 13:33:28.504 | INFO     | __main__:<module>:259 - Analysis date: 2023-10-11
-2023-10-17 13:35:02.438 | INFO     | __main__:sjoin_vp_to_segments:222 - prep vp and persist: 0:01:33.930536
-2023-10-17 13:39:08.674 | INFO     | __main__:sjoin_vp_to_segments:237 - sjoin with map_partitions: 0:04:06.236607
-2023-10-17 13:39:16.788 | INFO     | __main__:sjoin_vp_to_segments:248 - export partitioned results: 0:00:08.113816
-2023-10-17 13:39:16.993 | INFO     | __main__:<module>:271 - execution time: 0:05:48.489259
-2023-10-17 18:14:35.528 | INFO     | __main__:<module>:456 - Analysis date: 2023-10-11
-2023-10-17 18:19:27.790 | INFO     | __main__:<module>:464 - remove erroneous sjoin results: 0:04:52.262114
diff --git a/rt_segment_speeds/logs/speeds_by_segment_trip.log b/rt_segment_speeds/logs/speeds_by_segment_trip.log
index e2527e55d..f5654c876 100644
--- a/rt_segment_speeds/logs/speeds_by_segment_trip.log
+++ b/rt_segment_speeds/logs/speeds_by_segment_trip.log
@@ -1,12 +1,32 @@
-2023-10-17 18:24:17.885 | INFO     | __main__:<module>:369 - Analysis date: 2023-10-11
-2023-10-17 18:24:23.381 | INFO     | __main__:linear_referencing_vp_against_line:58 - set up merged vp with segments: 0:00:00.132646
-2023-10-17 18:24:23.430 | INFO     | __main__:linear_referencing_vp_against_line:76 - linear referencing: 0:00:00.049441
-2023-10-17 18:28:11.254 | INFO     | __main__:linear_referencing_and_speed_by_segment:302 - linear referencing: 0:03:53.340694
-2023-10-17 18:32:08.241 | INFO     | __main__:linear_referencing_and_speed_by_segment:313 - make wide and get initial speeds: 0:03:56.986736
-2023-10-17 18:34:24.377 | INFO     | __main__:linear_referencing_and_speed_by_segment:354 - recalculate speeds and get final: 0:02:16.135938
-2023-10-17 18:34:34.838 | INFO     | __main__:<module>:377 - speeds for stop segments: 0:10:16.927674
-2023-10-17 18:34:34.838 | INFO     | __main__:<module>:378 - execution time: 0:10:16.928330
-2023-10-31 12:29:06.200 | INFO     | __main__:<module>:23 - Analysis date: 2023-09-13
-2023-10-31 12:29:29.129 | INFO     | __main__:<module>:69 - execution time: 0:00:22.926565
 2023-10-31 18:12:34.943 | INFO     | __main__:<module>:23 - Analysis date: 2023-09-13
 2023-10-31 18:12:57.436 | INFO     | __main__:<module>:69 - execution time: 0:00:22.465316
+2023-11-03 13:53:00.175 | INFO     | __main__:<module>:77 - Analysis date: 2023-10-11
+2023-11-03 13:53:24.360 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:24.139367
+2023-11-03 16:15:37.605 | INFO     | __main__:<module>:77 - Analysis date: 2023-08-15
+2023-11-03 16:16:03.643 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:26.037613
+2023-11-03 16:16:03.760 | INFO     | __main__:<module>:77 - Analysis date: 2023-07-12
+2023-11-03 16:16:26.371 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:22.610908
+2023-11-03 16:16:26.436 | INFO     | __main__:<module>:77 - Analysis date: 2023-06-14
+2023-11-03 16:16:46.858 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:20.420953
+2023-11-03 16:16:46.941 | INFO     | __main__:<module>:77 - Analysis date: 2023-05-17
+2023-11-03 16:17:08.225 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:21.282876
+2023-11-03 16:17:08.290 | INFO     | __main__:<module>:77 - Analysis date: 2023-04-12
+2023-11-03 16:17:33.748 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:25.457256
+2023-11-03 16:17:33.827 | INFO     | __main__:<module>:77 - Analysis date: 2023-03-15
+2023-11-03 16:18:00.624 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:26.796081
+2023-11-06 16:11:26.549 | INFO     | __main__:<module>:77 - Analysis date: 2023-10-11
+2023-11-06 16:11:47.642 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:21.073421
+2023-11-06 16:11:47.709 | INFO     | __main__:<module>:77 - Analysis date: 2023-09-13
+2023-11-06 16:12:06.201 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:18.491937
+2023-11-06 16:12:06.258 | INFO     | __main__:<module>:77 - Analysis date: 2023-08-15
+2023-11-06 16:12:26.412 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:20.153804
+2023-11-06 16:12:26.478 | INFO     | __main__:<module>:77 - Analysis date: 2023-07-12
+2023-11-06 16:12:45.627 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:19.146422
+2023-11-06 16:12:45.688 | INFO     | __main__:<module>:77 - Analysis date: 2023-06-14
+2023-11-06 16:13:03.685 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:17.996113
+2023-11-06 16:13:03.740 | INFO     | __main__:<module>:77 - Analysis date: 2023-05-17
+2023-11-06 16:13:22.611 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:18.869696
+2023-11-06 16:13:22.672 | INFO     | __main__:<module>:77 - Analysis date: 2023-04-12
+2023-11-06 16:13:42.942 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:20.269320
+2023-11-06 16:13:43.001 | INFO     | __main__:<module>:77 - Analysis date: 2023-03-15
+2023-11-06 16:14:02.267 | INFO     | __main__:calculate_speed_from_stop_arrivals:59 - execution time: 0:00:19.265320
diff --git a/rt_segment_speeds/logs/valid_vehicle_positions.log b/rt_segment_speeds/logs/valid_vehicle_positions.log
deleted file mode 100644
index 66aa86804..000000000
--- a/rt_segment_speeds/logs/valid_vehicle_positions.log
+++ /dev/null
@@ -1,135 +0,0 @@
-2023-08-13 16:04:05.150 | INFO     | __main__:<module>:349 - Analysis date: 2023-07-12
-2023-08-13 16:12:31.355 | INFO     | __main__:<module>:363 - pare down vp by stop segments special cases 0:08:26.201267
-2023-08-13 16:12:31.356 | INFO     | __main__:<module>:366 - execution time: 0:08:26.205295
-2023-08-13 20:45:54.933 | INFO     | __main__:<module>:160 - Analysis date: 2023-07-12
-2023-08-13 20:48:03.182 | INFO     | __main__:pare_down_vp_by_segment:132 - merge usable vp with sjoin results: 0:02:08.244979
-2023-08-13 20:48:03.285 | INFO     | __main__:pare_down_vp_by_segment:141 - keep enter/exit points: 0:00:00.103224
-2023-08-13 20:50:54.420 | INFO     | __main__:pare_down_vp_by_segment:149 - exported: 0:02:51.134568
-2023-08-13 20:50:54.422 | INFO     | __main__:<module>:174 - pare down vp by stop segments normal cases 0:04:59.485200
-2023-08-13 20:50:54.423 | INFO     | __main__:<module>:177 - execution time: 0:04:59.488991
-2023-08-13 20:51:37.688 | INFO     | __main__:<module>:351 - Analysis date: 2023-07-12
-2023-08-13 21:00:17.993 | INFO     | __main__:<module>:365 - pare down vp by stop segments special cases 0:08:40.301265
-2023-08-13 21:00:17.994 | INFO     | __main__:<module>:368 - execution time: 0:08:40.305588
-2023-08-13 21:29:04.380 | INFO     | __main__:<module>:160 - Analysis date: 2023-06-14
-2023-08-13 21:31:30.681 | INFO     | __main__:pare_down_vp_by_segment:132 - merge usable vp with sjoin results: 0:02:26.297493
-2023-08-13 21:31:30.753 | INFO     | __main__:pare_down_vp_by_segment:141 - keep enter/exit points: 0:00:00.072037
-2023-08-13 21:34:24.288 | INFO     | __main__:pare_down_vp_by_segment:149 - exported: 0:02:53.534893
-2023-08-13 21:34:24.290 | INFO     | __main__:<module>:174 - pare down vp by stop segments normal cases 0:05:19.906925
-2023-08-13 21:34:24.291 | INFO     | __main__:<module>:177 - execution time: 0:05:19.910619
-2023-08-13 21:34:38.073 | INFO     | __main__:<module>:351 - Analysis date: 2023-06-14
-2023-08-13 21:43:18.603 | INFO     | __main__:<module>:365 - pare down vp by stop segments special cases 0:08:40.525052
-2023-08-13 21:43:18.604 | INFO     | __main__:<module>:368 - execution time: 0:08:40.529666
-2023-08-13 22:12:36.662 | INFO     | __main__:<module>:160 - Analysis date: 2023-05-17
-2023-08-13 22:15:12.892 | INFO     | __main__:pare_down_vp_by_segment:132 - merge usable vp with sjoin results: 0:02:36.226611
-2023-08-13 22:15:12.971 | INFO     | __main__:pare_down_vp_by_segment:141 - keep enter/exit points: 0:00:00.078677
-2023-08-13 22:18:03.325 | INFO     | __main__:pare_down_vp_by_segment:149 - exported: 0:02:50.353998
-2023-08-13 22:18:03.327 | INFO     | __main__:<module>:174 - pare down vp by stop segments normal cases 0:05:26.661411
-2023-08-13 22:18:03.327 | INFO     | __main__:<module>:177 - execution time: 0:05:26.664871
-2023-08-13 22:18:18.278 | INFO     | __main__:<module>:351 - Analysis date: 2023-05-17
-2023-08-13 22:26:56.589 | INFO     | __main__:<module>:365 - pare down vp by stop segments special cases 0:08:38.303835
-2023-08-13 22:26:56.590 | INFO     | __main__:<module>:368 - execution time: 0:08:38.310902
-2023-08-13 22:48:28.482 | INFO     | __main__:<module>:160 - Analysis date: 2023-04-12
-2023-08-13 22:51:34.171 | INFO     | __main__:pare_down_vp_by_segment:132 - merge usable vp with sjoin results: 0:03:05.685506
-2023-08-13 22:51:34.253 | INFO     | __main__:pare_down_vp_by_segment:141 - keep enter/exit points: 0:00:00.081727
-2023-08-13 22:54:36.123 | INFO     | __main__:pare_down_vp_by_segment:149 - exported: 0:03:01.870409
-2023-08-13 22:54:36.125 | INFO     | __main__:<module>:174 - pare down vp by stop segments normal cases 0:06:07.639993
-2023-08-13 22:54:36.126 | INFO     | __main__:<module>:177 - execution time: 0:06:07.643654
-2023-08-13 22:54:49.842 | INFO     | __main__:<module>:351 - Analysis date: 2023-04-12
-2023-08-13 23:03:53.321 | INFO     | __main__:<module>:365 - pare down vp by stop segments special cases 0:09:03.475660
-2023-08-13 23:03:53.322 | INFO     | __main__:<module>:368 - execution time: 0:09:03.479617
-2023-08-14 08:07:45.615 | INFO     | __main__:<module>:160 - Analysis date: 2023-03-15
-2023-08-14 08:10:42.732 | INFO     | __main__:pare_down_vp_by_segment:132 - merge usable vp with sjoin results: 0:02:57.076804
-2023-08-14 08:10:42.830 | INFO     | __main__:pare_down_vp_by_segment:141 - keep enter/exit points: 0:00:00.097241
-2023-08-14 08:14:47.031 | INFO     | __main__:pare_down_vp_by_segment:149 - exported: 0:04:04.201704
-2023-08-14 08:14:47.056 | INFO     | __main__:<module>:174 - pare down vp by stop segments normal cases 0:07:01.400380
-2023-08-14 08:14:47.058 | INFO     | __main__:<module>:177 - execution time: 0:07:01.408741
-2023-08-14 08:15:05.189 | INFO     | __main__:<module>:351 - Analysis date: 2023-03-15
-2023-08-14 08:25:56.940 | INFO     | __main__:<module>:365 - pare down vp by stop segments special cases 0:10:51.746002
-2023-08-14 08:25:56.966 | INFO     | __main__:<module>:368 - execution time: 0:10:51.776126
-2023-08-18 14:22:39.116 | INFO     | __main__:<module>:160 - Analysis date: 2023-08-16
-2023-08-18 14:25:47.568 | INFO     | __main__:pare_down_vp_by_segment:132 - merge usable vp with sjoin results: 0:03:08.432750
-2023-08-18 14:25:47.656 | INFO     | __main__:pare_down_vp_by_segment:141 - keep enter/exit points: 0:00:00.088305
-2023-08-18 14:29:22.685 | INFO     | __main__:pare_down_vp_by_segment:149 - exported: 0:03:35.028937
-2023-08-18 14:29:22.687 | INFO     | __main__:<module>:174 - pare down vp by stop segments normal cases 0:06:43.551547
-2023-08-18 14:29:22.687 | INFO     | __main__:<module>:177 - execution time: 0:06:43.557315
-2023-08-18 14:29:38.732 | INFO     | __main__:<module>:351 - Analysis date: 2023-08-16
-2023-08-18 14:40:02.981 | INFO     | __main__:<module>:365 - pare down vp by stop segments special cases 0:10:24.243990
-2023-08-18 14:40:02.981 | INFO     | __main__:<module>:368 - execution time: 0:10:24.248704
-2023-08-24 14:33:21.591 | INFO     | __main__:<module>:160 - Analysis date: 2023-08-15
-2023-08-24 14:35:42.158 | INFO     | __main__:pare_down_vp_by_segment:132 - merge usable vp with sjoin results: 0:02:20.558585
-2023-08-24 14:35:42.253 | INFO     | __main__:pare_down_vp_by_segment:141 - keep enter/exit points: 0:00:00.094556
-2023-08-24 14:39:07.353 | INFO     | __main__:pare_down_vp_by_segment:149 - exported: 0:03:25.100624
-2023-08-24 14:39:07.355 | INFO     | __main__:<module>:174 - pare down vp by stop segments normal cases 0:05:45.755594
-2023-08-24 14:39:07.356 | INFO     | __main__:<module>:177 - execution time: 0:05:45.761935
-2023-08-24 14:39:22.403 | INFO     | __main__:<module>:351 - Analysis date: 2023-08-15
-2023-08-24 14:48:33.172 | INFO     | __main__:<module>:365 - pare down vp by stop segments special cases 0:09:10.762642
-2023-08-24 14:48:33.173 | INFO     | __main__:<module>:368 - execution time: 0:09:10.768661
-2023-09-12 10:02:48.637 | INFO     | __main__:<module>:131 - Analysis date: 2023-07-12
-2023-09-12 10:02:50.085 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.398546
-2023-09-12 10:02:50.172 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.086938
-2023-09-12 10:05:01.643 | INFO     | __main__:<module>:131 - Analysis date: 2023-07-12
-2023-09-12 10:05:02.945 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.297436
-2023-09-12 10:05:03.028 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.083493
-2023-09-12 10:10:26.430 | INFO     | __main__:pare_down_vp_by_segment:120 - exported: 0:05:23.402394
-2023-09-12 10:10:26.450 | INFO     | __main__:<module>:145 - pare down vp by stop segments normal cases 0:05:24.802713
-2023-09-12 10:10:26.451 | INFO     | __main__:<module>:148 - execution time: 0:05:24.807249
-2023-09-12 10:34:00.671 | INFO     | __main__:<module>:134 - Analysis date: 2023-07-12
-2023-09-12 10:34:01.998 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.307426
-2023-09-12 10:34:02.107 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.108987
-2023-09-12 10:39:50.666 | INFO     | __main__:pare_down_vp_by_segment:123 - exported: 0:05:48.558950
-2023-09-12 10:39:50.667 | INFO     | __main__:<module>:148 - pare down vp by stop segments for all cases 0:05:49.976836
-2023-09-12 10:39:50.668 | INFO     | __main__:<module>:151 - execution time: 0:05:49.981306
-2023-09-14 13:14:30.723 | INFO     | __main__:<module>:134 - Analysis date: 2023-09-13
-2023-09-14 13:14:31.992 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.264072
-2023-09-14 13:14:32.090 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.097104
-2023-09-14 13:20:47.965 | INFO     | __main__:pare_down_vp_by_segment:123 - exported: 0:06:15.874961
-2023-09-14 13:20:47.966 | INFO     | __main__:<module>:148 - pare down vp by stop segments for all cases 0:06:17.237883
-2023-09-14 13:20:47.967 | INFO     | __main__:<module>:151 - execution time: 0:06:17.243094
-2023-09-21 11:06:27.840 | INFO     | __main__:<module>:134 - Analysis date: 2023-09-13
-2023-09-21 11:06:29.244 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.386494
-2023-09-21 11:06:29.323 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.079269
-2023-09-21 11:13:01.939 | INFO     | __main__:pare_down_vp_by_segment:123 - exported: 0:06:32.615887
-2023-09-21 11:13:01.940 | INFO     | __main__:<module>:148 - pare down vp by stop segments for all cases 0:06:34.083216
-2023-09-21 11:13:01.941 | INFO     | __main__:<module>:151 - execution time: 0:06:34.088864
-2023-09-21 12:19:30.995 | INFO     | __main__:<module>:134 - Analysis date: 2023-08-15
-2023-09-21 12:19:32.432 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.410420
-2023-09-21 12:19:32.535 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.103218
-2023-09-21 12:27:56.376 | INFO     | __main__:pare_down_vp_by_segment:123 - exported: 0:08:23.840718
-2023-09-21 12:27:56.378 | INFO     | __main__:<module>:148 - pare down vp by stop segments for all cases 0:08:25.356390
-2023-09-21 12:27:56.379 | INFO     | __main__:<module>:151 - execution time: 0:08:25.360301
-2023-09-21 13:07:51.526 | INFO     | __main__:<module>:134 - Analysis date: 2023-07-12
-2023-09-21 13:07:52.848 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.297768
-2023-09-21 13:07:52.939 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.091154
-2023-09-21 13:16:20.491 | INFO     | __main__:pare_down_vp_by_segment:123 - exported: 0:08:27.552189
-2023-09-21 13:16:20.493 | INFO     | __main__:<module>:148 - pare down vp by stop segments for all cases 0:08:28.942646
-2023-09-21 13:16:20.493 | INFO     | __main__:<module>:151 - execution time: 0:08:28.946383
-2023-09-21 15:29:36.092 | INFO     | __main__:<module>:134 - Analysis date: 2023-06-14
-2023-09-21 15:29:37.363 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.266538
-2023-09-21 15:29:37.460 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.096562
-2023-09-21 15:36:38.508 | INFO     | __main__:pare_down_vp_by_segment:123 - exported: 0:07:01.048429
-2023-09-21 15:36:38.510 | INFO     | __main__:<module>:148 - pare down vp by stop segments for all cases 0:07:02.413058
-2023-09-21 15:36:38.510 | INFO     | __main__:<module>:151 - execution time: 0:07:02.417441
-2023-09-21 17:33:53.360 | INFO     | __main__:<module>:134 - Analysis date: 2023-05-17
-2023-09-21 17:33:54.621 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.258114
-2023-09-21 17:33:54.702 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.080633
-2023-09-21 17:40:45.339 | INFO     | __main__:pare_down_vp_by_segment:123 - exported: 0:06:50.637139
-2023-09-21 17:40:45.341 | INFO     | __main__:<module>:148 - pare down vp by stop segments for all cases 0:06:51.977448
-2023-09-21 17:40:45.341 | INFO     | __main__:<module>:151 - execution time: 0:06:51.981031
-2023-09-21 18:11:20.644 | INFO     | __main__:<module>:134 - Analysis date: 2023-04-12
-2023-09-21 18:11:21.907 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.253286
-2023-09-21 18:11:21.982 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.074969
-2023-09-21 18:19:17.733 | INFO     | __main__:pare_down_vp_by_segment:123 - exported: 0:07:55.751630
-2023-09-21 18:19:17.735 | INFO     | __main__:<module>:148 - pare down vp by stop segments for all cases 0:07:57.081328
