From a2ec530f25023c420b5835a9e1265dfe962ecab9 Mon Sep 17 00:00:00 2001
From: Hayden Atchley <atchley.sha@protonmail.com>
Date: Sat, 17 Feb 2024 16:23:41 -0700
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                                     |   2 +-
 images/tbm.png                                | Bin 0 -> 238872 bytes
 images/tour.png                               | Bin 0 -> 46969 bytes
 images/trip.png                               | Bin 0 -> 53580 bytes
 qmd/1_land_use.html                           |  62 ++---
 .../fig-draper-prison-location-1.png          | Bin 0 -> 98425 bytes
 qmd/2_transit.html                            |  27 +--
 .../figure-html/fig-frontrunner-map-1.png     | Bin 0 -> 293316 bytes
 qmd/3_wfh.html                                | 228 +++++++++++++++---
 .../figure-html/unnamed-chunk-6-1.png         | Bin 149518 -> 144434 bytes
 .../figure-html/unnamed-chunk-7-1.png         | Bin 0 -> 139280 bytes
 .../figure-html/unnamed-chunk-8-1.png         | Bin 0 -> 149518 bytes
 qmd/lit-review.html                           |  75 ++----
 qmd/methods.html                              |  14 +-
 search.json                                   |  18 +-
 15 files changed, 278 insertions(+), 148 deletions(-)
 create mode 100644 images/tbm.png
 create mode 100644 images/tour.png
 create mode 100644 images/trip.png
 create mode 100644 qmd/1_land_use_files/figure-html/fig-draper-prison-location-1.png
 create mode 100644 qmd/2_transit_files/figure-html/fig-frontrunner-map-1.png
 create mode 100644 qmd/3_wfh_files/figure-html/unnamed-chunk-7-1.png
 create mode 100644 qmd/3_wfh_files/figure-html/unnamed-chunk-8-1.png

diff --git a/.nojekyll b/.nojekyll
index fae477a..341a965 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-f34de4d8
\ No newline at end of file
+edbb9788
\ No newline at end of file
diff --git a/images/tbm.png b/images/tbm.png
new file mode 100644
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diff --git a/qmd/1_land_use.html b/qmd/1_land_use.html
index ca62d13..e5d5f82 100644
--- a/qmd/1_land_use.html
+++ b/qmd/1_land_use.html
@@ -205,13 +205,24 @@ <h1 class="title"><span id="sec-landuse" class="quarto-section-identifier"><span
 
 
 <!-- Include at the start of every doc -->
-<p>In our first scenario, we changes the land use at The Point.</p>
-<p>The change is based on WFRC 2050 SE data forecast, in turn based on development plan of The Point.</p>
-<p>This scenario uses the planned residential and commercial development at The Point Utah, the site of the recently demolished Utah State Prison, to model a major increase in land use.</p>
-<p>Near Draper, Utah there is a defunct prison site that is slated for redevelopment (see <span class="quarto-unresolved-ref">?fig-draper-prison-location</span>). The 600-acre site will be developed into a multi-use residential and commercial area, with an emphasis on walkability and transit. A bus rapid transit (BRT) line is planned to run through the development to the Draper FrontRunner station, and across I-15 to Lehi, Utah. This scenario is to model this development and its effect on transportation demand.</p>
-<p>To model this scenario in the trip-based model, the only thing that needed to be done was update the socioeconomic data to reflect the land use changes. Using the information made publicly available by Point of the Mountain State Land Authority <span class="citation" data-cites="state_land_authority_framework_2021">(<a href="references.html#ref-state_land_authority_framework_2021" role="doc-biblioref"><strong>state_land_authority_framework_2021?</strong></a>)</span>, the increase in population and employment was estimated by Wasatch Front Regional Council (WFRC) as shown in <span class="quarto-unresolved-ref">?tbl-point-se-data</span>. The development spans five transportation analysis zone (TAZ)s (see <span class="quarto-unresolved-ref">?fig-prison-new-transit</span>), so the socioeconomic data are spread out across them consistent with the development plan <span class="citation" data-cites="cite">(<a href="references.html#ref-cite" role="doc-biblioref"><strong>cite?</strong></a>)</span>. To add this information to the model, the input file containing socioeconomic data for each zone was adjusted so the five zones in the new development had SE data that matched the 2050 estimates for that area. As seen in <span class="quarto-unresolved-ref">?tbl-poin-se-data</span>, approximately 7,430 new households and 22,200 new employments were added to these zones.</p>
-<p>This adds X jobs and X households, etc. (show map/table)</p>
-<p><a href="#tbl-the-point-overview" class="quarto-xref">Table&nbsp;<span>4.1</span></a> shows the change in lu data, and <a href="#fig-the-point-zones" class="quarto-xref">Figure&nbsp;<span>4.1</span></a> shows these TAZs.</p>
+<p>Near Draper, Utah there is a defunct prison site that is slated for redevelopment (see <a href="#fig-draper-prison-location" class="quarto-xref">Figure&nbsp;<span>4.1</span></a>). The 600-acre site will be developed into a multi-use residential and commercial area, with an emphasis on walkability and transit. This development is known as The Point, and is the basis for the first model scenario in this paper.</p>
+<div class="cell">
+<div class="cell-output-display">
+<div id="fig-draper-prison-location" class="quarto-figure quarto-figure-center quarto-float anchored">
+<figure class="quarto-float quarto-float-fig figure">
+<div aria-describedby="fig-draper-prison-location-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<img src="1_land_use_files/figure-html/fig-draper-prison-location-1.png" class="img-fluid figure-img" width="672">
+</div>
+<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-draper-prison-location-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+Figure&nbsp;4.1: draper prison location
+</figcaption>
+</figure>
+</div>
+</div>
+</div>
+<p>This scenario models the change in land use that a development such as The Point would create. Though the actual development plans for The Point include an expansion of transit services, only the additional households and jobs created from this development are modeled in this scenario. The data for the land use changes comes from the WFRC land use forecast, which is in turn based on projections from the Point of the Mountain State Land Authority <span class="citation" data-cites="state_land_authority_framework_2021">(<a href="references.html#ref-state_land_authority_framework_2021" role="doc-biblioref"><strong>state_land_authority_framework_2021?</strong></a>)</span>. The development is expected to be fully completed by 2050, and so the 2050 WFRC land use and socioeconomic data projections are used for this site.</p>
+<p>The site consists of 5 TAZs, as shown in <a href="#fig-the-point-zones" class="quarto-xref">Figure&nbsp;<span>4.2</span></a>. <a href="#tbl-the-point-data" class="quarto-xref">Table&nbsp;<span>4.1</span></a> shows the additional households, population, and employment by type added to each TAZ in this scenario. <strong><em>Notably, there were no households or jobs in these TAZs in the baseline scenario, so <a href="#tbl-the-point-data" class="quarto-xref">Table&nbsp;<span>4.1</span></a> also represents the total in each category.</em></strong><br>
+No changes other than to the land use/socioeconomic data in these 5 TAZs was made relative to the baseline scenario.</p>
 <div class="cell">
 <div class="cell-output-display">
 <div id="fig-the-point-zones" class="quarto-figure quarto-figure-center quarto-float anchored">
@@ -220,19 +231,19 @@ <h1 class="title"><span id="sec-landuse" class="quarto-section-identifier"><span
 <img src="1_land_use_files/figure-html/fig-the-point-zones-1.png" class="img-fluid figure-img" width="672">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-the-point-zones-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;4.1: the point zones
+Figure&nbsp;4.2: the point zones
 </figcaption>
 </figure>
 </div>
 </div>
 </div>
 <div class="cell">
-<div id="tbl-the-point-overview" class="cell quarto-float anchored">
+<div id="tbl-the-point-data" class="cell quarto-float anchored">
 <figure class="quarto-float quarto-float-tbl figure">
-<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-the-point-overview-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Table&nbsp;4.1: the point overview
+<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-the-point-data-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+Table&nbsp;4.1: the point data
 </figcaption>
-<div aria-describedby="tbl-the-point-overview-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<div aria-describedby="tbl-the-point-data-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <div class="cell-output-display">
 <div>
 <table class="do-not-create-environment cell table table-sm table-striped small" data-quarto-postprocess="true">
@@ -287,19 +298,14 @@ <h1 class="title"><span id="sec-landuse" class="quarto-section-identifier"><span
 </div>
 <section id="scenario-creation" class="level2" data-number="4.1">
 <h2 data-number="4.1" class="anchored" data-anchor-id="scenario-creation"><span class="header-section-number">4.1</span> Scenario Creation</h2>
-<p>In the WFRC model the change is trivial to implement. We just copy/pasted the se data from 2050 to 2019 for the affected zones.</p>
-<p>This zonal data is used directly in the WFRC model, but a new synthetic population is needed for ActivitySim. We used the zonal data as new targets….</p>
-<p>Only created a new synthetic population for the 5 zones</p>
-<p>Used census targets from Gateway area in SLC</p>
-<p>ActivitySim requires two things for this scenario: updated SE data and a new pop. The updated SE data is the same as in the WFRC scenario, so is also trivial to implement. The pop, however, is not. New controls.</p>
-<p>New population for only the affected area, then joined to existing. There were no existing households so we didn’t need to remove any (we’re not double-counting).</p>
-<p>? Note in realistic scenario you could use just the land use forecast as controls directly, rahter than the whole census shenanigains but we didn’t do that.</p>
-<p>This is trivial to implement in the WFRC/MAG model, where all that is needed is a change in the TAZ- level land use data file for the affected TAZs. The new land use data is the WFRC/MAG predicted 2050 data for these zones, as this is when development of the site is scheduled for completion. That is, the 2050 socioeconomic data for these five zones (only) was brought into the 2019 scenario. This added 7,430 new households and 22,200 new jobs to the area.</p>
-<p>In ActivitySim, a modified synthetic population with these new households is needed. Currently, the synthetic population model (PopulationSim) is set up to use Census tract data for many control variables including income. At the suggestion of Bert Granberg, the BYU team used the Census tract covering the Gateway area in SLC to represent the new development patterns at The Point. The income distribution, etc. of this area will therefore match that of the Gateway area, though the number of households and zonal SE data will match the WFRC projections for 2050. The new synthetic population was run for this new development only and added to the base scenario population</p>
+<p>In the WFRC model, this change is trivial to implement. The model uses the land use/socioeconomic data directly, so the only adjustment needed is replacing the data for the specific TAZs with the 2050 data. As noted previously, all other TAZs have the same land use data as in the 2019 baseline scenario.</p>
+<p>ActivitySim requires two changes for this scenario. The first is an update to the TAZ-level land use and socioeconomic data, which is identical to the process for the WFRC model. The second is an updated synthetic population. In order to keep consistency between model scenarios, a new population was created only for the 5 affected TAZs and joined to the existing synthetic population. There were no individuals or households in the affected zones in the existing synthetic population, so no individuals or households needed to be removed before joining the two populations.</p>
+<p>Creating the new synthetic population followed a similar process as in the baseline scenario (<a href="methods.html#sec-populationsim" class="quarto-xref"><span>Section 3.2.1</span></a>), but used the new land use data as new TAZ-level controls. However, many of the controls for PopulationSim use tract-level data from the Census, and existing Census data for these controls is unrepresentative of the new development. Because of this, the Census tract covering the Gateway area in Salt Lake City (see <span class="quarto-unresolved-ref">?fig-gateway-tract</span>) is used to represent the new development patterns at The Point. The income distribution, etc. of this area will therefore match that of the Gateway area, though the TAZ-level controls and land use/socioeconomic data will match the WFRC projections for 2050.</p>
+<p>? Note in realistic scenario you could use just the land use forecast as controls directly, rahter than the whole census shenanigans but we didn’t do that since we wanted the models to be independent.</p>
 </section>
 <section id="scenario-analysis" class="level2" data-number="4.2">
 <h2 data-number="4.2" class="anchored" data-anchor-id="scenario-analysis"><span class="header-section-number">4.2</span> Scenario Analysis</h2>
-<p>We looked at person-miles for both scenarios. <a href="#fig-lu-personmiles-abm" class="quarto-xref">Figure&nbsp;<span>4.2</span></a> shows the increase in person-miles for the ABM. Shaded based on “in-zone”. (Not many transit trips since we didn’t add transit). <a href="#fig-lu-personmiles-tbm" class="quarto-xref">Figure&nbsp;<span>4.3</span></a> shows for TBM.</p>
+<p>We looked at person-miles for both scenarios. <a href="#fig-lu-personmiles-abm" class="quarto-xref">Figure&nbsp;<span>4.3</span></a> shows the increase in person-miles for the ABM. Shaded based on “in-zone”. (Not many transit trips since we didn’t add transit). <a href="#fig-lu-personmiles-tbm" class="quarto-xref">Figure&nbsp;<span>4.4</span></a> shows for TBM.</p>
 <p>In a trip-based model, it is possible to calculate VMT/person-miles traveled/etc. from trips produced in and/or attracted to the new development zones (see Figure 4). Additionally, Figure 5 shows desire lines for trips produced in these zones. However, those living in the new development make many more trips than only those produced in their home zone. Figure 6 shows a desire line plot of all trips taken by the newly-added households in ActivitySim, and Figure 7 shows the distribution of these trips based on their production zone. Though trip-based models do include non–home-based trips, it is difficult to model accurately where and by whom these trips are made. Analyzing these trips and capturing the full impact of new development in this way is only possible in an ABM. Note that in a trip-based model, a round-trip from home to work and back is regarded as two trips produced in the home zone. ActivitySim does not deal with productions and attractions in the same way, so we are counting a trip with an origin or destination in the home zone as produced by that zone e.g.&nbsp;in Figure 6Figures 6 Figure 7and 7.</p>
 <div class="cell">
 <div class="cell-output-display">
@@ -309,7 +315,7 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="scenario-analysis"><span
 <img src="1_land_use_files/figure-html/fig-lu-personmiles-abm-1.png" class="img-fluid figure-img" width="672">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-lu-personmiles-abm-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;4.2: personmiles abm
+Figure&nbsp;4.3: personmiles abm
 </figcaption>
 </figure>
 </div>
@@ -323,13 +329,13 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="scenario-analysis"><span
 <img src="1_land_use_files/figure-html/fig-lu-personmiles-tbm-1.png" class="img-fluid figure-img" width="672">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-lu-personmiles-tbm-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;4.3: personmiles tbm
+Figure&nbsp;4.4: personmiles tbm
 </figcaption>
 </figure>
 </div>
 </div>
 </div>
-<p>Can’t really shade since nhb relocation happens before mode choice. Best you can do is difference the NHB matrices (<a href="#fig-lu-desire-tbm" class="quarto-xref">Figure&nbsp;<span>4.4</span></a> (2-panel)) (If there are nhb trips in/out of zones): clearly some of these are not nhb from residents since they occur in these zones.</p>