-2023-09-21 18:19:17.735 | INFO     | __main__:<module>:151 - execution time: 0:07:57.086467
-2023-09-21 19:05:57.111 | INFO     | __main__:<module>:134 - Analysis date: 2023-03-15
-2023-09-21 19:05:58.586 | INFO     | __main__:pare_down_vp_by_segment:103 - merge usable vp with sjoin results: 0:00:01.446362
-2023-09-21 19:05:58.673 | INFO     | __main__:pare_down_vp_by_segment:112 - keep enter/exit points: 0:00:00.087041
-2023-09-21 19:14:00.946 | INFO     | __main__:pare_down_vp_by_segment:123 - exported: 0:08:02.272685
-2023-09-21 19:14:00.947 | INFO     | __main__:<module>:148 - pare down vp by stop segments for all cases 0:08:03.807576
-2023-09-21 19:14:00.947 | INFO     | __main__:<module>:151 - execution time: 0:08:03.812106
-2023-10-17 18:19:45.986 | INFO     | __main__:<module>:131 - Analysis date: 2023-10-11
-2023-10-17 18:19:47.197 | INFO     | __main__:pare_down_vp_by_segment:100 - merge usable vp with sjoin results: 0:00:01.184533
-2023-10-17 18:19:47.264 | INFO     | __main__:pare_down_vp_by_segment:109 - keep enter/exit points: 0:00:00.067347
-2023-10-17 18:24:04.316 | INFO     | __main__:pare_down_vp_by_segment:120 - exported: 0:04:17.051551
-2023-10-17 18:24:04.318 | INFO     | __main__:<module>:145 - pare down vp by stop segments for all cases 0:04:18.305061
-2023-10-17 18:24:04.318 | INFO     | __main__:<module>:148 - execution time: 0:04:18.308819
diff --git a/rt_segment_speeds/scripts/A1_sjoin_vp_segments.py b/rt_segment_speeds/scripts/A1_sjoin_vp_segments.py
deleted file mode 100644
index f5b0653cc..000000000
--- a/rt_segment_speeds/scripts/A1_sjoin_vp_segments.py
+++ /dev/null
@@ -1,262 +0,0 @@
-"""
-Spatial join vehicle positions to segments.
-
-Ensure that RT trips can only join to the scheduled shape
-for that scheduled trip. 
-Otherwise, vp on the same road get joined to multiple segments
-across shapes.
-"""
-import dask.dataframe as dd
-import dask_geopandas as dg
-import datetime
-import geopandas as gpd
-import pandas as pd
-import sys
-
-from loguru import logger
-
-from calitp_data_analysis.geography_utils import WGS84
-from segment_speed_utils import helpers, wrangle_shapes
-from segment_speed_utils.project_vars import (analysis_date, SEGMENT_GCS, 
-                                              CONFIG_PATH, PROJECT_CRS)
-
-def add_grouping_col_to_vp(
-    vp_file_name: str,
-    analysis_date: str,
-    trip_grouping_cols: list
-) -> pd.DataFrame:
-    """
-    Import unique trips present in vehicle positions.
-    Use trip_instance_key to merge RT with schedule.
-    
-    Determine trip_grouping_cols, a list of columns to aggregate trip tables
-    up to how segments are cut. 
-    Can be ["route_id", "direction_id"] or ["shape_array_key"]
-    
-    """
-    vp_trips = pd.read_parquet(
-        f"{SEGMENT_GCS}{vp_file_name}_{analysis_date}",
-        columns = ["trip_instance_key"]
-    ).drop_duplicates().dropna().reset_index(drop=True)
-   
-    trips = helpers.import_scheduled_trips(
-        analysis_date,
-        columns = ["trip_instance_key"] + trip_grouping_cols,
-        get_pandas = True
-    )
-    
-    vp_with_crosswalk = dd.merge(
-        vp_trips,
-        trips,
-        on = "trip_instance_key",
-        how = "inner"
-    )
-    
-    return vp_with_crosswalk
-    
-    
-def import_segments_and_buffer(
-    segment_file_name: str,
-    buffer_size: int,
-    segment_identifier_cols: list,
-    **kwargs
-) -> gpd.GeoDataFrame:
-    """
-    Import segments , subset certain columns, 
-    and buffer by some specified amount.
-    """
-    if "stop_segments" in segment_file_name:
-        filename = f"{SEGMENT_GCS}{segment_file_name}.parquet"
-    
-    elif "road_segments" in segment_file_name:
-        filename = f"{SEGMENT_GCS}{segment_file_name}"
-    
-    segments = gpd.read_parquet(
-        filename,
-        columns = segment_identifier_cols + [
-            "seg_idx", "stop_primary_direction", "geometry"],
-        **kwargs
-    ).to_crs(PROJECT_CRS)
-
-    # Buffer the segment for vehicle positions (points) to fall in polygons
-    segments = segments.assign(
-        geometry = segments.geometry.buffer(buffer_size)
-    )
-    
-    return segments
-
-
-def get_sjoin_results(
-    vp: dd.DataFrame, 
-    segments: gpd.GeoDataFrame, 
-    grouping_col: str,
-    segment_identifier_cols: list,
-) -> pd.DataFrame:
-    """
-    Merge all the segments for a shape for that trip,
-    and check if vp is within.
-    Export just vp_idx and seg_idx as our "crosswalk" of sjoin results.
-    If we use dask map_partitions, this is still faster than dask.delayed.
-    """
-    vp_gddf = gpd.GeoDataFrame(
-        vp,
-        geometry = gpd.points_from_xy(vp.x, vp.y, crs=WGS84)
-    ).to_crs(PROJECT_CRS).drop(columns = ["x", "y"])
-    
-    vp_to_seg = gpd.sjoin(
-        vp_gddf,
-        segments,
-        how = "inner",
-        predicate = "within"
-    ).query(
-        f'{grouping_col}_left == {grouping_col}_right'
-    ).drop(
-        columns = f"{grouping_col}_right"
-    ).rename(columns = {f"{grouping_col}_left": grouping_col})
-
-    results = (vp_to_seg[["vp_idx"] + segment_identifier_cols]
-               .drop_duplicates()
-               .reset_index(drop=True)
-              )
-    
-    return results
-
-
-def stage_direction_results(
-    vp: dd.DataFrame, 
-    segments: gpd.GeoDataFrame, 
-    grouping_col: str,
-    segment_identifier_cols: list,
-    direction: str
-):
-    opposite = wrangle_shapes.OPPOSITE_DIRECTIONS[direction]
-    keep_vp = [d for d in wrangle_shapes.ALL_DIRECTIONS if d != opposite] + ["Unknown"]
-    
-    # Keep all directions of vp except the ones running in opposite direction
-    # Esp since buses make turns, a northbound segment can be 
-    # partially westbound and then northbound
-    vp_subset = vp[vp.vp_primary_direction.isin(keep_vp)].repartition(npartitions=20)
-    
-    segments_subset = segments[
-        segments.stop_primary_direction==direction
-    ].reset_index(drop=True)
-    
-    seg_id_dtypes = segments[segment_identifier_cols].dtypes.to_dict()
-    
-    results_subset = vp_subset.map_partitions(
-        get_sjoin_results,
-        segments_subset,
-        grouping_col,
-        segment_identifier_cols,
-        meta = {"vp_idx": "int64", 
-               **seg_id_dtypes},
-        align_dataframes = False
-    )
-    
-    return results_subset
-    
-def sjoin_vp_to_segments(
-    analysis_date: str,
-    dict_inputs: dict = {}
-):
-    """
-    Spatial join vehicle positions to segments.
-    Subset by grouping columns.
-    
-    Vehicle positions can only join to the relevant segments.
-    Use route_dir_identifier or shape_array_key to figure out 
-    the relevant segments those vp can be joined to.
-    """
-    INPUT_FILE = dict_inputs["stage1"]
-    SEGMENT_FILE = dict_inputs["segments_file"]
-    TRIP_GROUPING_COLS = dict_inputs["trip_grouping_cols"]
-    GROUPING_COL = dict_inputs["grouping_col"]
-    SEGMENT_IDENTIFIER_COLS = dict_inputs["segment_identifier_cols"]
-    EXPORT_FILE = dict_inputs["stage2"]
-    
-    BUFFER_METERS = 35
-    
-    time0 = datetime.datetime.now()
-    
-    # Get a list of trips we need to keep from vp
-    vp_trips = add_grouping_col_to_vp(
-        f"{INPUT_FILE}",
-        analysis_date,
-        TRIP_GROUPING_COLS
-    )
-    
-    groups_present = vp_trips[GROUPING_COL].unique().tolist()
-
-    # only import segments whose shape_array_key is associated with a vp_trip
-    segments = import_segments_and_buffer(
-        f"{SEGMENT_FILE}_{analysis_date}",
-        BUFFER_METERS,
-        SEGMENT_IDENTIFIER_COLS,
-        filters = [[(GROUPING_COL, "in", groups_present)]]
-    )
-    
-    # Import vp, keep trips that are usable
-    vp = dd.read_parquet(
-        f"{SEGMENT_GCS}{INPUT_FILE}_{analysis_date}/",
-        columns = [
-            "trip_instance_key", "vp_idx", "x", "y", 
-            "vp_primary_direction"]
-    ).merge(
-        vp_trips,
-        on = "trip_instance_key",
-        how = "inner"
-    )
-    
-    vp = vp.repartition(npartitions=100).persist()
-    
-    time1 = datetime.datetime.now()
-    logger.info(f"prep vp and persist: {time1 - time0}")
-    
-    
-    results = [
-        stage_direction_results(
-            vp,
-            segments,
-            GROUPING_COL,
-            SEGMENT_IDENTIFIER_COLS, 
-            one_direction
-        ).persist() for one_direction in wrangle_shapes.ALL_DIRECTIONS
-    ]
-    
-    
-    time2 = datetime.datetime.now()
-    logger.info(f"sjoin with map_partitions: {time2 - time1}")
-    
-    full_results = dd.multi.concat(results, axis=0).reset_index(drop=True)
-    full_results = full_results.repartition(npartitions=4)
-
-    full_results.to_parquet(
-        f"{SEGMENT_GCS}vp_sjoin/{EXPORT_FILE}_{analysis_date}",
-        overwrite=True
-    )
-    
-    time3 = datetime.datetime.now()
-    logger.info(f"export partitioned results: {time3 - time2}")
-    
-        
-if __name__ == "__main__":
-    
-    LOG_FILE = "../logs/sjoin_vp_segments.log"
-    logger.add(LOG_FILE, retention="3 months")
-    logger.add(sys.stderr, 
-               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
-               level="INFO")
-    
-    logger.info(f"Analysis date: {analysis_date}")
-    
-    start = datetime.datetime.now()
-    
-    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
-    
-    sjoin_vp_to_segments(
-        analysis_date = analysis_date,
-        dict_inputs = STOP_SEG_DICT
-    )
-    
-    end = datetime.datetime.now()
-    logger.info(f"execution time: {end-start}")
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/A2_sjoin_postprocessing.py b/rt_segment_speeds/scripts/A2_sjoin_postprocessing.py
deleted file mode 100644
index 4bef3b131..000000000
--- a/rt_segment_speeds/scripts/A2_sjoin_postprocessing.py
+++ /dev/null
@@ -1,465 +0,0 @@
-"""
-Spatial join post-processing.
-Inlining causes erroneous sjoin results, and we may 
-keep way too many sjoin pairs.
-
-Instead of only focusing only loop_or_inlining shapes for 
-direction check, do it for all shapes.
-If there are 2 groupings of vp (non-consecutive) attached to 
-a segment, check that the vp run the same direction as the segment.
-Otherwise, drop.
-"""
-import dask.dataframe as dd
-import datetime
-import geopandas as gpd
-import numpy as np
-import pandas as pd
-import shapely
-import sys
-
-from loguru import logger
-
-from calitp_data_analysis.geography_utils import WGS84
-from segment_speed_utils import helpers, segment_calcs, wrangle_shapes
-from segment_speed_utils.project_vars import (SEGMENT_GCS, analysis_date, 
-                                              PROJECT_CRS, CONFIG_PATH)
-from A3_valid_vehicle_positions import merge_usable_vp_with_sjoin_vpidx
-
-
-def find_convoluted_segments(
-    ddf: dd.DataFrame, 
-    segment_identifier_cols: list 
-) -> pd.DataFrame:
-    """
-    Identify which segments (shape-stop_seq) are prone to 
-    having double sjoin results due to inlining.
-
-    These are particularly complex to resolve, because 
-    even though we have vp_idx as a column,
-    we cannot simply check whether it is a difference of 1
-    compared to previous or subsequent vp. 
-    
-    Ex: if we're going westbound first, then eastbound, then
-    both 2 distinct groupings of sorted vp_idx will appear on both segments.
-    The westbound segment should be associated with smaller vp_idx.
-    The eastbound segment should be associated with larger vp_idx.
-        
-    So, we need to use direction to resolve this.
-    """
-    segment_trip_cols = ["trip_instance_key"] + segment_identifier_cols
-    
-    ddf["prior_vp_idx"] = (ddf.groupby(segment_trip_cols, 
-                                   observed=True, group_keys=False)
-                       ["vp_idx"]
-                       .shift(1, meta = ("prior_vp_idx", "Int64")) 
-                      )
-
-    ddf["subseq_vp_idx"] = (ddf.groupby(segment_trip_cols, 
-                                   observed=True, group_keys=False)
-                       ["vp_idx"]
-                       .shift(-1, meta = ("subseq_vp_idx", "Int64")) 
-                      )
-
-    ddf = ddf.assign(
-        change_from_prior = ddf.vp_idx - ddf.prior_vp_idx, 
-        change_to_subseq = ddf.subseq_vp_idx - ddf.vp_idx
-    )
-
-    # The segments that are convoluted would have either
-    # a max(change_from_prior) > 1 or max(change_to_subseq) > 1
-    convoluted_segments = ddf.assign(
-        max_change_from_prior = (
-            ddf.groupby(segment_trip_cols, 
-                        observed=True, group_keys=False)
-            .change_from_prior
-            .transform("max", 
-                       meta = ("max_change_from_prior", "Int64"))
-        ),
-        max_change_to_subseq = (
-            ddf.groupby(segment_trip_cols,
-                        observed=True, group_keys=False)
-            .change_to_subseq
-            .transform("max", 
-                       meta = ("max_change_to_subseq", "Int64"))
-        )
-    )[segment_trip_cols + [
-        "max_change_from_prior", 
-        "max_change_to_subseq"]
-     ].drop_duplicates().query(
-        'max_change_from_prior > 1 or max_change_to_subseq > 1'
-    ).reset_index(drop=True)
-    
-    return (convoluted_segments[segment_trip_cols]
-            .compute().reset_index(drop=True))
-
-
-def split_vp_into_groups(
-    df: dd.DataFrame, 
-    group_cols: list,
-    col_to_find_groups: str = "location_timestamp_local"
-) -> dd.DataFrame:
-    """
-    Within each segment-trip, break up the vp into 2 groups using 
-    vp_idx. Within each group, check direction.
-    Only correct sjoin results are kept.
-    
-    Can use vp_idx, should be simpler than original use of location_timestamp_local.
-    """
-    if col_to_find_groups == "location_timestamp_local":
-        col = f"{col_to_find_groups}_sec"
-
-        df = segment_calcs.convert_timestamp_to_seconds(
-            df, [col_to_find_groups])
-    else:
-        col = col_to_find_groups
-    
-    mean_df = (df.groupby(group_cols, observed=True, group_keys=False)
-                 .agg({col: "mean"})
-                 .reset_index()
-                 .rename(columns = {col: "avg"})
-                )
-    
-    df2 = dd.merge(
-        df,
-        mean_df,
-        on = group_cols,
-    )
-    
-    df2 = df2.assign(
-        group = df2.apply(
-            lambda x: 0 if x[col] <= x.avg 
-            else 1, axis=1, meta=("group", "int8"))
-    ).drop(columns = "avg")
-    
-    return df2
-    
-    
-def get_first_last_position_in_group(
-    df: dd.DataFrame, 
-    group_cols: list,
-    col_to_find_groups: str = "location_timestamp_local_sec"
-) -> pd.DataFrame:
-    """
-    For each grouping of vp (separated by the mean timestamp)
-    for a segment-trip, get the first and last vp.
-    Find the direction each pair of points.
-    """
-    col = col_to_find_groups
-    trip_group_cols = group_cols + ["group"]
-    
-    grouped_df = df.groupby(trip_group_cols, observed=True, group_keys=False)
-    
-    first = (grouped_df
-             .agg({col: "min"})
-             .reset_index()
-            )
-    
-    last = (grouped_df
-             .agg({col: "max"})
-             .reset_index()
-            )
-    
-    keep_cols = trip_group_cols + [col, "x", "y"]
-    
-    pared_down = (dd.multi.concat([first, last], axis=0)
-                  [trip_group_cols + [col]]
-                  .drop_duplicates()
-                  .reset_index(drop=True)
-                 )
-    
-    # get rid of the groups with only 1 obs 
-    # if it has only 1 point (cannot calculate direction vector), 
-    # which means it'll get excluded down the line
-    more_than_2 = (pared_down
-                   .groupby(trip_group_cols, observed=True, group_keys=False)
-                   [col].size()
-                   .loc[lambda x: x > 1]
-                   .reset_index()
-                   .drop(columns = col)
-                  )
-    
-    pared_down2 = dd.merge(
-        pared_down,
-        more_than_2,
-        on = trip_group_cols
-    ).reset_index(drop=True)
-    
-    # Do subset first, because dask doesn't like subsetting on-the-fly
-    df2 = df[keep_cols]
-    df3 = dd.merge(
-        df2,
-        pared_down2,
-        on = trip_group_cols + [col]
-    ).compute() # compute so we can sort by multiple columns
-    
-    # Sorting right before the groupby causes errors
-    df3 = df3.sort_values(
-        trip_group_cols + [col]
-    ).reset_index(drop=True)
-    
-    df3 = df3.assign(
-        obs = (df3.groupby(trip_group_cols, observed=True, 
-                           group_keys=False)[col]
-               .cumcount() + 1
-              ).astype("int8")
-    )
-    
-    return df3
-
-
-def get_stop_segments_direction_vector(
-    stop_segments: gpd.GeoDataFrame
-) -> pd.DataFrame:
-    """
-    Grab the first and last coordinate points in the stop segment
-    and turn that into a normalized vector.
-    """
-    # Take the stop segment geometry and turn it into an array of coords
-    shape_array = [np.array(shapely.LineString(i).coords) 
-               for i in stop_segments.geometry]
-    
-    # Grab the first and last items in the array, 
-    # and turn it back to shapely
-    subset_shape_array = [
-        np.array(
-            [shapely.geometry.Point(i[0]), 
-             shapely.geometry.Point(i[-1])]
-        ).flatten() for i in shape_array
-    ]
-    
-    # Get the shape's direction vector and normalize it
-    direction_vector = [
-        wrangle_shapes.distill_array_into_direction_vector(i) 
-        for i in subset_shape_array
-    ]
-    
-    shape_vec = [wrangle_shapes.get_normalized_vector(i) 
-                 for i in direction_vector]
-    
-    # Assign this vector as a column, drop geometry, since we can
-    # bring it back for full df later
-    stop_segments2 = stop_segments.assign(
-        segments_vector = shape_vec
-    ).drop(columns = "geometry")
-    
-    return stop_segments2
-
-
-def find_vp_direction_vector(
-    df: pd.DataFrame, 
-    group_cols: list,
-    crs: str = PROJECT_CRS
-) -> pd.DataFrame:
-    """
-    Get direction vector for first and last vp within segment.
-    """
-    trip_group_cols = group_cols + ["group", "segments_vector"]
-    keep_cols = trip_group_cols + ["x", "y"]
-    
-    first_position = df[df.obs == 1][keep_cols]
-    last_position = df[df.obs==2][keep_cols]
-    
-    # Set this up to be wide so we can compare positions and 
-    # get a vector
-    df_wide = pd.merge(
-        first_position,
-        last_position,
-        on = trip_group_cols,
-        suffixes = ('_start', '_end')
-    ).sort_values(trip_group_cols).reset_index(drop=True)
-    
-    # Use 2 geoseries, the first point and the last point
-    first_series = gpd.points_from_xy(
-        df_wide.x_start, df_wide.y_start,
-        crs=WGS84
-    ).to_crs(crs)
-           
-    last_series = gpd.points_from_xy(
-        df_wide.x_end, df_wide.y_end, 
-        crs=WGS84
-    ).to_crs(crs)
-    
-    # Input 2 series to get a directon for each element-pair 
-    direction_vector = [
-        wrangle_shapes.get_direction_vector(start, end) 
-        for start, end in zip(first_series, last_series)
-    ]
-    
-    # Normalize vector by Pythagorean Theorem to get values between -1 and 1
-    vector_normalized = [wrangle_shapes.get_normalized_vector(i) 
-                     for i in direction_vector]
-    
-    results = df_wide[trip_group_cols]
-    results = results.assign(
-        vp_vector = vector_normalized
-    )
-    
-    # Take the dot product. 
-    # positive = same direction; 0 = orthogonal; negative = opposite direction
-    dot_result = [wrangle_shapes.dot_product(vec1, vec2) for vec1, vec2 in 
-                  zip(results.segments_vector, results.vp_vector)]
-    
-    results = results.assign(
-        dot_product = dot_result
-    )
-    
-    return results
-
-def check_vp_direction_against_segment_direction(
-    convoluted_sjoin_results: dd.DataFrame,
-    convoluted_segments: pd.DataFrame,
-    segment_identifier_cols: list,
-    grouping_col: str
-) -> dd.DataFrame:
-    """
-    Return vp sjoined to segment_identifier_cols that are 
-    should be excluded.
-    """
-    segment_trip_cols = ["trip_instance_key"] + segment_identifier_cols
-    
-    convoluted_vp_grouped = split_vp_into_groups(
-        convoluted_sjoin_results, 
-        group_cols = segment_trip_cols,
-        col_to_find_groups = "vp_idx"
-    ).persist()
-
-    convoluted_vp_first_last = get_first_last_position_in_group(
-        convoluted_vp_grouped,
-        group_cols = segment_trip_cols,
-        col_to_find_groups = "vp_idx"
-    )
-    
-    shapes_with_error = convoluted_segments[grouping_col].unique().tolist()
-    
-    segments_to_fix = gpd.read_parquet(
-        f"{SEGMENT_GCS}stop_segments_{analysis_date}.parquet",
-        columns = segment_identifier_cols + ["geometry"],
-        filters = [[(grouping_col, "in", shapes_with_error)]]
-    ).merge(
-        convoluted_segments[segment_identifier_cols],
-        on = segment_identifier_cols,
-        how = "inner"
-    )
-    
-    segments_to_fix = get_stop_segments_direction_vector(
-        segments_to_fix)
-
-    vp_with_segment_vec = pd.merge(
-        segments_to_fix,
-        convoluted_vp_first_last,
-        on = segment_identifier_cols,
-    )
-
-    vp_dot_prod = find_vp_direction_vector(
-        vp_with_segment_vec, segment_trip_cols)
-
-    vp_to_drop = vp_dot_prod[vp_dot_prod.dot_product < 0][
-        segment_trip_cols + ["group"]]
-    
-    vp_to_seg_drop = (convoluted_vp_grouped
-                      .merge(
-                          vp_to_drop,
-                          on = segment_trip_cols + ["group"],
-                          how = "inner"
-                      )
-                 )[segment_identifier_cols + ["vp_idx"]].drop_duplicates()
-    
-    return vp_to_seg_drop
-
-
-def remove_erroneous_sjoin_results(
-    analysis_date: str, 
-    dict_inputs: dict
-):
-    """
-    Split the sjoin results into segment-trips that look ok 
-    and ones that look convoluted.
-    Fix the convoluted sjoins by checking for direction.
-    Drop the erroneous ones.
-    Save over the existing sjoin results.
-    """
-    USABLE_VP = dict_inputs["stage1"]
-    INPUT_FILE_PREFIX = dict_inputs["stage2"]
-    SEGMENT_IDENTIFIER_COLS = dict_inputs["segment_identifier_cols"]
-    SEGMENT_TRIP_COLS = ["trip_instance_key"] + SEGMENT_IDENTIFIER_COLS
-    GROUPING_COL = dict_inputs["grouping_col"]
-
-    vp_trip_info = pd.read_parquet(
-        f"{SEGMENT_GCS}{USABLE_VP}_{analysis_date}/",
-        columns = ["vp_idx", "trip_instance_key"]
-    )
-
-    sjoin_results = pd.read_parquet(
-        f"{SEGMENT_GCS}vp_sjoin/{INPUT_FILE_PREFIX}_{analysis_date}/",
-    ).merge(
-        vp_trip_info,
-        on = "vp_idx",
-        how = "inner"
-    ).sort_values(SEGMENT_TRIP_COLS + ["vp_idx"]).reset_index(drop=True)
-
-    # We can do groupby and shift with ddfs, but
-    # divisions not known error addressed only with sort=False
-    ddf = dd.from_pandas(sjoin_results, npartitions=80, sort=False)
-
-    convoluted_segments = find_convoluted_segments(
-        ddf, SEGMENT_IDENTIFIER_COLS)
-    
-    error_shapes = convoluted_segments.shape_array_key.unique().tolist()
-        
-    convoluted_sjoin_results = merge_usable_vp_with_sjoin_vpidx(
-        f"{USABLE_VP}_{analysis_date}",
-        f"{INPUT_FILE_PREFIX}_{analysis_date}",
-        sjoin_filtering = [[(GROUPING_COL, "in", error_shapes)]]
-    ).merge(
-        convoluted_segments,
-        on = SEGMENT_TRIP_COLS,
-        how = "inner"
-    )
-    
-    convoluted_sjoin_results = convoluted_sjoin_results.repartition(npartitions=5)
-
-    convoluted_sjoin_drop = check_vp_direction_against_segment_direction(
-        convoluted_sjoin_results,
-        convoluted_segments,
-        SEGMENT_IDENTIFIER_COLS,
-        GROUPING_COL
-    )
-        
-    all_sjoin_results = dd.read_parquet(
-        f"{SEGMENT_GCS}vp_sjoin/{INPUT_FILE_PREFIX}_{analysis_date}",
-    )
-    
-    cleaned_sjoin_results = dd.merge(
-        all_sjoin_results,
-        convoluted_sjoin_drop,
-        on = SEGMENT_IDENTIFIER_COLS + ["vp_idx"],
-        how = "left",
-        indicator = True
-    ).query('_merge=="left_only"').drop(columns = "_merge")
-    
-    cleaned_sjoin_results = (cleaned_sjoin_results.repartition(npartitions=5)
-                             .reset_index(drop=True))
-    
-    cleaned_sjoin_results.to_parquet(
-        f"{SEGMENT_GCS}vp_sjoin/{INPUT_FILE_PREFIX}_{analysis_date}"
-    )
-    
-    
-if __name__ == "__main__":
-    
-    LOG_FILE = "../logs/sjoin_vp_segments.log"
-    logger.add(LOG_FILE, retention="3 months")
-    logger.add(sys.stderr, 
-               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
-               level="INFO")
-    
-    logger.info(f"Analysis date: {analysis_date}")
-    
-    start = datetime.datetime.now()
-    
-    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
-    remove_erroneous_sjoin_results(analysis_date, STOP_SEG_DICT)
-
-    end = datetime.datetime.now()
-    logger.info(f"remove erroneous sjoin results: {end-start}")
-    
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/A3_valid_vehicle_positions.py b/rt_segment_speeds/scripts/A3_valid_vehicle_positions.py
deleted file mode 100644
index 18f3865c2..000000000
--- a/rt_segment_speeds/scripts/A3_valid_vehicle_positions.py
+++ /dev/null
@@ -1,148 +0,0 @@
-"""
-Filter out unusable trips using RT trip diagnostics.
-
-Keep the enter / exit points for each segment.
-"""
-import dask.dataframe as dd
-import datetime
-import numpy as np
-import pandas as pd
-import sys
-
-from loguru import logger
-from typing import Literal
-
-from segment_speed_utils import helpers, segment_calcs
-from segment_speed_utils.project_vars import (SEGMENT_GCS, analysis_date, 
-                                              CONFIG_PATH)
-    
-def identify_stop_segment_cases(
-    analysis_date: str, 
-    grouping_col: str,
-    loop_or_inlining: Literal[0, 1]
-) -> np.ndarray:
-    """
-    Filter based on the column loop_or_inlining in the
-    stops_projected file. 
-    1 is special case, can have loops or inlining.
-    0 is normal case
-    """
-    shape_cases = pd.read_parquet(
-        f"{SEGMENT_GCS}stops_projected_{analysis_date}/",
-        filters = [[("loop_or_inlining", "==", loop_or_inlining)]],
-        columns = [grouping_col]
-    )[grouping_col].unique().tolist()
-
-    return shape_cases
-
-
-def merge_usable_vp_with_sjoin_vpidx(
-    usable_vp_file: str,
-    sjoin_results_file: str,
-    sjoin_filtering: tuple = None,
-    **kwargs
-) -> dd.DataFrame:
-    """
-    Grab all the usable vp (with lat/lon columns), filter it down to
-    normal or special cases, and merge it
-    against the sjoin results (which only has vp_idx + segment_identifier_cols).
-    """
-    # First, grab all the usable vp (with lat/lon columns)
-    usable_vp = dd.read_parquet(
-        f"{SEGMENT_GCS}{usable_vp_file}", 
-        **kwargs
-    ).repartition(npartitions=100)
-            
-    # Grab our results of vp_idx joined to segments
-    vp_to_seg = dd.read_parquet(
-        f"{SEGMENT_GCS}vp_sjoin/{sjoin_results_file}",
-        filters = sjoin_filtering,
-    )
-    
-    usable_vp_full_info = dd.merge(
-        usable_vp,
-        vp_to_seg,
-        on = "vp_idx",
-        how = "inner"
-    )
-    
-    return usable_vp_full_info
-
-
-def pare_down_vp_by_segment(
-    analysis_date: str, 
-    dict_inputs: dict = {}
-):
-    """
-    Pare down vehicle positions that have been joined to segments
-    to keep the enter / exit timestamps.
-    Also, exclude any bad batches of trips.
-    """
-    time0 = datetime.datetime.now()
-    
-    USABLE_VP = dict_inputs["stage1"]
-    INPUT_FILE_PREFIX = dict_inputs["stage2"]
-    SEGMENT_IDENTIFIER_COLS = dict_inputs["segment_identifier_cols"]
-    GROUPING_COL = dict_inputs["grouping_col"]
-    TIMESTAMP_COL = dict_inputs["timestamp_col"]
-    EXPORT_FILE = dict_inputs["stage3"]
-
-    # First, grab all the usable vp (with lat/lon columns)
-    usable_vp = merge_usable_vp_with_sjoin_vpidx(
-        f"{USABLE_VP}_{analysis_date}",
-        f"{INPUT_FILE_PREFIX}_{analysis_date}",
-        sjoin_filtering = None,
-        columns = ["vp_idx", "trip_instance_key", TIMESTAMP_COL,
-                   "x", "y"]
-    )
-    
-    time1 = datetime.datetime.now()    
-    logger.info(f"merge usable vp with sjoin results: {time1 - time0}")
-
-    vp_to_keep = segment_calcs.keep_min_max_timestamps_by_segment(
-        usable_vp,       
-        SEGMENT_IDENTIFIER_COLS + ["trip_instance_key"],
-        TIMESTAMP_COL
-    )
-        
-    time2 = datetime.datetime.now()
-    logger.info(f"keep enter/exit points: {time2 - time1}")
-
-    vp_to_keep = (vp_to_keep.drop_duplicates()
-                  .reset_index(drop=True)
-                  .repartition(npartitions=3)
-                 )
-    vp_to_keep.to_parquet(
-        f"{SEGMENT_GCS}{EXPORT_FILE}_{analysis_date}",
-        overwrite=True
-    )
-    
-    logger.info(f"exported: {datetime.datetime.now() - time2}")
-    
-    
-if __name__ == "__main__":
-    
-    LOG_FILE = "../logs/valid_vehicle_positions.log"
-    logger.add(LOG_FILE, retention="3 months")
-    logger.add(sys.stderr, 
-               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
-               level="INFO")
-    
-    logger.info(f"Analysis date: {analysis_date}")
-    
-    start = datetime.datetime.now()
-    
-    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
-   
-    time1 = datetime.datetime.now()
-    
-    pare_down_vp_by_segment(
-        analysis_date,
-        dict_inputs = STOP_SEG_DICT
-    )
-    
-    time2 = datetime.datetime.now()
-    logger.info(f"pare down vp by stop segments for all cases {time2 - time1}")
-    
-    end = datetime.datetime.now()
-    logger.info(f"execution time: {end-start}")
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/B1_speeds_by_segment_trip.py b/rt_segment_speeds/scripts/B1_speeds_by_segment_trip.py
deleted file mode 100644
index 75b8d7b14..000000000
--- a/rt_segment_speeds/scripts/B1_speeds_by_segment_trip.py
+++ /dev/null
@@ -1,379 +0,0 @@
-"""
-Do linear referencing by segment-trip 
-and derive speed.
-"""
-import os
-os.environ['USE_PYGEOS'] = '0'
-
-import dask.dataframe as dd
-import dask_geopandas as dg
-import datetime
-import geopandas as gpd
-import numpy as np
-import pandas as pd
-import sys
-
-from loguru import logger
-
-from calitp_data_analysis import geography_utils 
-from segment_speed_utils import helpers, segment_calcs, wrangle_shapes
-from segment_speed_utils.project_vars import (SEGMENT_GCS, analysis_date, 
-                                              PROJECT_CRS, CONFIG_PATH)    
-from shared_utils.rt_utils import MPH_PER_MPS
-
-
-def linear_referencing_vp_against_line(
-    vp: dd.DataFrame, 
-    segments: gpd.GeoDataFrame,
-    segment_identifier_cols: list,
-    timestamp_col: str
-) -> dd.DataFrame:
-    """
-    Take the vp x,y columns, make into gdf.
-    Merge in segment geometry and do linear referencing.
-    Return just the shape_meters result and timestamp converted to seconds.
-    """
-    time0 = datetime.datetime.now()
-    
-    # https://stackoverflow.com/questions/71685387/faster-methods-to-create-geodataframe-from-a-dask-or-pandas-dataframe
-    # https://github.com/geopandas/dask-geopandas/issues/197    
-    vp_gddf = dg.from_dask_dataframe(
-        vp, 
-        geometry=dg.points_from_xy(vp, "x", "y")
-    ).set_crs(geography_utils.WGS84).to_crs(PROJECT_CRS).drop(columns = ["x", "y"])
-    
-    vp_with_seg_geom = dd.merge(
-        vp_gddf, 
-        segments,
-        on = segment_identifier_cols,
-        how = "inner"
-    ).rename(columns = {
-        "geometry_x": "vp_geometry",
-        "geometry_y": "segment_geometry"}
-    ).set_geometry("vp_geometry")
-
-    vp_with_seg_geom = vp_with_seg_geom.repartition(npartitions=50)
-          
-    time1 = datetime.datetime.now()
-    logger.info(f"set up merged vp with segments: {time1 - time0}")
-    
-    shape_meters_series = vp_with_seg_geom.map_partitions(
-        wrangle_shapes.project_point_geom_onto_linestring,
-        "segment_geometry",
-        "vp_geometry",
-        meta = ("shape_meters", "float")
-    )
-    
-    vp_with_seg_geom = segment_calcs.convert_timestamp_to_seconds(
-        vp_with_seg_geom, [timestamp_col])
-    
-    vp_with_seg_geom = vp_with_seg_geom.assign(
-        shape_meters = shape_meters_series,
-        segment_meters = vp_with_seg_geom.segment_geometry.length
-    )
-    
-    time2 = datetime.datetime.now()
-    logger.info(f"linear referencing: {time2 - time1}")
-    
-    drop_cols = [f"{timestamp_col}", "vp_geometry", "segment_geometry"]
-    vp_with_seg_geom2 = vp_with_seg_geom.drop(columns = drop_cols)
-    
-    return vp_with_seg_geom2
-    
-
-def make_wide_get_speed(
-    df: dd.DataFrame, 
-    group_cols: list,
-    timestamp_col: str
-) -> dd.DataFrame:
-    """
-    Get df wide and set up current vp_idx and get meters/sec_elapsed
-    against prior and calculate speed.
-    """
-    vp2 = (
-        df.groupby(group_cols, 
-                   observed=True, group_keys=False)
-        .agg({"vp_idx": "max"})
-        .reset_index()
-        .merge(
-            df,
-            on = group_cols + ["vp_idx"],
-            how = "inner"
-        )
-    )
-
-    vp1 = (
-        df.groupby(group_cols, 
-                   observed=True, group_keys=False)
-        .agg({"vp_idx": "min"})
-        .reset_index()
-        .merge(
-            df,
-            on = group_cols + ["vp_idx"],
-            how = "inner"
-        ).rename(columns = {
-            "vp_idx": "prior_vp_idx",
-            f"{timestamp_col}_sec": f"prior_{timestamp_col}_sec",
-            "shape_meters": "prior_shape_meters",
-        })
-    )
-    
-    df_wide = dd.merge(
-        vp2,
-        vp1,
-        on = group_cols,
-        how = "left"
-    )
-    
-    speed = segment_calcs.derive_speed(
-        df_wide,
-        distance_cols = ("prior_shape_meters", "shape_meters"),
-        time_cols = (f"prior_{timestamp_col}_sec", f"{timestamp_col}_sec")
-    )
-    
-    speed = speed.assign(
-        pct_segment = speed.meters_elapsed.divide(speed.segment_meters)
-    )
-    
-    return speed
-
-   
-def filter_for_unstable_speeds(
-    df: pd.DataFrame,
-    pct_segment_threshold: float
-) -> tuple[pd.DataFrame]:
-    ok_speeds = df[df.pct_segment > pct_segment_threshold]
-    low_speeds = df[df.pct_segment <= pct_segment_threshold]
-    
-    return ok_speeds, low_speeds
-
-
-def recalculate_low_speeds_with_straight_distance(
-    low_speeds_df: pd.DataFrame,
-    group_cols: list,
-    timestamp_col: str
-):
-    """
-    For low speed segments, select a different vp_idx.
-    Use the current vp_idx and subtract by 1.