+<p>Can’t really shade since nhb relocation happens before mode choice. Best you can do is difference the NHB matrices (<a href="#fig-lu-desire-tbm" class="quarto-xref">Figure&nbsp;<span>4.5</span></a> (2-panel)) (If there are nhb trips in/out of zones): clearly some of these are not nhb from residents since they occur in these zones.</p>
 <p>An output of the trip-based model is a large trip matrix that contains the number of trips that were produced and attracted between each zone. Along with this matrix, there are matrices showing productions and attractions based on each mode and each trip purpose. By selecting just the zones we wanted to analyze, we could see how many trips the new development produced and attracted, and we could see what modes and purposes were connected to those trips. To visualize this, we used the matrix file and the TAZ shape file to create desire lines as seen in <em>(some figure we can add)</em>.</p>
 <p>Implications: To compare this to the base scenario, we simply subtracted the base scenario trip matrix by the trip matrix from the land use scenario to get desire lines showing the increase in trips. As could be reasonably expected, we saw that more trips were produced and attracted by the zones containing the new land use area. We were also able to get an idea of where each of the trips to and from the area were coming from or going to, and what types of modes where being used.</p>
 <p>Talk about the numbers we found and describe the results.</p>
@@ -364,12 +370,12 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="scenario-analysis"><span
 </div>
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-lu-desire-tbm-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;4.4: lu desire tbm
+Figure&nbsp;4.5: lu desire tbm
 </figcaption>
 </figure>
 </div>
 </div>
-<p>With abm, you can follow people and trace their exact paths (<a href="#fig-lu-desire-abm" class="quarto-xref">Figure&nbsp;<span>4.5</span></a>). Implications: Along with seeing where each trip produced in the new development was going, the activity-based model allowed us to see how trips from the development were related to other trips in a travel tour. With the synthetic population, we were also able to see which types of people were taking trips from the new development.</p>
+<p>With abm, you can follow people and trace their exact paths (<a href="#fig-lu-desire-abm" class="quarto-xref">Figure&nbsp;<span>4.6</span></a>). Implications: Along with seeing where each trip produced in the new development was going, the activity-based model allowed us to see how trips from the development were related to other trips in a travel tour. With the synthetic population, we were also able to see which types of people were taking trips from the new development.</p>
 <div class="cell">
 <div class="cell-output-display">
 <div id="fig-lu-desire-abm" class="quarto-figure quarto-figure-center quarto-float anchored">
@@ -378,7 +384,7 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="scenario-analysis"><span
 <img src="1_land_use_files/figure-html/fig-lu-desire-abm-1.png" class="img-fluid figure-img" width="672">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-lu-desire-abm-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;4.5: desire abm
+Figure&nbsp;4.6: desire abm
 </figcaption>
 </figure>
 </div>
diff --git a/qmd/1_land_use_files/figure-html/fig-draper-prison-location-1.png b/qmd/1_land_use_files/figure-html/fig-draper-prison-location-1.png
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diff --git a/qmd/2_transit.html b/qmd/2_transit.html
index 27027e7..979c0e8 100644
--- a/qmd/2_transit.html
+++ b/qmd/2_transit.html
@@ -205,29 +205,28 @@ <h1 class="title"><span id="sec-transit" class="quarto-section-identifier"><span
 
 
 <!-- Include at the start of every doc -->
-<p>The second scenario updates the transit network (frontrunner).</p>
 <p>The FrontRunner is a commuter rail line between Provo and Ogden, Utah, with several stops in-between. Currently, there is only one set of tracks for much of the line, and train crossings are only possible near stations. Because of this, headways are quite large, with trains running every half-hour in peak periods and hourly in off-peak periods. Additionally, trains occasionally need to wait for each other in order to cross paths.</p>
-<p>There is a planned improvement to the FrontRunner that would “double-track” the entire route, allowing trains to pass each other at any point. This would allow for much smaller headways, which are currently planned at 15 and 30 minutes for peak and off-peak periods respectively. The improvement would also partially electrify the FrontRunner, allowing for faster travel speeds, and extend the track farther south with additional stops.</p>
-<p>Data comes from the WFRC 2050 build plan. Includes new stations, new headways, and faster speeds.</p>
-<p>With the proposed double-tracking and potential electric updates to FrontRunner, there is a potential to cut headways in half and increase train speeds. Both the increased speed and shorter headways are modeled in this scenario. The BYU team adjusted the FrontRunner headways from 30 to 15 minutes in peak, and 60 to 30 minutes off-peak. Additionally, the proposed 2050 FrontRunner network is used, which includes higher speeds due in part to electrification of the corridor. This network also adds additional stations in Vineyard, Springville, Spanish Fork, and Payson. No other modifications were made to the Base 2019 scenario; for example, a revised bus service network serving the Springville station is not included.</p>
-<p>Show map of changes.</p>
+<p>There is a planned improvement to the FrontRunner that would “double-track” the entire route, allowing trains to pass each other at any point. This would allow for much smaller headways, which are currently planned at 15 and 30 minutes for peak and off-peak periods respectively (half of the current headways). The improvement would also partially electrify the FrontRunner, allowing for faster travel speeds, and extend the track farther south with additional stops.</p>
+<p>This scenario models these planned improvements to the FrontRunner. The scenario adjusts the headways to 15/30 minutes for peak/off-peak service, increases travel speeds, and adds additional stops in Vineyard, Springville, Spanish Fork, and Payson. <a href="#fig-frontrunner-map" class="quarto-xref">Figure&nbsp;<span>5.1</span></a> shows the FrontRunner network with the modeled changes. No other modifications were made to the baseline scenario; for example, a revised bus service network serving the Springville station is not included.</p>
 <div class="cell">
 <div class="cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="2_transit_files/figure-html/unnamed-chunk-2-1.png" class="img-fluid figure-img" width="672"></p>
+<div id="fig-frontrunner-map" class="quarto-figure quarto-figure-center quarto-float anchored">
+<figure class="quarto-float quarto-float-fig figure">
+<div aria-describedby="fig-frontrunner-map-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<img src="2_transit_files/figure-html/fig-frontrunner-map-1.png" class="img-fluid figure-img" width="672">
+</div>
+<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-frontrunner-map-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+Figure&nbsp;5.1: frontrunner map
+</figcaption>
 </figure>
 </div>
 </div>
 </div>
 <section id="scenario-creation" class="level2" data-number="5.1">
 <h2 data-number="5.1" class="anchored" data-anchor-id="scenario-creation"><span class="header-section-number">5.1</span> Scenario Creation</h2>
-<p>WFRC model needs three adjustments: edit the headways, point the model to the new frontrunner network (which includes speeds), and add park-and-ride option at new station locations in highway? network.</p>
-<p>To model this scenario in the Wasatch Front Regional Council (WFRC) model, we simply needed to add the new stations and adjust the headway and speed inputs. Since transit is a direct input in the WFRC model, we accessed and updated the file that stores the FrontRunner headway and node data. We changed the headways from 60 minutes during off-peak and 30 minutes during peak times to 30 minutes during off-peak and 15 minutes during peak times. In the same file, we added the extra nodes corresponding to the nodes on the extended rail line in the network file. In the network file, each node on the rail line that is a train station is flagged as a place to park and ride, and, because there were new stations added, we had to flag the new stations’ nodes as places to park and ride. The last thing we did was change the posted speed of each link to the speeds that WFRC had predicted for a partially electrified train system. After changing the speeds, headways, and train stations, we ran the model.</p>