-    This will fill in something where the segment only had 1 point previously.
-    """
-    keep_cols = group_cols + [
-        "vp_idx", "location_timestamp_local_sec",
-    ]
-    
-    df1 = low_speeds_df[keep_cols].drop_duplicates().reset_index(drop=True)
-    
-    df1 = df1.assign(
-        prior_vp_idx = df1.vp_idx - 1
-    )
-    
-    usable_vp = dd.read_parquet(
-        f"{SEGMENT_GCS}vp_usable_{analysis_date}",
-        columns = ["trip_instance_key",
-                   "vp_idx", timestamp_col, "x", "y"]
-    )
-
-    vp_idx_bounds = segment_calcs.get_usable_vp_bounds_by_trip(usable_vp)
-    
-    df2 = pd.merge(
-        df1, 
-        vp_idx_bounds,
-        on = "trip_instance_key",
-        how = "inner"
-    )
-    
-    # Check that the prior_vp_idx actually is on the same trip (must be within bounds)
-    # If not, select the next point
-    df2 = df2.assign(
-        prior_vp_idx = df2.apply(
-            lambda x: 
-            x.vp_idx + 1 if (x.prior_vp_idx < x.min_vp_idx) and 
-            (x.vp_idx + 1 <= x.max_vp_idx)
-            else x.prior_vp_idx, 
-            axis=1)
-    ).drop(columns = ["trip_instance_key", "min_vp_idx", "max_vp_idx"])
-    
-    # We will need point geom again, since we are using straight distance
-    subset_vp_idx = np.union1d(
-        df2.vp_idx.unique(), 
-        df2.prior_vp_idx.unique()
-    ).tolist()
-    
-    usable_vp2 = usable_vp[usable_vp.vp_idx.isin(subset_vp_idx)].compute()
-    
-    usable_gdf = geography_utils.create_point_geometry(
-        usable_vp2,
-        longitude_col = "x",
-        latitude_col = "y",
-        crs = PROJECT_CRS
-    ).drop(columns = ["x", "y"]).reset_index(drop=True)
-    
-    usable_gdf2 = segment_calcs.convert_timestamp_to_seconds(
-        usable_gdf, [timestamp_col]).drop(columns = timestamp_col)
-    
-    # Merge in coord for current_vp_idx
-    # we already have a timestamp_sec for current vp_idx
-    gdf = pd.merge(
-        usable_gdf2.drop(columns = f"{timestamp_col}_sec"),
-        df2,
-        on = "vp_idx",
-        how = "inner"
-    )
-    
-    # Merge in coord for prior_vp_idx
-    gdf2 = pd.merge(
-        gdf,
-        usable_gdf2[
-            ["vp_idx", f"{timestamp_col}_sec", "geometry"]
-        ].add_prefix("prior_"),
-        on = "prior_vp_idx",
-        how = "inner"
-    )
-    
-    # should we do straight distance or interpolate against full shape?
-    # what if full shape is problematic?
-    # do we want to do a check against the scale? that's not very robust either though
-
-    gdf2 = gdf2.assign(
-        straight_distance = gdf2.geometry.distance(gdf2.prior_geometry)
-    )
-    
-    gdf2 = gdf2.assign(
-        sec_elapsed = (gdf2[f"{timestamp_col}_sec"] - 
-                   gdf2[f"prior_{timestamp_col}_sec"]).abs()
-    )
-    
-    gdf2 = gdf2.assign(
-        speed_mph = gdf2.straight_distance.divide(gdf2.sec_elapsed) * MPH_PER_MPS
-    )
-    
-    drop_cols = ["geometry", "prior_geometry"]
-    results = gdf2.drop(columns = drop_cols)
-                        
-    return results
-
-
-def linear_referencing_and_speed_by_segment(
-    analysis_date: str,
-    dict_inputs: dict = {}
-):
-    """
-    With just enter / exit points on segments, 
-    do the linear referencing to get shape_meters, and then derive speed.
-    Do a second pass for low speed segments with straight distance.
-    """
-    time0 = datetime.datetime.now()    
-    
-    VP_FILE = dict_inputs["stage3"]
-    SEGMENT_FILE = dict_inputs["segments_file"]
-    SEGMENT_IDENTIFIER_COLS = dict_inputs["segment_identifier_cols"]
-    TIMESTAMP_COL = dict_inputs["timestamp_col"]    
-    EXPORT_FILE = dict_inputs["stage4"]
-    PCT_SEGMENT_MIN = dict_inputs["pct_segment_minimum"]
-    
-    # Keep subset of columns - don't need it all. we can get the 
-    # columns dropped through segments file
-    vp_keep_cols = [
-        'trip_instance_key',
-        TIMESTAMP_COL,
-        'x', 'y', 'vp_idx'
-    ] + SEGMENT_IDENTIFIER_COLS
-    
-    vp = dd.read_parquet(
-        f"{SEGMENT_GCS}{VP_FILE}_{analysis_date}",
-        columns = vp_keep_cols
-    )
-    
-    segments = helpers.import_segments(
-        SEGMENT_GCS,
-        f"{SEGMENT_FILE}_{analysis_date}", 
-        columns = SEGMENT_IDENTIFIER_COLS + ["geometry"]
-    ).dropna(subset="geometry").reset_index(drop=True)
-     
-    vp_with_seg_geom = linear_referencing_vp_against_line(
-        vp,
-        segments,
-        SEGMENT_IDENTIFIER_COLS,
-        TIMESTAMP_COL
-    ).persist()
-
-    time1 = datetime.datetime.now()
-    logger.info(f"linear referencing: {time1 - time0}")
-    
-    SEGMENT_TRIP_COLS = ["trip_instance_key", 
-                         "segment_meters"] + SEGMENT_IDENTIFIER_COLS
-
-    initial_speeds = make_wide_get_speed(
-        vp_with_seg_geom, SEGMENT_TRIP_COLS, TIMESTAMP_COL
-    ).compute()
-    
-    
-    time2 = datetime.datetime.now()
-    logger.info(f"make wide and get initial speeds: {time2 - time1}")
-    
-    ok_speeds, low_speeds = filter_for_unstable_speeds(
-        initial_speeds,
-        pct_segment_threshold = PCT_SEGMENT_MIN
-    )
-    
-    low_speeds_recalculated = recalculate_low_speeds_with_straight_distance(
-        low_speeds,
-        SEGMENT_TRIP_COLS,
-        TIMESTAMP_COL
-    )
-    
-    # Add a flag that tells us speed was recalculated
-    # Combine columns and rename straight distance as meters_elapsed
-    low_speeds_recalculated = low_speeds_recalculated.assign(
-        flag_recalculated = 1,
-        meters_elapsed = low_speeds_recalculated.straight_distance
-    )
-        
-    keep_cols = SEGMENT_TRIP_COLS + [
-        "vp_idx", "prior_vp_idx", 
-        f"{TIMESTAMP_COL}_sec", f"prior_{TIMESTAMP_COL}_sec",
-        "meters_elapsed",
-        "sec_elapsed",
-        "pct_segment",
-        "speed_mph",
-        "flag_recalculated",
-    ]
-    
-    speeds = pd.concat([
-        ok_speeds,
-        low_speeds_recalculated
-    ], axis=0).sort_values(SEGMENT_IDENTIFIER_COLS + ["trip_instance_key"]
-                      ).reset_index(drop=True)    
-    
-    speeds = speeds.assign(
-        flag_recalculated = speeds.flag_recalculated.fillna(0).astype("int8")
-    )[keep_cols]
-    
-    time3 = datetime.datetime.now()
-    logger.info(f"recalculate speeds and get final: {time3 - time2}")
-    
-    speeds.to_parquet(
-        f"{SEGMENT_GCS}{EXPORT_FILE}_{analysis_date}.parquet", 
-    )
-    
-    
-if __name__ == "__main__": 
-    
-    LOG_FILE = "../logs/speeds_by_segment_trip.log"
-    logger.add(LOG_FILE, retention="3 months")
-    logger.add(sys.stderr, 
-               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
-               level="INFO")
-    
-    logger.info(f"Analysis date: {analysis_date}")
-    
-    start = datetime.datetime.now()
-
-    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
-        
-    linear_referencing_and_speed_by_segment(analysis_date, STOP_SEG_DICT)
-            
-    logger.info(f"speeds for stop segments: {datetime.datetime.now() - start}")
-    logger.info(f"execution time: {datetime.datetime.now() - start}")
-            
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/B2_avg_speeds_by_segment.py b/rt_segment_speeds/scripts/B2_avg_speeds_by_segment.py
deleted file mode 100644
index dc499e5d1..000000000
--- a/rt_segment_speeds/scripts/B2_avg_speeds_by_segment.py
+++ /dev/null
@@ -1,181 +0,0 @@
-"""
-Quick aggregation for speed metrics by segment
-"""
-import datetime
-import geopandas as gpd
-import pandas as pd
-import sys
-
-from loguru import logger
-
-from segment_speed_utils import helpers, sched_rt_utils
-from segment_speed_utils.project_vars import (SEGMENT_GCS, analysis_date, 
-                                              CONFIG_PATH)
-from calitp_data_analysis import utils, geography_utils
-
-
-def calculate_avg_speeds(
-    df: pd.DataFrame,
-    group_cols: list
-) -> pd.DataFrame:
-    """
-    Calculate the median, 20th, and 80th percentile speeds 
-    by groups.
-    """
-    # Take the average after dropping unusually high speeds
-    grouped_df = df.groupby(group_cols, observed=True, group_keys=False)
-    
-    
-    avg = (grouped_df
-          .agg({
-            "speed_mph": "median",
-            "trip_instance_key": "nunique"})
-          .reset_index()
-    )
-    
-    p20 = (grouped_df
-           .agg({"speed_mph": lambda x: x.quantile(0.2)})
-           .reset_index()  
-          )
-    
-    p80 = (grouped_df
-           .agg({"speed_mph": lambda x: x.quantile(0.8)})
-           .reset_index()  
-          )
-    
-    stats = pd.merge(
-        avg.rename(columns = {"speed_mph": "p50_mph", 
-                              "trip_instance_key": "n_trips"}),
-        p20.rename(columns = {"speed_mph": "p20_mph"}),
-        on = group_cols,
-        how = "left"
-    ).merge(
-        p80.rename(columns = {"speed_mph": "p80_mph"}),
-        on = group_cols,
-        how = "left"
-    )
-    
-    # Clean up for map
-    speed_cols = [c for c in stats.columns if "_mph" in c]
-    stats[speed_cols] = stats[speed_cols].round(2)
-    
-    return stats
-    
-def speeds_with_segment_geom(
-    analysis_date: str, 
-    max_speed_cutoff: int = 70,
-    dict_inputs: dict = {},
-) -> gpd.GeoDataFrame: 
-    """
-    Import the segment-trip table. 
-    Average the speed_mph across all trips present in the segment.
-    By default, filter out rows where meters_elapsed covers less than 40% of segment length
-    """
-    SEGMENT_FILE = dict_inputs["segments_file"]
-    SEGMENT_IDENTIFIER_COLS = dict_inputs["segment_identifier_cols"]
-    SPEEDS_FILE = dict_inputs["stage4"]
-    
-    # Load in segment geometry
-    segment_cols_to_keep = SEGMENT_IDENTIFIER_COLS + [
-        "schedule_gtfs_dataset_key", 
-        "stop_id",
-        "loop_or_inlining",
-        "geometry", 
-        "district_name"
-    ]
-    
-    segments = helpers.import_segments(
-        SEGMENT_GCS,
-        f"{SEGMENT_FILE}_{analysis_date}",
-        columns = segment_cols_to_keep
-    )
-        
-    # Read in speeds
-    df = pd.read_parquet(
-        f"{SEGMENT_GCS}{SPEEDS_FILE}_{analysis_date}.parquet", 
-        filters = [[
-            ("speed_mph", "<=", max_speed_cutoff),
-            ("meters_elapsed", ">", 0),
-            ("sec_elapsed", ">", 0)
-    ]])
-    
-    # Do a merge with segments
-    df2 = pd.merge(
-        segments, 
-        df, 
-        on = SEGMENT_IDENTIFIER_COLS, 
-        how = "inner"
-    )
-    
-    # Keep only segments that have RT data. 
-    unique_segments = (df2[segment_cols_to_keep]
-                       .drop_duplicates()
-                       .reset_index(drop = True)
-                      ).to_crs(geography_utils.WGS84)
-    
-    time_of_day_df = sched_rt_utils.get_trip_time_buckets(analysis_date)
-
-    df3 = pd.merge(
-        df2, 
-        time_of_day_df, 
-        on = "trip_instance_key", 
-        how = "inner"
-    )
-    
-    all_day = calculate_avg_speeds(
-        df3, 
-        SEGMENT_IDENTIFIER_COLS
-    )
-    peak = calculate_avg_speeds(
-        df3[df3.time_of_day.isin(["AM Peak", "PM Peak"])], 
-        SEGMENT_IDENTIFIER_COLS
-    )
-    
-    stats = pd.concat([
-        all_day.assign(time_of_day = "all_day"),
-        peak.assign(time_of_day = "peak")
-    ], axis=0)
-    
-    
-    # Merge in segment geometry    
-    gdf = pd.merge(
-        unique_segments,
-        stats,
-        on = SEGMENT_IDENTIFIER_COLS,
-        how = "left"
-    ).sort_values(SEGMENT_IDENTIFIER_COLS + ["time_of_day"]).reset_index(drop=True)
-    
-    return gdf
-
-
-if __name__ == "__main__":
-    
-    LOG_FILE = "../logs/avg_speeds.log"
-    logger.add(LOG_FILE, retention="3 months")
-    logger.add(sys.stderr, 
-               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
-               level="INFO")
-    
-    logger.info(f"Analysis date: {analysis_date}")
-    
-    start = datetime.datetime.now()
-    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
-    EXPORT_FILE = f'{STOP_SEG_DICT["stage5"]}_{analysis_date}'
-    
-    MAX_SPEED = 70
-    
-    # Average the speeds for segment for entire day
-    # Drop speeds above our max cutoff
-    stop_segment_speeds = speeds_with_segment_geom(
-        analysis_date, 
-        max_speed_cutoff = MAX_SPEED,
-        dict_inputs = STOP_SEG_DICT,
-    )
-        
-    utils.geoparquet_gcs_export(
-        stop_segment_speeds,
-        SEGMENT_GCS,
-        EXPORT_FILE
-    )
-    
-    logger.info(f"execution time: {datetime.datetime.now() - start}")
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/B3_export.py b/rt_segment_speeds/scripts/B3_export.py
deleted file mode 100644
index 53333fc6b..000000000
--- a/rt_segment_speeds/scripts/B3_export.py
+++ /dev/null
@@ -1,149 +0,0 @@
-"""
-Attach columns needed for publishing to open data portal.
-Suppress certain rows and columns too.
-"""
-import os
-os.environ['USE_PYGEOS'] = '0'
-
-import dask.dataframe as dd
-import dask_geopandas as dg
-import datetime
-import geopandas as gpd
-import pandas as pd
-
-from shared_utils import schedule_rt_utils, utils
-from calitp_data_analysis.geography_utils import WGS84
-from segment_speed_utils import helpers
-from segment_speed_utils.project_vars import (SEGMENT_GCS, analysis_date, 
-                                              CONFIG_PATH)
-
-def get_operator_natural_identifiers(
-    df: pd.DataFrame, 
-    analysis_date: str
-) -> pd.DataFrame:
-    """
-    For each gtfs_dataset_key-shape_array_key combination,
-    re-attach the natural identifiers and organizational identifiers.
-    Return a df that should be merged against speeds_df.
-    """
-    operator_shape_df = (df[["schedule_gtfs_dataset_key", "shape_array_key"]]
-                         .drop_duplicates()
-                         .reset_index(drop=True)
-                         .rename(columns = {"schedule_gtfs_dataset_key": "gtfs_dataset_key"})
-                        )
-    
-    # Get shape_id back
-    shape_identifiers = helpers.import_scheduled_trips(
-        analysis_date,
-        columns = ["shape_array_key", "shape_id"],
-        get_pandas = True
-    )
-    
-    df_with_shape = pd.merge(
-        operator_shape_df,
-        shape_identifiers,
-        on = "shape_array_key",
-        how = "inner"
-    )
-    
-    # Get base64_url, uri, organization_source_record_id and organization_name
-    crosswalk = schedule_rt_utils.sample_gtfs_dataset_key_to_organization_crosswalk(
-        df_with_shape,
-        analysis_date,
-        quartet_data = "schedule",
-        dim_gtfs_dataset_cols = [
-            "key",
-            "base64_url",
-        ],
-        dim_organization_cols = ["source_record_id", "name"]
-    )
-
-    df_with_org = pd.merge(
-        df_with_shape.rename(columns = {"gtfs_dataset_key": "schedule_gtfs_dataset_key"}),
-        crosswalk,
-        on = "schedule_gtfs_dataset_key",
-        how = "inner"
-    )
-    
-    return df_with_org
-
-
-def finalize_df_for_export(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
-    """
-    Sorting, suppressing columns not needed in export.
-    """
-
-    RENAME_DICT = {
-        "organization_source_record_id": "org_id",
-        "organization_name": "agency",
-    }
-    
-    gdf2 = (gdf.sort_values(["organization_name", 
-                         "shape_id", "stop_sequence"])
-           .reset_index(drop=True)
-           .rename(columns = RENAME_DICT) 
-          )
-    
-    return gdf2
-
-if __name__ == "__main__":
-    
-    start = datetime.datetime.now()
-    
-    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
-    INPUT_FILE = f'{STOP_SEG_DICT["stage5"]}_{analysis_date}'
-    
-    gdf = gpd.read_parquet(
-        f"{SEGMENT_GCS}{INPUT_FILE}.parquet"
-    )
-    
-    operator_identifiers = get_operator_natural_identifiers(gdf, analysis_date)
-     
-    time1 = datetime.datetime.now()
-    print(f"get natural identifiers: {time1 - start}")
-    
-    gdf2 = pd.merge(
-        gdf,
-        operator_identifiers,
-        on = ["schedule_gtfs_dataset_key", "shape_array_key"],
-        how = "inner"
-    )
-            
-    final_gdf = finalize_df_for_export(gdf2)
-        
-    time2 = datetime.datetime.now()
-    print(f"finalize: {time2 - time1}")
-    
-    keep_cols = [
-        'org_id', 'agency',
-        'shape_id', 'stop_sequence', 'stop_id', 
-        'geometry',
-        'p50_mph', 'p20_mph', 
-        'p80_mph', 'n_trips', 
-        'time_of_day', 
-        'base64_url',
-        'district_name'
-    ]
-    
-    utils.geoparquet_gcs_export(
-        final_gdf[keep_cols],
-        f"{SEGMENT_GCS}export/",
-        INPUT_FILE
-    )
-    
-    # Keep a tabular version (geom is big to save) for us to compare what's published
-    # and contains columns we use for internal modeling 
-    # (shape_array_key, gtfs_dataset_key, etc)
-    final_gdf.drop(columns = "geometry").to_parquet(
-        f"{SEGMENT_GCS}export/{INPUT_FILE}_tabular.parquet"
-    )
-       
-    utils.geoparquet_gcs_export(
-        final_gdf[keep_cols],
-        f"{SEGMENT_GCS}export/",
-        "speeds_by_stop_segments"
-    )
-    
-    end = datetime.datetime.now()
-    print(f"export: {end - time2}")
-    print(f"execution time: {end - start}")
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/C2_triangulate_vp.py b/rt_segment_speeds/scripts/C2_triangulate_vp.py
deleted file mode 100644
index daba8dbfb..000000000
--- a/rt_segment_speeds/scripts/C2_triangulate_vp.py
+++ /dev/null
@@ -1,184 +0,0 @@
-"""
-Starting from the vp that were spatially joined
-to segments, pick a subset of these.
-Triangulate these by picking 5 points to better 
-calculate speeds for the entire trip.
-
-If we pick only 2 points, for a looping route, origin/destination
-are basically the same. If we pick 3 points, this is better 
-for triangulating the distance traveled.
-"""
-import os
-os.environ['USE_PYGEOS'] = '0'
-
-import dask.dataframe as dd
-import datetime
-import numpy as np
-import pandas as pd
-
-from typing import Literal
-
-from segment_speed_utils import helpers
-from segment_speed_utils.project_vars import (SEGMENT_GCS, analysis_date,
-                                              CONFIG_PATH)
-
-from A3_valid_vehicle_positions import merge_usable_vp_with_sjoin_vpidx
-
-
-def triangulate_vp(
-    ddf: dd.DataFrame, 
-    group_cols: list = ["trip_instance_key"]
-) -> np.ndarray:
-    """
-    Grab a sample of vehicle positions for each trip to triangulate distance.
-    These vp already sjoined onto the shape.
-    Roughly pick vp at equally spaced intervals.
-    
-    Dask aggregation can't group and use lambda to create list of possible 
-    vp_idx.
-    """        
-    grouped_ddf = ddf.groupby(group_cols, observed=True, group_keys=False)
-
-    min_df = (grouped_ddf
-              .agg({"vp_idx": "min"})
-              .rename(columns = {"vp_idx": "min_vp_idx"})
-             )
-
-    max_df = (grouped_ddf
-              .agg({"vp_idx": "max"})
-              .rename(columns = {"vp_idx": "max_vp_idx"})
-             )
-    
-    vp_range = dd.merge(
-        min_df,
-        max_df,
-        left_index = True,
-        right_index = True,
-        how = "inner"
-    )
-
-    vp_range = vp_range.persist()
-    
-    vp_range["range_diff"] = vp_range.max_vp_idx - vp_range.min_vp_idx
-    
-    vp_range = vp_range.assign(
-        p25_vp_idx = (vp_range.range_diff * 0.25 + vp_range.min_vp_idx
-                     ).round(0).astype("int64"),
-        p50_vp_idx = (vp_range.range_diff * 0.5 + vp_range.min_vp_idx
-                     ).round(0).astype("int64"),
-        p75_vp_idx = (vp_range.range_diff * 0.75 + vp_range.min_vp_idx
-                     ).round(0).astype("int64"),
-    )
-    
-    vp_idx_cols = [
-        "min_vp_idx", 
-        "p25_vp_idx",
-        "p50_vp_idx", 
-        "p75_vp_idx",
-        "max_vp_idx"
-    ]
-
-    results = vp_range[vp_idx_cols].compute().to_numpy().flatten()    
-    
-    return results
-
-
-def subset_usable_vp(dict_inputs: dict) -> np.ndarray:
-    """
-    Subset all the usable vp and keep a sample of triangulated
-    vp per trip.
-    """
-    SEGMENT_FILE = f'{dict_inputs["segments_file"]}_{analysis_date}'
-    SJOIN_FILE = f'{dict_inputs["stage2"]}_{analysis_date}'
-    USABLE_FILE = f'{dict_inputs["stage1"]}_{analysis_date}'
-    GROUPING_COL = dict_inputs["grouping_col"]
-    
-    all_shapes = pd.read_parquet(
-        f"{SEGMENT_GCS}{SEGMENT_FILE}.parquet",
-        columns = ["shape_array_key"]
-    ).shape_array_key.unique().tolist()
-    
-    # Use this function to attach the crosswalk of sjoin results
-    # back to usable_vp
-    ddf = merge_usable_vp_with_sjoin_vpidx(
-        USABLE_FILE,
-        SJOIN_FILE,
-        sjoin_filtering = [(GROUPING_COL, "in", all_shapes)],
-        columns = ["trip_instance_key", "vp_idx"]
-    )
-    
-    # Results are just vp_idx as np array
-    results = triangulate_vp(
-        ddf, 
-        ["trip_instance_key"]
-    )
-    
-    return results
-
-
-def merge_rt_scheduled_trips(
-    rt_trips: dd.DataFrame,
-    analysis_date: str,
-    group_cols: list = ["trip_instance_key"]) -> dd.DataFrame:
-    """
-    Merge RT trips (vehicle positions) to scheduled trips 
-    to get the shape_array_key.
-    Don't pull other scheduled trip columns now, wait until
-    after aggregation is done.
-    """
-    trips = helpers.import_scheduled_trips(
-        analysis_date,
-        columns = group_cols + ["shape_array_key"],
-        get_pandas = True
-    )
-        
-    df = dd.merge(
-        rt_trips,
-        trips,
-        on = group_cols,
-        how = "left",
-    )
-    
-    return df
-
-
-if __name__ == "__main__":
-    
-    start = datetime.datetime.now()
-    
-    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
-    
-    results = subset_usable_vp(STOP_SEG_DICT)
-        
-    time1 = datetime.datetime.now()
-    print(f"compute results: {time1 - start}")
-    
-    # Use these vp_idx and filter the vp with all the columns
-    vp_idx_list = results.tolist()
-    
-    USABLE_FILE = f'{STOP_SEG_DICT["stage1"]}_{analysis_date}'
-
-    vp_results = dd.read_parquet(
-        f"{SEGMENT_GCS}{USABLE_FILE}/", 
-        columns = [
-            "gtfs_dataset_key", "trip_instance_key",
-            "location_timestamp_local",
-            "x", "y", "vp_idx"],
-        filters = [[("vp_idx", "in", vp_idx_list)]]
-    ).compute()
-
-    vp_with_sched = (
-        merge_rt_scheduled_trips(
-            vp_results, 
-            analysis_date, 
-            group_cols = ["trip_instance_key"]
-        ).sort_values("vp_idx")
-        .reset_index(drop=True)
-    )
-    
-    vp_with_sched.to_parquet(
-        f"{SEGMENT_GCS}trip_summary/vp_subset_{analysis_date}.parquet",
-    )
-    
-    end = datetime.datetime.now()
-    print(f"execution time: {end - start}")
diff --git a/rt_segment_speeds/scripts/C3_trip_route_speed.py b/rt_segment_speeds/scripts/C3_trip_route_speed.py
deleted file mode 100644
index 46795c36d..000000000
--- a/rt_segment_speeds/scripts/C3_trip_route_speed.py
+++ /dev/null
@@ -1,353 +0,0 @@
-"""
-Use triangulated points, 5 sample vp, per trip
-and calculate distance and seconds elapsed.
-For each trip, take the sum of the change in distances, change in time,
-and calculate speed.
-
-Aggregate trip speeds into route-direction averages by time-of-day.
-"""
-import dask.dataframe as dd
-import datetime
-import geopandas as gpd
-import numpy as np
-import pandas as pd
-
-from shared_utils.rt_utils import MPH_PER_MPS
-from calitp_data_analysis.geography_utils import WGS84
-from calitp_data_analysis import utils
-from shared_utils import portfolio_utils, schedule_rt_utils
-from segment_speed_utils import helpers, sched_rt_utils, wrangle_shapes
-from segment_speed_utils.project_vars import (SEGMENT_GCS, analysis_date,
-                                              PROJECT_CRS)
-
-def distance_and_seconds_elapsed(
-    df: pd.DataFrame, 
-    group_cols: list
-) -> pd.DataFrame:
-    """
-    If every trip has 3 vp, we want the change in time and distance
-    between 1st and 2nd, 2nd and 3rd.
-    Then, sum up the change in time and change by trip.
-    """
-    dist_col = "shape_meters"
-    time_col = "location_timestamp_local"
-    sort_cols = group_cols + ["vp_idx"]
-    
-    # Get the change in distance, time for each row
-    df = df.assign(
-        prior_dist = (df.sort_values(sort_cols)
-                      .groupby(group_cols, 
-                               observed=True, group_keys=False)
-                      [dist_col]
-                      .apply(lambda x: x.shift(1))
-                     ),
-        prior_time = (df.sort_values(sort_cols)
-                      .groupby(group_cols, 
-                               observed=True, group_keys=False)
-                      [time_col]
-                      .apply(lambda x: x.shift(1))
-                     )   
-    )
-    
-    # distance should be positive, but sometimes it's not, 
-    # so use absolute value
-    df = df.assign(
-        change_meters = abs(df[dist_col] - df.prior_dist),
-        change_sec = (df[time_col] - df.prior_time).divide(
-                       np.timedelta64(1, 's'))
-    )
-    
-    # For a trip, sum up the total change in distance and time 
-    # Easier to calculate the speed this way, than
-    # taking a weighted average later
-    df2 = (df.groupby(group_cols, 
-                     observed=True, group_keys=False)
-           .agg({"change_meters": "sum", 
-                 "change_sec": "sum"})
-           .reset_index()
-          )
-    
-    df2 = df2.assign(
-        speed_mph = (df2.change_meters.divide(df2.change_sec) * 
-                     MPH_PER_MPS)
-    )
-    
-    return df2
-
-
-def add_scheduled_trip_columns(
-    rt_trips: pd.DataFrame,
-    analysis_date: str,
-    group_cols: list = ["trip_instance_key"]) -> pd.DataFrame:
-    """
-    Merge RT trips (vehicle positions) to scheduled trips.
-    Add in the needed scheduled trip columns to take 
-    route-direction-time_of_day averages.
-    """
-    keep_cols = [
-        "gtfs_dataset_key",
-        "direction_id", 
-        "route_id", "route_short_name", "route_long_name", "route_desc",
-    ] + group_cols
-        
-    crosswalk = helpers.import_scheduled_trips(
-        analysis_date, 
-        columns = keep_cols, 
-        get_pandas = True
-    )
-    
-    common_shape = sched_rt_utils.most_common_shape_by_route_direction(analysis_date)
-    
-    crosswalk2 = pd.merge(
-        crosswalk,
-        common_shape,
-        on = ["schedule_gtfs_dataset_key", "route_id", "direction_id"],
-        how = "inner"
-    ).astype({"direction_id": "Int64"})
-    
-    
-    time_of_day = sched_rt_utils.get_trip_time_buckets(analysis_date)
-    
-    # Clean up route name
-    crosswalk2 = portfolio_utils.add_route_name(
-        crosswalk2
-    ).drop(columns = ["route_short_name", "route_long_name", "route_desc"])
-
-    df = dd.merge(
-        rt_trips,
-        crosswalk2,
-        on = group_cols,
-        how = "left",
-    ).merge(
-        time_of_day,
-        on = group_cols,
-        how = "left"
-    )
-    
-    return df
-
-
-def drop_extremely_low_and_high_speeds(
-    df: pd.DataFrame, 
-    speed_range: tuple
-) -> pd.DataFrame:
-    """
-    Descriptives show the 5th percentile is around 5 mph, 
-    and 95th percentile is around 25 mph.
-    
-    There are some weird calculations for <3 mph, and even
-    some negative values, so let's exclude those...maybe
-    the vp is not traveling across the entirety of the shape.
-    
-    Exclude unusually high speeds, over 70 mph.
-    """
-    low, high = speed_range
-    
-    df2 = df[(df.speed_mph >= low) & 
-             (df.speed_mph <= high)
-            ].reset_index(drop=True)
-    
-    return df2
-
-
-def avg_route_speeds_by_time_of_day(
-    df: pd.DataFrame,
-    group_cols: list,
-    speed_range: tuple = (3, 70)
-) -> pd.DataFrame:
-    """
-    Keep trips with average speeds at least LOWER_BOUND_SPEED
-    and less than or equal to UPPER_BOUND_SPEED.
-    
-    Take the average by route-direction-time_of_day.
-    Also include averages for scheduled trip service_minutes vs 
-    rt trip approximated-service-minutes
-    """
-    df2 = drop_extremely_low_and_high_speeds(df, speed_range = (3, 70))
-    
-    df3 = (df2.groupby(group_cols, 
-                       observed = True, group_keys = False)
-           .agg({
-               "speed_mph": "mean",
-               "service_minutes": "mean",
-               "change_sec": "mean",
-               "trip_instance_key": "count"
-           }).reset_index()
-          )
-    
-    df3 = df3.assign(
-        avg_rt_trip_min = df3.change_sec.divide(60).round(1),
-        service_minutes = df3.service_minutes.round(1),
-        speed_mph = df3.speed_mph.round(1),
-    ).rename(columns = {
-        "service_minutes": "avg_sched_trip_min",
-        "trip_instance_key": "n_trips",
-        "route_name_used": "route_name",
-    }).drop(columns = "change_sec")
-    
-    return df3
-
-
-def final_cleaning_for_export(
-    df: pd.DataFrame, 
-    analysis_date: str
-) -> gpd.GeoDataFrame: 
-    """
-    Attach shape geometry to most common shape_id.
-    """
-    # Attach org name and source_record_id
-    org_crosswalk = (
-        schedule_rt_utils.sample_gtfs_dataset_key_to_organization_crosswalk(
-            df,
-            analysis_date,
-            quartet_data = "vehicle_positions",
-            dim_gtfs_dataset_cols = ["key", "base64_url"],
-            dim_organization_cols = ["source_record_id", 
-                                     "name", "caltrans_district"])
-    )
-    
-    df_with_org = pd.merge(
-        df,
-        org_crosswalk.rename(columns = {
-            "vehicle_positions_gtfs_dataset_key": "gtfs_dataset_key"}),
-        on = "gtfs_dataset_key",
-        how = "inner"
-    )
-    
-    # Attach shape geometry and make sure it's in WGS84
-    shapes = helpers.import_scheduled_shapes(
-        analysis_date,
-        columns = ["shape_array_key", "geometry"],
-        get_pandas = True,
-        crs = WGS84
-    )
-    
-    df_with_shape = pd.merge(
-        shapes,
-        df_with_org,
-        on = "shape_array_key", # once merged, can drop shape_array_key
-        how = "inner"
-    )
-    
-    agency_cols = ['organization_source_record_id', 'organization_name']
-    route_cols = ['route_id', 'route_name', 
-                  'direction_id', 'common_shape_id']
-
-    col_order = agency_cols + route_cols + [
-        'time_of_day',
-        'speed_mph', 'n_trips', 
-        'avg_sched_trip_min', 'avg_rt_trip_min', 
-        'base64_url', 'caltrans_district',
-        'geometry'
-    ]
-    
-    final_df = df_with_shape.reindex(columns = col_order).rename(
-        columns = {"organization_source_record_id": "org_id",
-                   "organization_name": "agency", 
-                   "caltrans_district": "district_name"
-                  })
-
-    return final_df
-
-    
-if __name__ == "__main__":
-    
-    start = datetime.datetime.now()
-    
-    # Merge in the subset of vp to the shape geometry
-    vp = pd.read_parquet(
-        f"{SEGMENT_GCS}trip_summary/vp_subset_{analysis_date}.parquet",
-    )
-    
-    vp = gpd.GeoDataFrame(
-        vp,
-        geometry = gpd.points_from_xy(vp.x, vp.y, crs=WGS84)
-    ).to_crs(PROJECT_CRS).drop(columns = ["x", "y"])
-    
-    # in case there are fewer shapes to grab
-    shapes_list = vp.shape_array_key.unique().tolist()
-
-    # to_crs() takes a long time when os.environ["USE_PYGEOS"] = '0',
-    # so keep pygeos on
-    shapes = helpers.import_scheduled_shapes(
-        analysis_date,
-        columns = ["shape_array_key","geometry"],
-        filters = [[("shape_array_key", "in", shapes_list)]],
-        get_pandas = True,
-        crs = PROJECT_CRS
-    )
-    
-    df = pd.merge(
-        vp,
-        shapes,
-        on = "shape_array_key",
-        how = "inner"
-    ).rename(columns = {"geometry_x": "vp_geometry", 
-                        "geometry_y": "shape_geometry"}
-            ).set_geometry("vp_geometry")
-    
-    # project the vp geometry onto the shape geometry and get shape_meters
-    shape_meters_geoseries = wrangle_shapes.project_point_geom_onto_linestring(
-        df,
-        "shape_geometry",
-        "vp_geometry",
-    )
-
-    df["shape_meters"] = shape_meters_geoseries
-    
-    time1 = datetime.datetime.now()
-    print(f"linear ref: {time1 - start}")
-    
-    # Get trip-level speed
-    speed = distance_and_seconds_elapsed(
-        df,
-        group_cols = ["gtfs_dataset_key", "trip_instance_key"]
-    )
-    
-    # Attach scheduled trip columns, like route, direction, time_of_day
-    speed2 = add_scheduled_trip_columns(
-        speed,
-        analysis_date,
-        group_cols = ["trip_instance_key"]
-    )
-    
-    time2 = datetime.datetime.now()
-    print(f"calculate speed: {time2 - time1}")
-        
-    speed2.to_parquet(
-        f"{SEGMENT_GCS}trip_summary/trip_speeds_{analysis_date}.parquet"
-    )
-    
-    speed2 = pd.read_parquet(
-        f"{SEGMENT_GCS}trip_summary/trip_speeds_{analysis_date}.parquet")
-
-    # Take the average across route-direction-time_of_day
-    avg_speeds = avg_route_speeds_by_time_of_day(
-        speed2, 
-        group_cols = [
-            "gtfs_dataset_key", "time_of_day",
-            "route_id", "direction_id",
-            "route_name_used",
-            "common_shape_id", "shape_array_key"
-        ]
-    )
-    
-    avg_speeds2 = final_cleaning_for_export(avg_speeds, analysis_date)
-
-    utils.geoparquet_gcs_export(
-        avg_speeds2,
-        f"{SEGMENT_GCS}trip_summary/",
-        f"route_speeds_{analysis_date}"
-    )
-    
-    utils.geoparquet_gcs_export(
-        avg_speeds2,
-        f"{SEGMENT_GCS}export/",
-        "speeds_by_route_time_of_day"
-    )
-    
-    time3 = datetime.datetime.now()
-    print(f"route-direction average speeds: {time3 - time2}")
-    
-    end = datetime.datetime.now()
-    print(f"execution time: {end - start}")
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/Makefile b/rt_segment_speeds/scripts/Makefile
index 8f79d19d8..9c8d4d87b 100644
--- a/rt_segment_speeds/scripts/Makefile
+++ b/rt_segment_speeds/scripts/Makefile
@@ -5,28 +5,18 @@ segmentize:
 	python cut_special_stop_segments.py
 	python concatenate_stop_segments.py
 	#python cut_road_segments.py #cut once creates files needed to run in concat_road_segments
-	python concatenate_road_segments.py
-    
-   
-speeds_pipeline:
-	python A1_sjoin_vp_segments.py
-	python A2_sjoin_postprocessing.py
-	python A3_valid_vehicle_positions.py
-	python B1_speeds_by_segment_trip.py
-	python B2_avg_speeds_by_segment.py
-	python B3_export.py
-	python C2_triangulate_vp.py      
-	python C3_trip_route_speed.py  
+	python concatenate_road_segments.py 
 