-<p>The WFRC/MAG model contains data on transit headways (including FrontRunner). The BYU team will change this data from 30-minute to 15-minute peak headways and 60-minute to 30-minute off-peak headways. To increase the speed, the team will modify the model to use the predicted speeds after electrification instead of the base year speeds. The 2050 transit networks from the WFRC/MAG model appear to have these changes made, and so the team will use these speed values for the FrontRunner. These networks will be paired with the 2019 socioeconomic scenario for comparison with the base scenario.</p>
-<p>The output of the WFRC/MAG model for this scenario will contain new transit network skims. These updated skims will be used in ActivitySim instead of the baseline scenario skims. Because the mode share of transit is relatively low, it is not expected that the highway travel times will be affected very much by this change, and so the baseline highway skims will remain fixed. No other changes to ActivitySim are necessary to model this scenario.</p>
-<p>Asim just needs new skims, gotten from the new WFRC scenario.</p>
-<p>In the WFRC model, this change was relatively easy to implement. The headways are stored in the input data and are easily modified, and the 2050 network is already built in to the model for future-year analysis. Including network assignment is outside the scope of this research, and the BYU implementation of ActivitySim uses the same skim files that the WFRC demand model uses. In this case, the team used the new congested network skims from the WFRC transit scenario to feed the ActivitySim demand model representing this scenario. This is the only change needed for this scenario.</p>
+<p>In the WFRC model, this change is relatively easy to implement. The headways are stored directly in the input data and are easily modified, and a year-2050 network with increased speeds and additional stations is already built into the model for future-year analysis. The only additional change needed was to turn on the “park and ride” flag in the highway network at each new station. Wasatch Front Regional Council (WFRC)</p>
+<!-- The WFRC/MAG model contains data on transit headways (including FrontRunner). The BYU team will change this data from 30-minute to 15-minute peak headways and 60-minute to 30-minute off-peak headways. To increase the speed, the team will modify the model to use the predicted speeds after electrification instead of the base year speeds. The 2050 transit networks from the WFRC/MAG model appear to have these changes made, and so the team will use these speed values for the FrontRunner. These networks will be paired with the 2019 socioeconomic scenario for comparison with the base scenario. -->
+<p>To implement this scenario in ActivitySim, only updated travel skims are needed. As in the baseline scenario, the updated transit skims output from the WFRC model’s network assignment in this model scenario are taken directly as inputs to ActivitySim. Because the mode share of transit is relatively low, it is not expected that the highway travel times will be affected very much by this change, and so the highway skims from the baseline scenario are used directly and not updated for this scenario. No other changes to ActivitySim are necessary to model this scenario. <!-- No other changes are needed to ActivitySim for this model scenario. --></p>
 </section>
 <section id="scenario-analysis" class="level2" data-number="5.2">
 <h2 data-number="5.2" class="anchored" data-anchor-id="scenario-analysis"><span class="header-section-number">5.2</span> Scenario Analysis</h2>
diff --git a/qmd/2_transit_files/figure-html/fig-frontrunner-map-1.png b/qmd/2_transit_files/figure-html/fig-frontrunner-map-1.png
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diff --git a/qmd/3_wfh.html b/qmd/3_wfh.html
index 4944d0d..f6a3f83 100644
--- a/qmd/3_wfh.html
+++ b/qmd/3_wfh.html
@@ -88,6 +88,8 @@
     "search-label": "Search"
   }
 }</script>
+<script src="../site_libs/kePrint-0.0.1/kePrint.js"></script>
+<link href="../site_libs/lightable-0.0.1/lightable.css" rel="stylesheet">
 
 
 </head>
@@ -224,7 +226,7 @@ <h1 class="title"><span id="sec-wfh" class="quarto-section-identifier"><span cla
 
 
 <!-- Include at the start of every doc -->
-<p>With the onset of the COVID 19 pandemic, there were unprecedented numbers of people working from home <span class="citation" data-cites="bick_work_2021">(<a href="references.html#ref-bick_work_2021" role="doc-biblioref">Bick, Blandin, and Mertens 2021</a>)</span>. This scenario is an attempt to model an increase in people working from home, or telecommuting. There has been much research, especially in recent years, on the implications of remote work. While many MPOs have adjusted their models to account for telecommuting, and most models follow similar principles, it is not obvious what the best method is. <span class="citation" data-cites="bramberga_teleworking_2023">Bramberga (<a href="references.html#ref-bramberga_teleworking_2023" role="doc-biblioref">2023</a>)</span> even suggested that considerations for teleworking should be on a case by case basis because there is no single best approach. Before describing how we set up the telecommuting scenario for our research, we will first describe some general considerations we had based on what is in the literature.</p>
+<p>With the onset of the COVID 19 pandemic, there were unprecedented numbers of people working remotely <span class="citation" data-cites="bick_work_2021">(<a href="references.html#ref-bick_work_2021" role="doc-biblioref">Bick, Blandin, and Mertens 2021</a>)</span>. There has been much research, especially in recent years, on the implications of remote work. While many MPOs have adjusted their models to account for remote work, and most models follow similar principles, it is not obvious what the best method is. <span class="citation" data-cites="bramberga_teleworking_2023">Bramberga (<a href="references.html#ref-bramberga_teleworking_2023" role="doc-biblioref">2023</a>)</span> even suggested that considerations for teleworking should be made on a case by case basis because there is no single best approach.</p>
 <section id="considerations-for-modelling-remote-work" class="level2" data-number="6.1">
 <h2 data-number="6.1" class="anchored" data-anchor-id="considerations-for-modelling-remote-work"><span class="header-section-number">6.1</span> Considerations for Modelling Remote Work</h2>
 <p>The first thing to consider is the fact that different job types allow different levels of telecommuting <span class="citation" data-cites="yasenov_who_2020">(<a href="references.html#ref-yasenov_who_2020" role="doc-biblioref">Yasenov 2020</a>)</span>. An office worker using a computer is much more capable to work from home than a construction worker. This fact is something that has been considered for telecommuting in most travel demand models <span class="citation" data-cites="bramberga_teleworking_2023 moeckel_working_2017 sener_copula-based_2011">(<a href="references.html#ref-bramberga_teleworking_2023" role="doc-biblioref">Bramberga 2023</a>; <a href="references.html#ref-moeckel_working_2017" role="doc-biblioref">Moeckel 2017</a>; <a href="references.html#ref-sener_copula-based_2011" role="doc-biblioref">Sener and Bhat 2011</a>)</span>.</p>
@@ -243,30 +245,84 @@ <h2 data-number="6.1" class="anchored" data-anchor-id="considerations-for-modell
 <section id="scenario-creation" class="level2" data-number="6.2">
 <h2 data-number="6.2" class="anchored" data-anchor-id="scenario-creation"><span class="header-section-number">6.2</span> Scenario Creation</h2>
 <p>WFRC needs only to move 2050 data to 2019 for TC% and HBJ.</p>
+<div class="cell">
 <div id="tbl-wfrc-telecommute-years" class="cell quarto-float anchored">
 <figure class="quarto-float quarto-float-tbl figure">
 <figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-wfrc-telecommute-years-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Table&nbsp;6.1: wfrc telecommute data
 </figcaption>