-new_pipeline:
-	python prep_stop_segments.py
+
+speeds_pipeline:
 	python shapely_project_vp.py
 	python nearest_vp_to_stop.py
 	python interpolate_stop_arrival.py
 	python stop_arrivals_to_speed.py
-	#python handle_common_errors.py
+	python handle_common_errors.py
+	python avg_speeds_by_segment.py  
+	python export.py
     
-
     
 download_roads:
 	#pip install esridump
diff --git a/rt_segment_speeds/scripts/avg_speeds_by_segment.py b/rt_segment_speeds/scripts/avg_speeds_by_segment.py
new file mode 100644
index 000000000..8dba1e57c
--- /dev/null
+++ b/rt_segment_speeds/scripts/avg_speeds_by_segment.py
@@ -0,0 +1,260 @@
+"""
+Quick aggregation for speed metrics by segment
+"""
+import datetime
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import sys
+
+from loguru import logger
+
+from segment_speed_utils import helpers, sched_rt_utils
+from segment_speed_utils.project_vars import SEGMENT_GCS, CONFIG_PATH
+from calitp_data_analysis import utils, geography_utils
+from shared_utils import portfolio_utils, rt_utils
+
+
+def calculate_avg_speeds(
+    df: pd.DataFrame,
+    group_cols: list
+) -> pd.DataFrame:
+    """
+    Calculate the median, 20th, and 80th percentile speeds 
+    by groups.
+    """
+    # pd.groupby and pd.quantile is so slow
+    # create our own list of speeds and use np
+    df2 = (df.groupby(group_cols, 
+                      observed=True, group_keys=False)
+           .agg({"speed_mph": lambda x: sorted(list(x))})
+           .reset_index()
+           .rename(columns = {"speed_mph": "speed_mph_list"})
+    )
+                        
+    df2 = df2.assign(
+        p50_mph = df2.apply(lambda x: np.percentile(x.speed_mph_list, 0.5), axis=1),
+        n_trips = df2.apply(lambda x: len(x.speed_mph_list), axis=1).astype("int"),
+        p20_mph = df2.apply(lambda x: np.percentile(x.speed_mph_list, 0.2), axis=1),
+        p80_mph = df2.apply(lambda x: np.percentile(x.speed_mph_list, 0.8), axis=1),
+    )
+    
+    stats = df2.drop(columns = "speed_mph_list")
+    
+    # Clean up for map
+    speed_cols = [c for c in stats.columns if "_mph" in c]
+    stats[speed_cols] = stats[speed_cols].round(2)
+    
+    return stats
+    
+    
+def speeds_with_segment_geom(
+    analysis_date: str, 
+    dict_inputs: dict = {},
+): 
+    """
+    Import the segment-trip table. 
+    Average the speed_mph across all trips present in the segment.
+    """
+    start = datetime.datetime.now()
+    
+    SEGMENT_FILE = f'{dict_inputs["segments_file"]}_{analysis_date}'
+    SEGMENT_IDENTIFIER_COLS = dict_inputs["segment_identifier_cols"]
+    SPEEDS_FILE = f'{dict_inputs["stage4"]}_{analysis_date}'
+    EXPORT_FILE = f'{dict_inputs["stage5"]}_{analysis_date}'
+    MAX_SPEED = dict_inputs["max_speed"]
+    
+    # Read in speeds and attach time-of-day
+    df = pd.read_parquet(
+        f"{SEGMENT_GCS}{SPEEDS_FILE}.parquet", 
+        filters = [[("speed_mph", "<=", MAX_SPEED)]]
+    )
+    
+    time_of_day_df = sched_rt_utils.get_trip_time_buckets(analysis_date)
+
+    df2 = pd.merge(
+        df,
+        time_of_day_df,
+        on = "trip_instance_key",
+        how = "inner"
+    )
+    
+    subset_shape_keys = df2.shape_array_key.unique().tolist()
+    
+    # Load in segment geometry, keep shapes present in speeds  
+    segments = gpd.read_parquet(
+        f"{SEGMENT_GCS}{SEGMENT_FILE}.parquet",
+        columns = SEGMENT_IDENTIFIER_COLS + [
+            "schedule_gtfs_dataset_key", 
+            "stop_id",
+            "loop_or_inlining",
+            "geometry", 
+            "district_name"        
+        ],
+        filters = [[("shape_array_key", "in", subset_shape_keys)]]
+    ).to_crs(geography_utils.WGS84)
+    
+    all_day = calculate_avg_speeds(
+        df2,
+        SEGMENT_IDENTIFIER_COLS,
+    )
+                              
+    peak = calculate_avg_speeds(
+        df2[df2.time_of_day.isin(["AM Peak", "PM Peak"])],
+        SEGMENT_IDENTIFIER_COLS,
+    )
+
+    stats = pd.concat([
+        all_day.assign(time_of_day = "all_day"),
+        peak.assign(time_of_day = "peak")
+    ], axis=0)
+    
+    
+    # Merge in segment geometry    
+    gdf = pd.merge(
+        segments,
+        stats,
+        on = SEGMENT_IDENTIFIER_COLS,
+        how = "left"
+    ).sort_values(
+        SEGMENT_IDENTIFIER_COLS + ["time_of_day"]
+    ).reset_index(drop=True)
+    
+    utils.geoparquet_gcs_export(
+        gdf,
+        SEGMENT_GCS,
+        EXPORT_FILE
+    )
+    
+    end = datetime.datetime.now()
+    logger.info(f"segment averages execution time: {end - start}")
+
+    return
+
+
+def add_scheduled_trip_columns(
+    rt_trips: pd.DataFrame,
+    analysis_date: str,
+    group_cols: list = ["trip_instance_key"]) -> pd.DataFrame:
+    """
+    Merge RT trips (vehicle positions) to scheduled trips.
+    Add in the needed scheduled trip columns to take 
+    route-direction-time_of_day averages.
+    """
+    keep_cols = [
+        "gtfs_dataset_key",
+        "direction_id", 
+        "route_id", "route_short_name", "route_long_name", "route_desc",
+    ] + group_cols
+        
+    crosswalk = helpers.import_scheduled_trips(
+        analysis_date, 
+        columns = keep_cols, 
+        get_pandas = True
+    )
+    
+    common_shape = sched_rt_utils.most_common_shape_by_route_direction(analysis_date)
+    
+    crosswalk2 = pd.merge(
+        crosswalk,
+        common_shape,
+        on = ["schedule_gtfs_dataset_key", "route_id", "direction_id"],
+        how = "inner"
+    ).astype({"direction_id": "Int64"})
+    
+    time_of_day = sched_rt_utils.get_trip_time_buckets(analysis_date)
+    
+    # Clean up route name
+    crosswalk2 = portfolio_utils.add_route_name(
+        crosswalk2
+    ).drop(columns = ["route_short_name", "route_long_name", "route_desc"])
+
+    df = pd.merge(
+        rt_trips,
+        crosswalk2,
+        on = group_cols,
+        how = "left",
+    ).merge(
+        time_of_day,
+        on = group_cols,
+        how = "left"
+    )
+    
+    return df
+
+
+def avg_trip_speeds_with_time_of_day(
+    analysis_date: str,
+    dict_inputs: dict,
+) -> pd.DataFrame:
+    """    
+    Get trip-level speeds, scheduled trip service_minutes
+    and rt trip approximated-service_minutes.
+    """
+    start = datetime.datetime.now()
+    
+    SPEEDS_FILE = f'{dict_inputs["stage4"]}_{analysis_date}'
+    EXPORT_FILE = f'{dict_inputs["stage6"]}_{analysis_date}'
+    MAX_SPEED = dict_inputs["max_speed"]
+    
+    by_segment = pd.read_parquet(
+        f"{SEGMENT_GCS}{SPEEDS_FILE}.parquet",
+        columns = ["trip_instance_key", "meters_elapsed", "sec_elapsed"]
+    )
+    
+    by_trip = (by_segment.groupby("trip_instance_key")
+           .agg({
+               "meters_elapsed": "sum",
+                "sec_elapsed": "sum"
+                })
+           .reset_index()
+          )
+    
+    by_trip = by_trip.assign(
+        speed_mph = (by_trip.meters_elapsed.divide(by_trip.sec_elapsed)
+                    ) * rt_utils.MPH_PER_MPS,
+        rt_trip_min = by_trip.sec_elapsed.divide(60)
+    ).query('speed_mph <= @MAX_SPEED')
+    
+    
+    df = add_scheduled_trip_columns(
+        by_trip,
+        analysis_date,
+        group_cols = ["trip_instance_key"]        
+    )
+    
+    df.to_parquet(
+        f"{SEGMENT_GCS}trip_summary/{EXPORT_FILE}.parquet"
+    )
+    
+    end = datetime.datetime.now()
+    logger.info(f"trip summary execution time: {end - start}")
+
+    return 
+
+
+if __name__ == "__main__":
+    
+    from segment_speed_utils.project_vars import analysis_date_list
+    
+    LOG_FILE = "../logs/avg_speeds.log"
+    logger.add(LOG_FILE, retention="3 months")
+    logger.add(sys.stderr, 
+               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
+               level="INFO")
+    
+    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
+        
+    for analysis_date in analysis_date_list:
+        logger.info(f"Analysis date: {analysis_date}")
+        
+        speeds_with_segment_geom(
+            analysis_date, 
+            STOP_SEG_DICT
+        )
+        
+        avg_trip_speeds_with_time_of_day(
+            analysis_date, 
+            STOP_SEG_DICT
+        )
+    
diff --git a/rt_segment_speeds/scripts/concatenate_stop_segments.py b/rt_segment_speeds/scripts/concatenate_stop_segments.py
index 3cd2a883f..6f46a32c9 100644
--- a/rt_segment_speeds/scripts/concatenate_stop_segments.py
+++ b/rt_segment_speeds/scripts/concatenate_stop_segments.py
@@ -96,11 +96,8 @@ def spatial_join_to_caltrans_districts(
     
     gdf2 = spatial_join_to_caltrans_districts(gdf)
     
-    arrowized_segments = wrangle_shapes.add_arrowized_geometry(
-        gdf2)
-    
     utils.geoparquet_gcs_export(
-        arrowized_segments, 
+        gdf2, 
         SEGMENT_GCS,
         f"{EXPORT_FILE}_{analysis_date}"
     )
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/config.yml b/rt_segment_speeds/scripts/config.yml
index ac1d96bdb..022e28e65 100644
--- a/rt_segment_speeds/scripts/config.yml
+++ b/rt_segment_speeds/scripts/config.yml
@@ -2,11 +2,16 @@ stop_segments:
     stage1: "vp_usable"
     stage2: "nearest_vp"
     stage3: "stop_arrivals"
-    stage4: "speed_stop_segments"
+    stage4: "speeds_stop_segments"
+    stage5: "avg_speeds_stop_segments"
+    stage6: "trip_speeds"
+    stage7: "route_speeds"
+    segments_file: "stop_segments"    
     segment_identifier_cols: ["shape_array_key", "stop_sequence"]
     timestamp_col: "location_timestamp_local"
     time_min_cutoff: 10
     pct_segment_minimum: 0.3
+    max_speed: 80
 road_segments:
     stage1: "vp_usable"
     stage2: "vp_road_segment"
diff --git a/rt_segment_speeds/scripts/cut_road_segments.py b/rt_segment_speeds/scripts/cut_road_segments.py
index 443907456..6d585e856 100644
--- a/rt_segment_speeds/scripts/cut_road_segments.py
+++ b/rt_segment_speeds/scripts/cut_road_segments.py
@@ -87,6 +87,7 @@ def load_roads(filtering: tuple) -> gpd.GeoDataFrame:
     
     return df2 
 
+
 """
 Primary/Secondary Roads 
 """
@@ -246,7 +247,7 @@ def add_segment_direction(df: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
             rt_utils.primary_cardinal_direction(x.origin, x.destination),
             axis=1,
         )
-    ).drop(columns = ["origin", "destination"])
+    )
 
     return df
  
@@ -267,7 +268,7 @@ def add_segment_direction(df: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
 
     roads = load_roads(filtering = [("MTFCC", "in", road_type_values)])
     primary_secondary_roads = cut_primary_secondary_roads(
-        roads, ROAD_SEGMENT_METERS).drop(columns = "road_length")
+        roads, ROAD_SEGMENT_METERS)
     
     utils.geoparquet_gcs_export(
         primary_secondary_roads,
diff --git a/rt_segment_speeds/scripts/export.py b/rt_segment_speeds/scripts/export.py
new file mode 100644
index 000000000..4c50d6c11
--- /dev/null
+++ b/rt_segment_speeds/scripts/export.py
@@ -0,0 +1,275 @@
+"""
+Attach columns needed for publishing to open data portal.
+Suppress certain rows and columns too.
+"""
+import datetime
+import geopandas as gpd
+import pandas as pd
+
+from shared_utils import schedule_rt_utils, utils
+from calitp_data_analysis import utils, geography_utils
+from segment_speed_utils import helpers
+from segment_speed_utils.project_vars import SEGMENT_GCS, CONFIG_PATH
+
+def get_operator_natural_identifiers(
+    df: pd.DataFrame, 
+    analysis_date: str
+) -> pd.DataFrame:
+    """
+    For each gtfs_dataset_key-shape_array_key combination,
+    re-attach the natural identifiers and organizational identifiers.
+    Return a df that should be merged against speeds_df.
+    """
+    operator_shape_df = (df[["schedule_gtfs_dataset_key", "shape_array_key"]]
+                         .drop_duplicates()
+                         .reset_index(drop=True)
+                         .rename(columns = {
+                             "schedule_gtfs_dataset_key": "gtfs_dataset_key"})
+                        )
+    
+    # Get shape_id back
+    shape_identifiers = helpers.import_scheduled_trips(
+        analysis_date,
+        columns = ["shape_array_key", "shape_id"],
+        get_pandas = True
+    )
+    
+    df_with_shape = pd.merge(
+        operator_shape_df,
+        shape_identifiers,
+        on = "shape_array_key",
+        how = "inner"
+    )
+    
+    # Get base64_url, uri, organization_source_record_id and organization_name
+    crosswalk = schedule_rt_utils.sample_gtfs_dataset_key_to_organization_crosswalk(
+        df_with_shape,
+        analysis_date,
+        quartet_data = "schedule",
+        dim_gtfs_dataset_cols = [
+            "key",
+            "base64_url",
+        ],
+        dim_organization_cols = ["source_record_id", "name"]
+    )
+
+    df_with_org = pd.merge(
+        df_with_shape.rename(
+            columns = {"gtfs_dataset_key": "schedule_gtfs_dataset_key"}),
+        crosswalk,
+        on = "schedule_gtfs_dataset_key",
+        how = "inner"
+    )
+    
+    return df_with_org
+
+
+def finalize_df_for_export(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
+    """
+    Sorting, suppressing columns not needed in export.
+    """
+
+    RENAME_DICT = {
+        "organization_source_record_id": "org_id",
+        "organization_name": "agency",
+    }
+    
+    gdf2 = (gdf.sort_values(["organization_name", 
+                         "shape_id", "stop_sequence"])
+           .reset_index(drop=True)
+           .rename(columns = RENAME_DICT) 
+          )
+    
+    return gdf2
+
+
+def export_average_speeds(
+    analysis_date: str, 
+    dict_inputs: dict
+):
+    start = datetime.datetime.now()
+    INPUT_FILE = f'{dict_inputs["stage5"]}_{analysis_date}'
+    
+    gdf = gpd.read_parquet(
+        f"{SEGMENT_GCS}{INPUT_FILE}.parquet"
+    )
+    
+    operator_identifiers = get_operator_natural_identifiers(gdf, analysis_date)
+     
+    time1 = datetime.datetime.now()
+    print(f"get natural identifiers: {time1 - start}")
+    
+    gdf2 = pd.merge(
+        gdf,
+        operator_identifiers,
+        on = ["schedule_gtfs_dataset_key", "shape_array_key"],
+        how = "inner"
+    )
+            
+    final_gdf = finalize_df_for_export(gdf2)
+        
+    time2 = datetime.datetime.now()
+    print(f"finalize: {time2 - time1}")
+    
+    keep_cols = [
+        'org_id', 'agency',
+        'shape_id', 'stop_sequence', 'stop_id', 
+        'geometry',
+        'p50_mph', 'p20_mph', 
+        'p80_mph', 'n_trips', 
+        'time_of_day', 
+        'base64_url',
+        'district_name'
+    ]
+    
+    utils.geoparquet_gcs_export(
+        final_gdf[keep_cols],
+        f"{SEGMENT_GCS}export/",
+        INPUT_FILE
+    )
+    
+    # Keep a tabular version (geom is big to save) for us to compare what's published
+    # and contains columns we use for internal modeling 
+    # (shape_array_key, gtfs_dataset_key, etc)
+    final_gdf.drop(columns = "geometry").to_parquet(
+        f"{SEGMENT_GCS}export/{INPUT_FILE}_tabular.parquet"
+    )
+    
+    utils.geoparquet_gcs_export(
+        final_gdf[keep_cols],
+        f"{SEGMENT_GCS}export/",
+        "speeds_by_stop_segments"
+    )
+    
+    end = datetime.datetime.now()
+    print(f"execution time: {end - start}")
+    
+    return
+
+
+def average_route_speeds_for_export(
+    analysis_date: str,
+    dict_inputs: dict
+) -> gpd.GeoDataFrame: 
+    """
+    Aggregate trip speeds to route-direction.
+    Attach shape geometry to most common shape_id.
+    """
+    SPEEDS_FILE = f'{dict_inputs["stage6"]}_{analysis_date}'
+    EXPORT_FILE = f'{dict_inputs["stage7"]}_{analysis_date}'
+    MAX_SPEED = dict_inputs["max_speed"]
+    
+    df = pd.read_parquet(
+        f"{SEGMENT_GCS}trip_summary/{SPEEDS_FILE}.parquet",
+        filters = [[("speed_mph", "<=", MAX_SPEED)]]
+    )
+    
+    # Aggregate by route-direction
+    route_cols = [
+        "schedule_gtfs_dataset_key", "time_of_day",
+        "route_id", "direction_id",
+        "route_name_used",
+        "common_shape_id", "shape_array_key"
+    ]
+    
+    df2 = (df.groupby(route_cols, 
+                      observed = True, group_keys = False)
+           .agg({
+               "service_minutes": "mean",
+               "rt_trip_min": "mean",
+               "speed_mph": "mean",
+               "trip_instance_key": "count"
+           }).reset_index()
+          )
+
+    df3 = df2.assign(
+        rt_trip_min = df2.rt_trip_min.round(1),
+        service_minutes = df2.service_minutes.round(1),
+        speed_mph = df2.speed_mph.round(1)
+    ).rename(columns = {
+        "service_minutes": "avg_sched_trip_min",
+        "rt_trip_min": "avg_rt_trip_min",
+        "trip_instance_key": "n_trips",
+        "route_name_used": "route_name",
+        "schedule_gtfs_dataset_key": "gtfs_dataset_key"
+    })
+    
+    # Attach org name and source_record_id
+    org_crosswalk = (
+        schedule_rt_utils.sample_gtfs_dataset_key_to_organization_crosswalk(
+            df3,
+            analysis_date,
+            quartet_data = "schedule",
+            dim_gtfs_dataset_cols = ["key", "base64_url"],
+            dim_organization_cols = ["source_record_id", 
+                                     "name", "caltrans_district"])
+    )
+    
+    df_with_org = pd.merge(
+        df3,
+        org_crosswalk.rename(columns = {
+            "schedule_gtfs_dataset_key": "gtfs_dataset_key"}),
+        on = "gtfs_dataset_key",
+        how = "inner"
+    )
+    
+    # Attach shape geometry and make sure it's in WGS84
+    shapes = helpers.import_scheduled_shapes(
+        analysis_date,
+        columns = ["shape_array_key", "geometry"],
+        get_pandas = True,
+        crs = geography_utils.WGS84
+    )
+    
+    df_with_shape = pd.merge(
+        shapes,
+        df_with_org,
+        on = "shape_array_key", # once merged, can drop shape_array_key
+        how = "inner"
+    )
+    
+    agency_cols = ['organization_source_record_id', 'organization_name']
+    route_cols = ['route_id', 'route_name', 
+                  'direction_id', 'common_shape_id']
+
+    col_order = agency_cols + route_cols + [
+        'time_of_day',
+        'speed_mph', 'n_trips', 
+        'avg_sched_trip_min', 'avg_rt_trip_min', 
+        'base64_url', 'caltrans_district',
+        'geometry'
+    ]
+    
+    final_df = df_with_shape.reindex(columns = col_order).rename(
+        columns = {"organization_source_record_id": "org_id",
+                   "organization_name": "agency", 
+                   "caltrans_district": "district_name"
+                  })
+
+    
+    utils.geoparquet_gcs_export(
+        final_df,
+        f"{SEGMENT_GCS}trip_summary/",
+        EXPORT_FILE
+    )
+    
+    utils.geoparquet_gcs_export(
+        final_df,
+        f"{SEGMENT_GCS}export/",
+        "speeds_by_route_time_of_day"
+    )
+    
+    return 
+
+
+if __name__ == "__main__":
+    
+    from segment_speed_utils.project_vars import analysis_date_list
+    
+    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
+    
+    for analysis_date in analysis_date_list:
+        export_average_speeds(analysis_date, STOP_SEG_DICT)
+        average_route_speeds_for_export(analysis_date, STOP_SEG_DICT)
+        
+        
diff --git a/rt_segment_speeds/scripts/interpolate_stop_arrival.py b/rt_segment_speeds/scripts/interpolate_stop_arrival.py
index 4460d8538..9045f54d4 100644
--- a/rt_segment_speeds/scripts/interpolate_stop_arrival.py
+++ b/rt_segment_speeds/scripts/interpolate_stop_arrival.py
@@ -12,7 +12,6 @@
 from segment_speed_utils import helpers
 from segment_speed_utils.project_vars import (SEGMENT_GCS, 
                                               PROJECT_CRS, CONFIG_PATH)
-from shared_utils import rt_dates
 
 
 def attach_vp_shape_meters_with_timestamp(
@@ -82,22 +81,13 @@ def get_stop_arrivals(df: pd.DataFrame) -> pd.DataFrame:
     return df
 
 
-if __name__ == "__main__":
-    
-    LOG_FILE = "../logs/interpolate_stop_arrival.log"
-    logger.add(LOG_FILE, retention="3 months")
-    logger.add(sys.stderr, 
-               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
-               level="INFO")
+def main(
+    analysis_date: str, 
+    dict_inputs: dict
+):
+    NEAREST_VP = f"{dict_inputs['stage2']}_{analysis_date}"
+    STOP_ARRIVALS_FILE = f"{dict_inputs['stage3']}_{analysis_date}"
     
-    analysis_date = rt_dates.DATES["sep2023"]
-    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
-    
-    NEAREST_VP = f"{STOP_SEG_DICT['stage2']}_{analysis_date}"
-    STOP_ARRIVALS_FILE = f"{STOP_SEG_DICT['stage3']}_{analysis_date}"
-    
-    logger.info(f"Analysis date: {analysis_date}")
-
     start = datetime.datetime.now()
     
     vp_pared = pd.read_parquet(
@@ -143,3 +133,22 @@ def get_stop_arrivals(df: pd.DataFrame) -> pd.DataFrame:
     
     end = datetime.datetime.now()
     logger.info(f"execution time: {end - start}")    
+
+    return
+
+
+if __name__ == "__main__":
+    
+    from segment_speed_utils.project_vars import analysis_date_list
+    
+    LOG_FILE = "../logs/interpolate_stop_arrival.log"
+    logger.add(LOG_FILE, retention="3 months")
+    logger.add(sys.stderr, 
+               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
+               level="INFO")
+    
+    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
+    
+    for analysis_date in analysis_date_list:
+        logger.info(f"Analysis date: {analysis_date}")
+        main(analysis_date, STOP_SEG_DICT)
diff --git a/rt_segment_speeds/scripts/loop_utils.py b/rt_segment_speeds/scripts/loop_utils.py
deleted file mode 100644
index d296089bc..000000000
--- a/rt_segment_speeds/scripts/loop_utils.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import dask.dataframe as dd
-import folium
-import geopandas as gpd
-import numpy as np
-import pandas as pd
-
-import prep_stop_segments
-from segment_speed_utils import (gtfs_schedule_wrangling, helpers, 
-                                 wrangle_shapes)
-from segment_speed_utils.project_vars import analysis_date
-
-def grab_loop_trips(analysis_date: str) -> pd.DataFrame:
-    """
-    Use stop_times table to grab the trips that 
-    visit the same stop_id at least twice.
-    """
-    stop_times = helpers.import_scheduled_stop_times(
-        analysis_date, 
-        columns = [
-            "feed_key", "trip_id", 
-            "stop_id", "stop_sequence",
-        ]
-    ).drop_duplicates()
-    
-    stop_visits = (stop_times.groupby(
-                    ["feed_key", "trip_id", "stop_id"])
-                  .agg({"stop_sequence": "count"}) 
-                   #nunique doesn't work in dask
-                  .reset_index()
-                 )
-    
-    loop_trips = (stop_visits[stop_visits.stop_sequence > 1]
-                  [["feed_key", "trip_id"]]
-                  .drop_duplicates()
-                  .reset_index(drop=True)
-                  .compute()
-                 )
-    return loop_trips
-
-
-def grab_loop_shapes(analysis_date: str) -> gpd.GeoDataFrame: 
-    
-    loop_trips = grab_loop_trips(analysis_date)
-    
-    trips_with_geom = gtfs_schedule_wrangling.get_trips_with_geom(
-        analysis_date).compute()
-    
-    loop_trips_with_geom = pd.merge(
-        trips_with_geom,
-        loop_trips,
-        on = ["feed_key", "trip_id"],
-        how = "inner"
-    )
-        
-    return loop_trips_with_geom
-
-
-def assign_visits_to_stop(df: pd.DataFrame):
-    """
-    Groupby shape and stop_id and count how many times it's being visited
-    and which number visit it is.
-    """
-    df = df.assign(
-        num_visits = df.groupby(["shape_array_key", "stop_id"])
-                    .stop_sequence.transform("nunique")
-    )
-    
-    return df
-
-
-def plot_segments_and_stops(
-    segment: gpd.GeoSeries, 
-    stops: gpd.GeoSeries
-):
-    m = segment.explore(tiles="CartoDB Positron", name="segment")
-    m = stops.explore(m=m, name="stops")
-
-    folium.LayerControl().add_to(m)
-    return m
-
-
-def stop_segment_components_to_geoseries(
-    subset_shape_geom_array: np.ndarray,
-    subset_stop_geom_array: np.ndarray = [],
-    crs: str = "EPSG:3310"
-) -> tuple:#[gpd.GeoDataFrame]:
-    """
-    Turn segments and stops into geoseries so we can plot it easily.
-    """
-    stop_segment = wrangle_shapes.array_to_geoseries(
-        subset_shape_geom_array, 
-        geom_type="line",
-        crs=crs
-    )#.to_frame(name="stop_segment")
-    
-    if len(subset_stop_geom_array) > 0:
-        related_stops = wrangle_shapes.array_to_geoseries(
-            subset_stop_geom_array,
-            geom_type="point",
-            crs=crs
-        )#.to_frame(name="surrounding_stops_geom")
-
-        return stop_segment, related_stops
-    else:
-        return stop_segment
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/nearest_vp_to_stop.py b/rt_segment_speeds/scripts/nearest_vp_to_stop.py
index 847f6db30..a6b52ee16 100644
--- a/rt_segment_speeds/scripts/nearest_vp_to_stop.py
+++ b/rt_segment_speeds/scripts/nearest_vp_to_stop.py
@@ -16,7 +16,6 @@
 from segment_speed_utils import helpers, segment_calcs, wrangle_shapes
 from segment_speed_utils.project_vars import (SEGMENT_GCS, 
                                               PROJECT_CRS, CONFIG_PATH)
-from shared_utils import rt_dates
 
 
 def rt_trips_to_shape(analysis_date: str) -> pd.DataFrame:
@@ -320,25 +319,23 @@ def find_nearest_vp_to_stop(
     fixed_results.to_parquet(
         f"{SEGMENT_GCS}projection/{EXPORT_FILE}.parquet")
     
-
+    end = datetime.datetime.now()
+    logger.info(f"execution time: {end - start}")
 
 
 if __name__ == "__main__":
     
+    from segment_speed_utils.project_vars import analysis_date_list
+    
     LOG_FILE = "../logs/nearest_vp.log"
     logger.add(LOG_FILE, retention="3 months")
     logger.add(sys.stderr, 
                format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
                level="INFO")
     
-    analysis_date = rt_dates.DATES["sep2023"]
     STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
     
-    logger.info(f"Analysis date: {analysis_date}")
+    for analysis_date in analysis_date_list:
+        logger.info(f"Analysis date: {analysis_date}")
     
-    start = datetime.datetime.now()
-    
-    find_nearest_vp_to_stop(analysis_date, STOP_SEG_DICT)
-   
-    end = datetime.datetime.now()
-    logger.info(f"execution time: {end - start}")
+        find_nearest_vp_to_stop(analysis_date, STOP_SEG_DICT)
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/prep_stop_segments.py b/rt_segment_speeds/scripts/prep_stop_segments.py
index 0f3b739e4..d71198b9f 100644
--- a/rt_segment_speeds/scripts/prep_stop_segments.py
+++ b/rt_segment_speeds/scripts/prep_stop_segments.py
@@ -231,7 +231,7 @@ def prep_stop_segments(analysis_date: str) -> gpd.GeoDataFrame:
             1 if x.shape_array_key in loopy_inlining
             else 0, axis=1, 
         ).astype("int8")
-    )
+    ).sort_values(["shape_array_key", "stop_sequence"]).reset_index(drop=True)
     
     return stop_times_with_geom2
 
diff --git a/rt_segment_speeds/scripts/shapely_project_vp.py b/rt_segment_speeds/scripts/shapely_project_vp.py
index 5539fb31c..260b9e9a9 100644
--- a/rt_segment_speeds/scripts/shapely_project_vp.py
+++ b/rt_segment_speeds/scripts/shapely_project_vp.py
@@ -11,7 +11,6 @@
 from loguru import logger
 
 from calitp_data_analysis.geography_utils import WGS84
-from shared_utils import rt_dates
 from segment_speed_utils import helpers
 from segment_speed_utils.project_vars import (SEGMENT_GCS,
                                               PROJECT_CRS, CONFIG_PATH)
@@ -47,14 +46,13 @@ def project_vp_to_shape(
     return vp_projected_result
 
 
-if __name__ == "__main__":
-    
-    analysis_date = rt_dates.DATES["sep2023"]
-    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
-    
+def project_usable_vp_one_day(
+    analysis_date: str, 
+    dict_inputs: dict = {}
+):
     start = datetime.datetime.now()
     
-    USABLE_VP = f'{STOP_SEG_DICT["stage1"]}_{analysis_date}'
+    USABLE_VP = f'{dict_inputs["stage1"]}_{analysis_date}'
     
     trips = helpers.import_scheduled_trips(
         analysis_date,
@@ -104,4 +102,27 @@ def project_vp_to_shape(
         f"{SEGMENT_GCS}projection/vp_projected_{analysis_date}.parquet")
 