 <div aria-describedby="tbl-wfrc-telecommute-years-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<div class="cell-output cell-output-stdout">
-<pre><code># A tibble: 10 × 3
-   name            wfrc_2019 wfrc_2050
-   &lt;chr&gt;               &lt;dbl&gt;     &lt;dbl&gt;
- 1 Retail             0.027          0
- 2 Food               0.0187        NA
- 3 Manufacturing      0.0202        NA
- 4 Office             0.0666        NA
- 5 Gov't/Education    0.0167        NA
- 6 Health             0.0286        NA
- 7 Agriculture        0.0693        NA
- 8 Mining             0.0053        NA
- 9 Construction       0.0328        NA
-10 Other              0.0537        NA</code></pre>
+<div class="cell-output-display">
+<div>
+<table class="do-not-create-environment cell table table-sm table-striped small" data-quarto-postprocess="true">
+<thead>
+<tr class="header">
+<th style="text-align: left;" data-quarto-table-cell-role="th">name</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">wfrc_2019</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">wfrc_2050</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td style="text-align: left;">Retail</td>
+<td style="text-align: right;">0.027</td>
+<td style="text-align: right;">0</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">Food</td>
+<td style="text-align: right;">0.019</td>
+<td style="text-align: right;">NA</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">Manufacturing</td>
+<td style="text-align: right;">0.020</td>
+<td style="text-align: right;">NA</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">Office</td>
+<td style="text-align: right;">0.067</td>
+<td style="text-align: right;">NA</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">Gov't/Education</td>
+<td style="text-align: right;">0.017</td>
+<td style="text-align: right;">NA</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">Health</td>
+<td style="text-align: right;">0.029</td>
+<td style="text-align: right;">NA</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">Agriculture</td>
+<td style="text-align: right;">0.069</td>
+<td style="text-align: right;">NA</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">Mining</td>
+<td style="text-align: right;">0.005</td>
+<td style="text-align: right;">NA</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">Construction</td>
+<td style="text-align: right;">0.033</td>
+<td style="text-align: right;">NA</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">Other</td>
+<td style="text-align: right;">0.054</td>
+<td style="text-align: right;">NA</td>
+</tr>
+</tbody>
+</table>
+
+
+</div>
 </div>
 </div>
 </figure>
 </div>
+</div>
 <p>This model is already designed to represent work from home using a lookup table of telecommute percentages segmented by job type. This data is used to create telecommuting trips that don’t get assigned during trip distribution. Additionally, there is a global scaling factor to adjust all telecommute percentages. The BYU Team proposes using the WFRC/MAG predicted 2040 telecommute percentages in place of the base year values. Figure 3 shows a plot of the average telecommute percentage by year as forecasted in the WFRC/MAG model.</p>
 <p>In the WFRC trip-based model, it was already programmed to reduce home-based work trips using a telecommute percentage for each type of employment and a percentage of home-base jobs. We simply changed the telecommute and home-based job percentages to align with projections for 2050. which increased telecommute trips from 3.7 to 10.2 percent and increased home-based jobs from 2.9 to 3.5 percent. If there was more guidance in the literature on how telecommuting affects other types of trips, then we could have tried to represent that in the model by adding a certain amount of discretionary trips according to the telecommute percentages. With the Wasatch Front Regional Council (WFRC) model, it might be difficult to have the additional discretionary trips be coming from the same zones as the telecommute trips. <em>(I’m not sure about this. I’ll have to look more into it)</em></p>
 <p>Asim is the same as calibration for the baseline scenario, but with different target %s (show table).</p>
@@ -276,19 +332,117 @@ <h2 data-number="6.2" class="anchored" data-anchor-id="scenario-creation"><span
 <section id="scenario-analysis" class="level2" data-number="6.3">
 <h2 data-number="6.3" class="anchored" data-anchor-id="scenario-analysis"><span class="header-section-number">6.3</span> Scenario Analysis</h2>
 <div class="cell">
-<div class="cell-output cell-output-stdout">
-<pre><code># A tibble: 9 × 8
-  purpose mode     cube_wfh cube_by cube_diff_pct asim_wfh asim_by asim_diff_pct
-  &lt;chr&gt;   &lt;chr&gt;       &lt;dbl&gt;   &lt;dbl&gt;         &lt;dbl&gt;    &lt;int&gt;   &lt;int&gt;         &lt;dbl&gt;
-1 hbo     auto     4097821.  4.10e6      0.000277  3078596 3042754        0.0118
-2 hbo     nonmotor  508869.  5.10e5     -0.00250    597323  591164        0.0104
-3 hbo     transit    37359.  3.73e4      0.000344   220937  218438        0.0114
-4 hbw     auto     1483120.  1.59e6     -0.0651    1209400 1288620       -0.0615
-5 hbw     nonmotor   71063.  7.65e4     -0.0712     180968  191880       -0.0569
-6 hbw     transit    44977.  4.88e4     -0.0774     178056  189610       -0.0609
-7 nhb     auto     2193201.  2.22e6     -0.0142    1606889 1645356       -0.0234
-8 nhb     nonmotor  144126.  1.46e5     -0.0156     216257  224248       -0.0356
-9 nhb     transit    13199.  1.35e4     -0.0189     100654  103398       -0.0265</code></pre>
+<div class="cell-output-display">
+<div>
+<table data-quarto-postprocess="true" class="table table-sm table-striped small">
+<thead>
+<tr class="header">
+<th style="text-align: left;" data-quarto-table-cell-role="th">purpose</th>
+<th style="text-align: left;" data-quarto-table-cell-role="th">mode</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">cube_wfh</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">cube_by</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">cube_diff_pct</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">asim_wfh</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">asim_by</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">asim_diff_pct</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td style="text-align: left;">hbo</td>
+<td style="text-align: left;">auto</td>
+<td style="text-align: right;">4097821</td>
+<td style="text-align: right;">4096688</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">3078596</td>
+<td style="text-align: right;">3042754</td>
+<td style="text-align: right;">0.012</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">hbo</td>
+<td style="text-align: left;">nonmotor</td>
+<td style="text-align: right;">508869</td>
+<td style="text-align: right;">510143</td>
+<td style="text-align: right;">-0.002</td>
+<td style="text-align: right;">597323</td>
+<td style="text-align: right;">591164</td>
+<td style="text-align: right;">0.010</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">hbo</td>
+<td style="text-align: left;">transit</td>
+<td style="text-align: right;">37359</td>
+<td style="text-align: right;">37346</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">220937</td>
+<td style="text-align: right;">218438</td>
+<td style="text-align: right;">0.011</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">hbw</td>
+<td style="text-align: left;">auto</td>
+<td style="text-align: right;">1483120</td>
+<td style="text-align: right;">1586414</td>
+<td style="text-align: right;">-0.065</td>
+<td style="text-align: right;">1209400</td>
+<td style="text-align: right;">1288620</td>
+<td style="text-align: right;">-0.061</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">hbw</td>
+<td style="text-align: left;">nonmotor</td>
+<td style="text-align: right;">71063</td>
+<td style="text-align: right;">76506</td>
+<td style="text-align: right;">-0.071</td>
+<td style="text-align: right;">180968</td>
+<td style="text-align: right;">191880</td>
+<td style="text-align: right;">-0.057</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">hbw</td>
+<td style="text-align: left;">transit</td>
+<td style="text-align: right;">44977</td>
+<td style="text-align: right;">48752</td>
+<td style="text-align: right;">-0.077</td>
+<td style="text-align: right;">178056</td>
+<td style="text-align: right;">189610</td>
+<td style="text-align: right;">-0.061</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">nhb</td>
+<td style="text-align: left;">auto</td>
+<td style="text-align: right;">2193201</td>
+<td style="text-align: right;">2224878</td>
+<td style="text-align: right;">-0.014</td>
+<td style="text-align: right;">1606889</td>
+<td style="text-align: right;">1645356</td>
+<td style="text-align: right;">-0.023</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">nhb</td>
+<td style="text-align: left;">nonmotor</td>
+<td style="text-align: right;">144126</td>
+<td style="text-align: right;">146404</td>
+<td style="text-align: right;">-0.016</td>
+<td style="text-align: right;">216257</td>
+<td style="text-align: right;">224248</td>
+<td style="text-align: right;">-0.036</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">nhb</td>
+<td style="text-align: left;">transit</td>
+<td style="text-align: right;">13199</td>
+<td style="text-align: right;">13453</td>
+<td style="text-align: right;">-0.019</td>
+<td style="text-align: right;">100654</td>
+<td style="text-align: right;">103398</td>
+<td style="text-align: right;">-0.027</td>
+</tr>
+</tbody>
+</table>
+
+
+</div>
 </div>
 </div>
 <p>Using the outputs from the trip-based model that show lengths and number of trips between zones, we calculated the vehicle miles traveled for each trip. This allowed us to see which zones had the biggest decrease in VMT. This also allowed us to find the number of trips of certain length to create a trip length frequency distribution that can be seen in (<em>another figure</em>).</p>
@@ -335,6 +489,24 @@ <h2 data-number="6.3" class="anchored" data-anchor-id="scenario-analysis"><span
 </div>
 </div>
 </div>
+<div class="cell">
+<div class="cell-output-display">
+<div>
+<figure class="figure">
+<p><img src="3_wfh_files/figure-html/unnamed-chunk-7-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<div class="cell-output-display">
+<div>
+<figure class="figure">
+<p><img src="3_wfh_files/figure-html/unnamed-chunk-8-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
 
 
 <div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list" style="display: none">
diff --git a/qmd/3_wfh_files/figure-html/unnamed-chunk-6-1.png b/qmd/3_wfh_files/figure-html/unnamed-chunk-6-1.png
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diff --git a/qmd/lit-review.html b/qmd/lit-review.html
index de68fe2..fdef389 100644
--- a/qmd/lit-review.html
+++ b/qmd/lit-review.html
@@ -245,44 +245,7 @@ <h3 class="anchored" data-anchor-id="population-data">Population Data</h3>
 <p>An alternative approach to population data is to use a full synthetic population. A synthetic population takes demographic and socioeconomic data at various levels of detail to create a “population” with generally the same distribution as the study area <span class="citation" data-cites="national_academies_travel_2012">(<a href="references.html#ref-national_academies_travel_2012" role="doc-biblioref">National Academies 2012, 93</a>)</span>. The goal is to have a population that is functionally similar to the actual population, but without the privacy concerns of using real individual household data. <span class="citation" data-cites="castiglione_application_2006">Castiglione et al. (<a href="references.html#ref-castiglione_application_2006" role="doc-biblioref">2006</a>)</span> argue that the major advantage with this approach is that the demographic and socioeconomic data is known at the person and household level, rather than the zone level, and this data remains available throughout the modeling process. This allows, for example, an equity analysis to determine the “winners” and “losers” of a proposed development without needing to encode demographic variables into each step of the model.</p>
 <p><span class="citation" data-cites="bills_looking_2017">Bills and Walker (<a href="references.html#ref-bills_looking_2017" role="doc-biblioref">2017</a>)</span> used the 2000 Bay Area Travel Survey to create a synthetic population and compare the effects that certain scenarios had on high income and low income populations. With a 20% reduction in travel cost, they found that high income workers benefited more than low income workers. They did similar comparisons for scenarios involving reduced travel times for different mode choices and saw the effects each scenario had on the high and low income workers. These types of analysis, which are not possible with aggregate population data (<strong>this is actually possible; a lot of trip-based models segment high/low income</strong>), can be very valuable in transportation planning and policy making.</p>
 <p>It is important to note that while many connect them only with ABMs, synthetic populations can be used in running trip-based models as well. Trip-based models using a synthetic population—often called trip-based microsimulation models—do exist (see <span class="citation" data-cites="walker_making_2005">Walker (<a href="references.html#ref-walker_making_2005" role="doc-biblioref">2005</a>)</span> and <span class="citation" data-cites="moeckel_agent-based_2020">Moeckel et al. (<a href="references.html#ref-moeckel_agent-based_2020" role="doc-biblioref">2020</a>)</span>), but these are relatively rare.</p>
-<p><a href="#fig-pipeline-example" class="quarto-xref">Figure&nbsp;<span>2.2</span></a> gives a visualization of an example “information pipeline” for a trip-based model using aggregate data and an ABM using a synthetic population. In the aggregate data model, it is impossible to know which trips are made by e.g.&nbsp;2-worker, 1-vehicle, low-income households; it only describes which trips are made by households with fewer vehicles than workers. With a synthetic population, however, <em>individuals</em> are being modeled, and so each trip can be traced to a specific person. All information is known at each point in the model regardless of which data is used in previous steps.</p>
-<div id="fig-pipeline-examplea" class="quarto-layout-panel">
-<figure class="quarto-float quarto-float-fig figure">
-<div aria-describedby="fig-pipeline-examplea-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<div class="quarto-layout-row">
-<div class="cell-output-display quarto-layout-cell-subref quarto-layout-cell" data-ref-parent="fig-pipeline-examplea" style="flex-basis: 100.0%;justify-content: flex-start;">
-<div id="fig-pipeline-examplea-1" class="quarto-figure quarto-figure-center quarto-float anchored" data-fig-scap="Example &quot;information pipeline&quot; for a trip-based model and an \acr{ABM}.">
-<figure class="quarto-float quarto-subfloat-fig figure">
-<div aria-describedby="fig-pipeline-examplea-1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="images/aggregate.png" class="img-fluid figure-img" data-fig-scap="Example &quot;information pipeline&quot; for a trip-based model and an \acr{ABM}." data-ref-parent="fig-pipeline-examplea">
-</div>
-<figcaption class="quarto-float-caption-bottom quarto-subfloat-caption quarto-subfloat-fig" id="fig-pipeline-examplea-1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-(a) Trip-based
-</figcaption>
-</figure>
-</div>
-</div>
-</div>
-<div class="quarto-layout-row">
-<div class="cell-output-display quarto-layout-cell-subref quarto-layout-cell" data-ref-parent="fig-pipeline-examplea" style="flex-basis: 100.0%;justify-content: flex-start;">
-<div id="fig-pipeline-examplea-2" class="quarto-figure quarto-figure-center quarto-float anchored" data-fig-scap="Example &quot;information pipeline&quot; for a trip-based model and an \acr{ABM}.">
-<figure class="quarto-float quarto-subfloat-fig figure">
-<div aria-describedby="fig-pipeline-examplea-2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="images/synthetic.png" class="img-fluid figure-img" data-fig-scap="Example &quot;information pipeline&quot; for a trip-based model and an \acr{ABM}." data-ref-parent="fig-pipeline-examplea">
-</div>
-<figcaption class="quarto-float-caption-bottom quarto-subfloat-caption quarto-subfloat-fig" id="fig-pipeline-examplea-2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-(b) Activity-based
-</figcaption>
-</figure>
-</div>
-</div>
-</div>
-</div>
-<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-pipeline-examplea-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;2.1: Example “information pipeline” for a trip-based model and an ABM.