     end = datetime.datetime.now()
-    logger.info(f"compute and export: {end - time1}")
\ No newline at end of file
+    logger.info(f"compute and export: {end - time1}")
+    logger.info(f"execution time: {end - start}")
+    
+    return
+
+
+if __name__ == "__main__":
+    
+    from segment_speed_utils.project_vars import analysis_date_list
+    
+    LOG_FILE = "../logs/shapely_project_vp.log"
+    logger.add(LOG_FILE, retention="3 months")
+    logger.add(sys.stderr, 
+               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
+               level="INFO")
+        
+        
+    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
+    
+    for analysis_date in analysis_date_list:
+        logger.info(f"Analysis date: {analysis_date}")
+        
+        project_usable_vp_one_day(analysis_date, STOP_SEG_DICT)
+     
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/shapes_roads_crosswalk.py b/rt_segment_speeds/scripts/shapes_roads_crosswalk.py
new file mode 100644
index 000000000..cf3c3b1b0
--- /dev/null
+++ b/rt_segment_speeds/scripts/shapes_roads_crosswalk.py
@@ -0,0 +1,101 @@
+"""
+"""
+import dask.dataframe as dd
+import dask_geopandas as dg
+import datetime
+import geopandas as gpd
+import pandas as pd
+import sys
+
+from loguru import logger
+
+from segment_speed_utils import helpers
+from segment_speed_utils.project_vars import (SEGMENT_GCS, 
+                                              CONFIG_PATH, PROJECT_CRS)
+
+
+def sjoin_shapes_to_roads(
+    road_segments: gpd.GeoDataFrame,
+    shapes: gpd.GeoDataFrame
+) -> pd.DataFrame:
+    
+    keep_cols = ["shape_array_key", "linearid", 
+       "mtfcc", "segment_sequence"]
+    
+    shapes = shapes.assign(
+        geometry = shapes.geometry.buffer(25)
+    )
+    
+    shapes_to_roads = gpd.sjoin(
+        shapes,
+        road_segments,
+        how = "inner",
+        predicate = "intersects"
+    )[keep_cols].drop_duplicates()
+    
+    return shapes_to_roads
+
+    
+def main(analysis_date: str, dict_inputs: dict):
+    
+    start = datetime.datetime.now()
+    
+    shapes = helpers.import_scheduled_shapes(
+        analysis_date,
+        columns = ["shape_array_key", "geometry"],
+        get_pandas = True,
+        crs = PROJECT_CRS
+    ).pipe(
+        helpers.remove_shapes_outside_ca
+    ).drop(columns = "index_right")
+    
+    keep_road_cols = ["linearid", "mtfcc", "segment_sequence"]
+    
+    road_segments = dg.read_parquet(
+        f"{SEGMENT_GCS}road_segments_{analysis_date}",
+        columns = keep_road_cols + ["geometry"]
+    ).repartition(npartitions=5)
+    
+    keep_shape_cols = ["shape_array_key"]
+    
+    shape_cols_dtypes = shapes[keep_shape_cols].dtypes.to_dict()
+    road_cols_dtypes = road_segments[keep_road_cols].dtypes.to_dict()
+    
+    sjoin_results = road_segments.map_partitions(
+        sjoin_shapes_to_roads,
+        shapes,
+        meta = {
+            **shape_cols_dtypes,
+            **road_cols_dtypes,
+        },
+        align_dataframes = False
+    )
+    
+    results = sjoin_results.compute()
+    results.to_parquet(
+        f"{SEGMENT_GCS}shape_road_crosswalk_{analysis_date}.parquet"
+    )
+    
+    end = datetime.datetime.now()
+    logger.info(f"execution time: {end - start}")
+
+
+    
+if __name__ == "__main__":
+    
+    from segment_speed_utils.project_vars import analysis_date_list
+    
+    LOG_FILE = "../logs/sjoin_shapes_roads.log"
+    
+    logger.add(LOG_FILE, retention="3 months")
+    logger.add(sys.stderr, 
+               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
+               level="INFO")
+    
+    ROAD_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "road_segments")
+
+    for analysis_date in analysis_date_list:
+        logger.info(f"Analysis date: {analysis_date}")
+        main(analysis_date, ROAD_SEG_DICT)
+    
+
diff --git a/rt_segment_speeds/scripts/sjoin_vp_roads.py b/rt_segment_speeds/scripts/sjoin_vp_roads.py
deleted file mode 100644
index 5a4cf567d..000000000
--- a/rt_segment_speeds/scripts/sjoin_vp_roads.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import dask.dataframe as dd
-import dask_geopandas as dg
-import datetime
-import geopandas as gpd
-import numpy as np
-import pandas as pd
-import sys
-
-from loguru import logger
-
-import A1_sjoin_vp_segments as A1
-from calitp_data_analysis.geography_utils import WGS84
-from segment_speed_utils import helpers
-from segment_speed_utils.project_vars import (analysis_date, SEGMENT_GCS, 
-                                              CONFIG_PATH, PROJECT_CRS)
-
-
-
-def single_direction_spatial_join(
-    vp: dd.DataFrame, 
-    segments: gpd.GeoDataFrame, 
-    segment_identifer_cols: list,
-    direction: str
-) -> dd.DataFrame:
-    """
-    Merge all the segments for a shape for that trip,
-    and check if vp is within.
-    Use map partitions, which treats each partition as df or gdf.
-    """    
-    vp_gdf = gpd.GeoDataFrame(
-        vp,
-        geometry = gpd.points_from_xy(vp.x, vp.y, crs=WGS84)
-    ).to_crs(PROJECT_CRS).drop(columns = ["x", "y"])
-    
-    vp_to_seg = gpd.sjoin(
-        vp_gdf,
-        segments,
-        how = "inner",
-        predicate = "within"
-    )[["vp_idx"] + segment_identifer_cols]
-
-    results = (vp_to_seg
-               .drop_duplicates()
-               .reset_index(drop=True)
-              )
-    
-    return results
-
-
-def stage_direction_results(
-    vp: dd.DataFrame, 
-    segments: gpd.GeoDataFrame, 
-    segment_identifier_cols: list,
-    direction: str
-):
-    keep_vp = [direction, "Unknown"]
-    
-    vp_subset = vp[vp.vp_primary_direction.isin(keep_vp)].repartition(npartitions=20)
-    segments_subset = segments[
-        segments.primary_direction==direction].reset_index(drop=True)
-    
-    seg_id_dtypes = segments[segment_identifier_cols].dtypes.to_dict()
-
-    results_subset = vp_subset.map_partitions(
-        single_direction_spatial_join,
-        segments_subset,
-        segment_identifier_cols,
-        direction,
-        meta = {"vp_idx": "int64", 
-               **seg_id_dtypes},
-        align_dataframes = False
-    )
-    
-    return results_subset
-
-
-def sjoin_vp_to_segments(
-    analysis_date: str,
-    dict_inputs: dict = {}
-):
-    """
-    Spatial join vehicle positions to segments.
-    Subset by grouping columns.
-    
-    Vehicle positions can only join to the relevant segments.
-    Use route_dir_identifier or shape_array_key to figure out 
-    the relevant segments those vp can be joined to.
-    """
-    INPUT_FILE = dict_inputs["stage1"]
-    SEGMENT_FILE = dict_inputs["segments_file"]
-    SEGMENT_IDENTIFIER_COLS = dict_inputs["segment_identifier_cols"]
-    EXPORT_FILE = dict_inputs["stage2"]
-    
-    BUFFER_METERS = 35
-    
-    time0 = datetime.datetime.now()
-    
-    # Import vp, keep trips that are usable
-    vp = dd.read_parquet(
-        f"{SEGMENT_GCS}{INPUT_FILE}_{analysis_date}/",
-        columns = ["vp_idx", "x", "y", "vp_primary_direction"],
-    ).repartition(npartitions=100)
-    
-    segments = A1.import_segments_and_buffer(
-        f"{SEGMENT_FILE}_{analysis_date}",
-        BUFFER_METERS,
-        SEGMENT_IDENTIFIER_COLS,
-    )
-    
-    time1 = datetime.datetime.now()
-    logger.info(f"import vp and segments: {time1 - time0}")
-    
-    all_directions = ["Northbound", "Southbound", "Eastbound", "Westbound"]
-    
-    results = [
-        stage_direction_results(
-            vp,
-            segments,
-            SEGMENT_IDENTIFIER_COLS, 
-            one_direction
-        ) for one_direction in all_directions
-    ]
-    
-    time2 = datetime.datetime.now()
-    logger.info(f"sjoin with map_partitions: {time2 - time1}")
-    
-    full_results = dd.multi.concat(results, axis=0).reset_index(drop=True)
-    full_results = full_results.repartition(npartitions=4)
-    
-    full_results.to_parquet(
-        f"{SEGMENT_GCS}vp_sjoin/{EXPORT_FILE}_{analysis_date}",
-        overwrite = True
-    )
-    
-    time3 = datetime.datetime.now()
-    logger.info(f"export partitioned results: {time3 - time2}")
-    
-    
-if __name__ == "__main__":
-    LOG_FILE = "../logs/sjoin_vp_segments.log"
-    logger.add(LOG_FILE, retention="3 months")
-    logger.add(sys.stderr, 
-               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
-               level="INFO")
-    
-    logger.info(f"Analysis date: {analysis_date}")
-    
-    ROAD_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "road_segments")
-
-    sjoin_vp_to_segments(analysis_date, ROAD_SEG_DICT)
\ No newline at end of file
diff --git a/rt_segment_speeds/scripts/stop_arrivals_to_speed.py b/rt_segment_speeds/scripts/stop_arrivals_to_speed.py
index be9b0e677..46bca3828 100644
--- a/rt_segment_speeds/scripts/stop_arrivals_to_speed.py
+++ b/rt_segment_speeds/scripts/stop_arrivals_to_speed.py
@@ -7,26 +7,16 @@
 
 from loguru import logger
 
-from shared_utils import rt_dates
 from segment_speed_utils import helpers, segment_calcs
 from segment_speed_utils.project_vars import SEGMENT_GCS, CONFIG_PATH
 
-if __name__ == "__main__":
-    
-    LOG_FILE = "../logs/speeds_by_segment_trip.log"
-    logger.add(LOG_FILE, retention="3 months")
-    logger.add(sys.stderr, 
-               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
-               level="INFO")
+def calculate_speed_from_stop_arrivals(
+    analysis_date: str, 
+    dict_inputs: dict
+):
     
-    analysis_date = rt_dates.DATES["sep2023"]
-    logger.info(f"Analysis date: {analysis_date}")
-    
-    
-    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
-    
-    STOP_ARRIVALS_FILE = f"{STOP_SEG_DICT['stage3']}_{analysis_date}"
-    SPEED_FILE = f"{STOP_SEG_DICT['stage4']}_{analysis_date}"
+    STOP_ARRIVALS_FILE = f"{dict_inputs['stage3']}_{analysis_date}"
+    SPEED_FILE = f"{dict_inputs['stage4']}_{analysis_date}"
         
     start = datetime.datetime.now()
     
@@ -66,4 +56,24 @@
         f"{SEGMENT_GCS}{SPEED_FILE}.parquet")
     
     end = datetime.datetime.now()
-    logger.info(f"execution time: {end - start}")
\ No newline at end of file
+    logger.info(f"execution time: {end - start}")
+
+    return
+
+
+if __name__ == "__main__":
+    
+    from segment_speed_utils.project_vars import analysis_date_list
+    
+    LOG_FILE = "../logs/speeds_by_segment_trip.log"
+    logger.add(LOG_FILE, retention="3 months")
+    logger.add(sys.stderr, 
+               format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", 
+               level="INFO")
+        
+    STOP_SEG_DICT = helpers.get_parameters(CONFIG_PATH, "stop_segments")
+    
+    for analysis_date in analysis_date_list:
+        logger.info(f"Analysis date: {analysis_date}")
+        
+        calculate_speed_from_stop_arrivals(analysis_date, STOP_SEG_DICT)
diff --git a/rt_segment_speeds/segment_speed_utils/gtfs_schedule_wrangling.py b/rt_segment_speeds/segment_speed_utils/gtfs_schedule_wrangling.py
index f46123b27..dd51bb0be 100644
--- a/rt_segment_speeds/segment_speed_utils/gtfs_schedule_wrangling.py
+++ b/rt_segment_speeds/segment_speed_utils/gtfs_schedule_wrangling.py
@@ -68,29 +68,6 @@ def get_trips_with_geom(
     return trips_with_geom
 
 
-def merge_shapes_to_stop_times(
-    trips_with_shape_geom: Union[dg.GeoDataFrame, gpd.GeoDataFrame],
-    stop_times: dd.DataFrame,
-) -> dg.GeoDataFrame:
-    """
-    Merge stop_times with trips (with shape_geom) attached.
-    """    
-    st_with_shape = dd.merge(
-        trips_with_shape_geom,
-        stop_times, 
-        on = ["feed_key", "trip_id"],
-        how = "inner",
-    ).drop_duplicates()
-    
-    if isinstance(trips_with_shape_geom, (gpd.GeoDataFrame, dg.GeoDataFrame)):
-        geometry_col = trips_with_shape_geom.geometry.name
-        # Sometimes, geometry is lost...need to set it so it remains dg.GeoDataFrame
-        st_with_shape = st_with_shape.set_geometry(geometry_col)
-        st_with_shape = st_with_shape.set_crs(trips_with_shape_geom.crs)
-    
-    return st_with_shape
-
-
 def stop_arrivals_per_stop(
     stop_times: pd.DataFrame,
     group_cols: list,
diff --git a/rt_segment_speeds/segment_speed_utils/project_vars.py b/rt_segment_speeds/segment_speed_utils/project_vars.py
index 792b9ac29..14b26a905 100644
--- a/rt_segment_speeds/segment_speed_utils/project_vars.py
+++ b/rt_segment_speeds/segment_speed_utils/project_vars.py
@@ -9,9 +9,9 @@
 SHARED_GCS = f"{GCS_FILE_PATH}shared_data/"
 
 analysis_date = rt_dates.DATES["oct2023"]
+
 analysis_date_list = [
-    rt_dates.DATES["sep2023"],
-    rt_dates.DATES["oct2023"]
+    rt_dates.DATES["oct2023"] 
 ]
 
 PROJECT_CRS = "EPSG:3310"
diff --git a/rt_segment_speeds/segment_speed_utils/segment_calcs.py b/rt_segment_speeds/segment_speed_utils/segment_calcs.py
index 3dd8b306b..bf36a79d2 100644
--- a/rt_segment_speeds/segment_speed_utils/segment_calcs.py
+++ b/rt_segment_speeds/segment_speed_utils/segment_calcs.py
@@ -8,59 +8,12 @@
 
 from shared_utils import rt_utils
 
-# https://stackoverflow.com/questions/58145700/using-groupby-to-store-value-counts-in-new-column-in-dask-dataframe
-# https://github.com/dask/dask/pull/5327
-def keep_min_max_timestamps_by_segment(
-    vp_to_seg: dd.DataFrame, 
-    segment_identifier_cols: list,
-    timestamp_col: str = "location_timestamp_local"
-) -> dd.DataFrame:
-    """
-    For each segment-trip combination, throw away excess points, just 
-    keep the enter/exit points for the segment.
-    """
-    # a groupby including gtfs_dataset_key explodes the groupers, and 
-    # a lot of NaNs result...why?
-    # gtfs_dataset_key or name sometimes is category dtype...so we must use groupby(observed=True)
-    # shape_array_key will uniquely identify feed_key + shape_id, so should be ok
-    dtypes_map = vp_to_seg[segment_identifier_cols + [timestamp_col]].dtypes.to_dict()
-    
-    # https://stackoverflow.com/questions/52552066/dask-compute-gives-attributeerror-series-object-has-no-attribute-encode    
-    grouped_df = vp_to_seg.groupby(segment_identifier_cols, 
-                                   observed=True, group_keys=False)
-    
-    enter = (grouped_df
-             [timestamp_col].min()
-             .reset_index()
-             # we lose the dtypes for int16 in dask, set it again
-             .astype(dtypes_map)
-            )
-
-    exit = (grouped_df
-            [timestamp_col].max()
-            .reset_index()
-            .astype(dtypes_map)
-           )
-    
-    enter_exit = dd.multi.concat([enter, exit], axis=0)
-    
-    # Merge back in with original to only keep the min/max timestamps
-    # dask can't sort by multiple columns to drop
-    vp_full_info = dd.merge(
-        vp_to_seg,
-        enter_exit,
-        on = segment_identifier_cols + [timestamp_col],
-        how = "inner"
-    ).reset_index(drop=True)
-            
-    return vp_full_info
-    
-
 
 def derive_speed(
     df: pd.DataFrame, 
     distance_cols: tuple = ("prior_shape_meters", "shape_meters"), 
-    time_cols: tuple = ("prior_location_timestamp_local_sec", "location_timestamp_local_sec")
+    time_cols: tuple = ("prior_location_timestamp_local_sec", 
+                        "location_timestamp_local_sec")
 ) -> pd.DataFrame:
     """
     Derive meters and sec elapsed to calculate speed_mph.
@@ -91,58 +44,6 @@ def derive_speed(
     
     return df
 
-
-def calculate_speed_by_segment_trip(
-    df: dd.DataFrame, 
-    segment_identifier_cols: list,
-    timestamp_col: str
-) -> dd.DataFrame:
-    """
-    For each segment-trip pair, calculate find the min/max timestamp
-    and min/max shape_meters. Use that to derive speed column.
-    """ 
-    segment_trip_cols = [
-        "gtfs_dataset_key", "gtfs_dataset_name", 
-        "trip_id", "trip_instance_key", "schedule_gtfs_dataset_key"
-    ] + segment_identifier_cols
-    
-    grouped_df = df.groupby(segment_trip_cols, observed=True, group_keys=False)
-    
-    min_time_dist = (grouped_df
-                     .agg({timestamp_col: "min", 
-                           "shape_meters": "min"})
-                     .reset_index()
-                     .rename(columns = {
-                         timestamp_col: "min_time",
-                         "shape_meters": "min_dist"})
-    )
-    
-    max_time_dist = (grouped_df
-                     .agg({timestamp_col: "max", 
-                           "shape_meters": "max"})
-                     .reset_index()
-                     .rename(columns = {
-                         timestamp_col: "max_time",
-                         "shape_meters": "max_dist"})
-    )
-    
-    segment_trip_agg = pd.merge(
-        min_time_dist,
-        max_time_dist,
-        on = segment_trip_cols, 
-        how = "left"
-    )
-    
-    #segment_trip_agg = dd.from_pandas(segment_trip_agg, npartitions=3)
-    
-    segment_speeds = derive_speed(
-        segment_trip_agg,
-        distance_cols = ("min_dist", "max_dist"),
-        time_cols = ("min_time", "max_time")
-    )
-        
-    return segment_speeds
-
                                    
 def convert_timestamp_to_seconds(
     df: pd.DataFrame, 
diff --git a/rt_segment_speeds/segment_speed_utils/wrangle_shapes.py b/rt_segment_speeds/segment_speed_utils/wrangle_shapes.py
index acc7f1448..8a4c5be1e 100644
--- a/rt_segment_speeds/segment_speed_utils/wrangle_shapes.py
+++ b/rt_segment_speeds/segment_speed_utils/wrangle_shapes.py
@@ -84,31 +84,6 @@ def add_arrowized_geometry(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
     return gdf
 
 
-def project_point_geom_onto_linestring(
-    vp_with_seg_geom: dg.GeoDataFrame,
-    shape_geoseries: str = "segment_geometry",
-    point_geoseries: str = "vp_geometry"
-):
-    """
-    Use shapely.project to turn point coordinates into numeric.
-    The point coordinates will be converted to the distance along the linestring.
-    https://shapely.readthedocs.io/en/stable/manual.html?highlight=project#object.project
-    https://gis.stackexchange.com/questions/306838/snap-points-shapefile-to-line-shapefile-using-shapely
-    
-    From Eric: projecting the stop's point geom onto the shape_id's line geom
-    https://github.com/cal-itp/data-analyses/blob/f4c9c3607069da6ea96e70c485d0ffe1af6d7a47/rt_delay/rt_analysis/rt_parser.py#L102-L103
-    """
-    shape_meters_series = vp_with_seg_geom.apply(
-        lambda x: x[shape_geoseries].project(x[point_geoseries]), 
-        axis=1, 
-    )
-    
-    # To add this as a column to a dask df
-    # https://www.appsloveworld.com/coding/dataframe/6/add-a-dask-array-column-to-a-dask-dataframe
-    
-    return shape_meters_series
-
-
 def array_to_geoseries(
     array: np.ndarray,
     geom_type: Literal["point", "line", "polygon"],
@@ -132,6 +107,7 @@ def array_to_geoseries(
     
     return gdf
 
+
 def get_direction_vector(
     start: shapely.geometry.Point, 
     end: shapely.geometry.Point
@@ -146,6 +122,7 @@ def get_direction_vector(
     """
     return ((end.x - start.x), (end.y - start.y))
 
+
 def distill_array_into_direction_vector(array: np.ndarray) -> tuple:
     """
     Given an array of n items, let's take the start/end of that.
diff --git a/rt_segment_speeds/setup.py b/rt_segment_speeds/setup.py
index e9d338620..41c79e829 100644
--- a/rt_segment_speeds/setup.py
+++ b/rt_segment_speeds/setup.py
@@ -3,7 +3,7 @@
 setup(
     name="segment_speed_utils",
     packages=find_packages(),
-    version="0.2.1",
+    version="1.0.0",
     description="Utility functions for GTFS RT segment speeds",
     author="Cal-ITP",
     license="Apache",