-</figcaption>
-</figure>
-</div>
+<p><a href="#fig-pipeline-example" class="quarto-xref">Figure&nbsp;<span>2.1</span></a> gives a visualization of an example “information pipeline” for a trip-based model using aggregate data and an ABM using a synthetic population. In the aggregate data model, it is impossible to know which trips are made by e.g.&nbsp;2-worker, 1-vehicle, low-income households; it only describes which trips are made by households with fewer vehicles than workers. With a synthetic population, however, <em>individuals</em> are being modeled, and so each trip can be traced to a specific person. All information is known at each point in the model regardless of which data is used in previous steps.</p>
 <div id="fig-pipeline-example" class="quarto-layout-panel">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-pipeline-example-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
@@ -316,7 +279,7 @@ <h3 class="anchored" data-anchor-id="population-data">Population Data</h3>
 </div>
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-pipeline-example-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;2.2: Example “information pipeline” for a trip-based model and an ABM.
+Figure&nbsp;2.1: Example “information pipeline” for a trip-based model and an ABM.
 </figcaption>
 </figure>
 </div>
@@ -326,45 +289,41 @@ <h3 class="anchored" data-anchor-id="travel-behavior">Travel Behavior</h3>
 <p>The other primary difference between trip-based models and ABMs—and the main difference from trip-based microsimulation models—is that ABMs organize travel into “tours”, a sequence of trips that begin and end at the home, rather than just trips. It should be noted that <span class="citation" data-cites="miller_current_2023">Miller (<a href="references.html#ref-miller_current_2023" role="doc-biblioref">2023</a>)</span> argues that many current “activity-based” models ought to be labeled “tour-based” due to this focus on building tours. This is contrasted with “activity scheduling” models, in which activity participation is modeled explicitly and trips emerge as the means to get from one activity to the next. However, in practice there are few true “activity scheduling” models, and the term “activity-based” is used to refer to both activity scheduling and tour-based models.</p>
 <p>In a typical trip-based model, trips are forecasted based on empirical trip rates, usually by trip purpose and by household type (for example, low-income, 1-vehicle households make a certain number of “home-based work” trips) <span class="citation" data-cites="mcnally_four-step_2007">(<a href="references.html#ref-mcnally_four-step_2007" role="doc-biblioref">McNally 2007</a>)</span>. These trips are then assigned an origin and destination, mode, and often a time of day (peak/off-peak, etc.), resulting in a list of trips between each zone by mode and purpose. A trip-based microsimulation model may use regression models rather than aggregate data for some of the model steps <span class="citation" data-cites="moeckel_agent-based_2020">(<a href="references.html#ref-moeckel_agent-based_2020" role="doc-biblioref">Moeckel et al. 2020</a>)</span>, but the end result is similar: a list of trips by person, noting mode and purpose. However, this trip list may be inconsistent, and the forecasted trips may not be physically possible to complete in any sequence, as there is no sense of “trip-chaining”. The hope, though, is that over an area the inconsistencies would cancel out, leaving an overall accurate forecast.</p>
 <p>ABMs, on the other hand, model <em>tours</em> rather than trips. This attempts to create consistency in trip origins/destinations, mode choice, and time of day: since each trip is a part of a “chain” (tour), the trips within a tour are dependent on each other <span class="citation" data-cites="rasouli_activity-based_2014">(<a href="references.html#ref-rasouli_activity-based_2014" role="doc-biblioref">Rasouli and Timmermans 2014</a>)</span>. The open-source ABM ActivitySim <span class="citation" data-cites="association_of_metropolitan_planning_organizations_activitysim_2023">(<a href="references.html#ref-association_of_metropolitan_planning_organizations_activitysim_2023" role="doc-biblioref">Association of Metropolitan Planning Organizations 2023</a>)</span>, for example, has a tour-scheduling model that determines the number of “mandatory” (work, school, etc.) and “discretionary” tours each individual will make, and performs tour-level mode and destination choice for each tour. After the tour-level decisions are made, trip-level mode/destination choice is done for each trip in the tour, including the possible addition of subtours (see <span class="citation" data-cites="vovsha_activity-based_2005">Vovsha, Bradley, and Bowman (<a href="references.html#ref-vovsha_activity-based_2005" role="doc-biblioref">2005</a>)</span>, fig.&nbsp;18.1).</p>
-<p>Figures <a href="#fig-network-aggregate" class="quarto-xref"><span>2.3</span></a> and <a href="#fig-network-synth" class="quarto-xref"><span>2.4</span></a> show an example of the trips assigned to a network in the various model forms. <a href="#fig-network-aggregate" class="quarto-xref">Figure&nbsp;<span>2.3</span></a> depicts network assignment in a typical trip-based model where the total number of trips between each zone is given. With these results, the mode and purpose of each trip is known, but, with aggregate data, there is no way of telling who made which trips other than the segmentation in the previous steps (see <a href="#fig-pipeline-example-1" class="quarto-xref">Figure&nbsp;<span>2.2 (a)</span></a>). It is also not possible to construct a coherent daily list of trips for individuals.</p>
-<p><a href="#fig-network-synth" class="quarto-xref">Figure&nbsp;<span>2.4</span></a>, on the other hand, depicts visual representations of an <em>individual’s</em> travel made possible by the use of a synthetic population. <a href="#fig-network-synth-1" class="quarto-xref">Figure&nbsp;<span>2.4 (a)</span></a> depicts the trip list that could be given for an individual in a trip-based microsimulation model. Though each individual’s trips are known, there is no guarantee of consistency between trips. For example, it could predict that the individual takes transit to work but then drives home or that the individual makes two recreational trips without ever making a return trip. The activity-based approach, depicted in <a href="#fig-network-synth-2" class="quarto-xref">Figure&nbsp;<span>2.4 (b)</span></a>, attempts to add this consistency by modeling tours, and only generating trips consistent with each tour.</p>
-<div class="cell">
-<div class="cell-output-display">
+<p>Figures <a href="#fig-network-aggregate" class="quarto-xref"><span>2.2</span></a> and <a href="#fig-network-synth" class="quarto-xref"><span>2.3</span></a> show an example of the trips assigned to a network in the various model forms. <a href="#fig-network-aggregate" class="quarto-xref">Figure&nbsp;<span>2.2</span></a> depicts network assignment in a typical trip-based model where the total number of trips between each zone is given. With these results, the mode and purpose of each trip is known, but, with aggregate data, there is no way of telling who made which trips other than the segmentation in the previous steps (see <a href="#fig-pipeline-example-1" class="quarto-xref">Figure&nbsp;<span>2.1 (a)</span></a>). It is also not possible to construct a coherent daily list of trips for individuals.</p>
+<p><a href="#fig-network-synth" class="quarto-xref">Figure&nbsp;<span>2.3</span></a>, on the other hand, depicts visual representations of an <em>individual’s</em> travel made possible by the use of a synthetic population. <a href="#fig-network-tbmicro" class="quarto-xref">Figure&nbsp;<span>2.3 (a)</span></a> depicts the trip list that could be given for an individual in a trip-based microsimulation model. Though each individual’s trips are known, there is no guarantee of consistency between trips. For example, it could predict that the individual takes transit to work but then drives home or that the individual makes two recreational trips without ever making a return trip. The activity-based approach, depicted in <a href="#fig-network-abm" class="quarto-xref">Figure&nbsp;<span>2.3 (b)</span></a>, attempts to add this consistency by modeling tours, and only generating trips consistent with each tour.</p>
 <div id="fig-network-aggregate" class="quarto-figure quarto-figure-center quarto-float anchored" data-fig-scap="Example network assignment using aggregate data.">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-network-aggregate-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="images/tbm.png" class="img-fluid figure-img" data-fig-scap="Example network assignment using aggregate data.">
+<img src="../images/tbm.png" class="img-fluid figure-img" data-fig-scap="Example network assignment using aggregate data.">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-network-aggregate-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;2.3: Example network assignment using aggregate data. There is little information on who is making which trips, and it is not known how trips are related to each other.
+Figure&nbsp;2.2: Example network assignment using aggregate data. There is little information on who is making which trips, and it is not known how trips are related to each other.
 </figcaption>
 </figure>
 </div>
-</div>
-</div>
 <div id="fig-network-synth" class="quarto-layout-panel">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-network-synth-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <div class="quarto-layout-row">
-<div class="cell-output-display quarto-layout-cell-subref quarto-layout-cell" data-ref-parent="fig-network-synth" style="flex-basis: 50.0%;justify-content: flex-start;">
-<div id="fig-network-synth-1" class="quarto-figure quarto-figure-center quarto-float anchored" data-fig-scap="Example network assignment using a synthetic population.">
+<div class="quarto-layout-cell-subref quarto-layout-cell" data-ref-parent="fig-network-synth" style="flex-basis: 50.0%;justify-content: flex-start;">
+<div id="fig-network-tbmicro" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-subfloat-fig figure">
-<div aria-describedby="fig-network-synth-1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="images/trip.png" class="img-fluid figure-img" data-fig-scap="Example network assignment using a synthetic population." data-ref-parent="fig-network-synth">
+<div aria-describedby="fig-network-tbmicro-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<img src="../images/trip.png" class="img-fluid figure-img" data-ref-parent="fig-network-synth">
 </div>
-<figcaption class="quarto-float-caption-bottom quarto-subfloat-caption quarto-subfloat-fig" id="fig-network-synth-1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<figcaption class="quarto-float-caption-bottom quarto-subfloat-caption quarto-subfloat-fig" id="fig-network-tbmicro-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 (a) Trip-based microsimulation
 </figcaption>
 </figure>
 </div>
 </div>
-<div class="cell-output-display quarto-layout-cell-subref quarto-layout-cell" data-ref-parent="fig-network-synth" style="flex-basis: 50.0%;justify-content: flex-start;">
-<div id="fig-network-synth-2" class="quarto-figure quarto-figure-center quarto-float anchored" data-fig-scap="Example network assignment using a synthetic population.">
+<div class="quarto-layout-cell-subref quarto-layout-cell" data-ref-parent="fig-network-synth" style="flex-basis: 50.0%;justify-content: flex-start;">
+<div id="fig-network-abm" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-subfloat-fig figure">
-<div aria-describedby="fig-network-synth-2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="images/tour.png" class="img-fluid figure-img" data-fig-scap="Example network assignment using a synthetic population." data-ref-parent="fig-network-synth">
+<div aria-describedby="fig-network-abm-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<img src="../images/tour.png" class="img-fluid figure-img" data-ref-parent="fig-network-synth">
 </div>
-<figcaption class="quarto-float-caption-bottom quarto-subfloat-caption quarto-subfloat-fig" id="fig-network-synth-2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<figcaption class="quarto-float-caption-bottom quarto-subfloat-caption quarto-subfloat-fig" id="fig-network-abm-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 (b) Activity (tour)-based
 </figcaption>
 </figure>
@@ -373,7 +332,7 @@ <h3 class="anchored" data-anchor-id="travel-behavior">Travel Behavior</h3>
 </div>
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-network-synth-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;2.4: Example network assignment using a synthetic population allows an individual’s travel to be tracked. In the tour-based approach, an attempt is made to make the trips consistent with each other.
+Figure&nbsp;2.3: Example network assignment using a synthetic population allows an individual’s travel to be tracked. In the tour-based approach, an attempt is made to make the trips consistent with each other.
 </figcaption>
 </figure>
 </div>
diff --git a/qmd/methods.html b/qmd/methods.html
index 8ed75f9..c6f2e72 100644
--- a/qmd/methods.html
+++ b/qmd/methods.html
@@ -279,7 +279,8 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="wfrc-model"><span class="
 <section id="activitysim" class="level2" data-number="3.2">
 <h2 data-number="3.2" class="anchored" data-anchor-id="activitysim"><span class="header-section-number">3.2</span> ActivitySim</h2>
 <p>ActivitySim is an open-source ABM led by a consortium of transportation planning agencies. ActivitySim is highly configurable, and many agencies have their own bespoke implementation. This paper uses an ActivitySim implementation based on <span class="citation" data-cites="gregory_s_macfarlane_estimation_2021">Gregory S. Macfarlane and Nathan J. Lant (<a href="references.html#ref-gregory_s_macfarlane_estimation_2021" role="doc-biblioref">2021</a>)</span>, which is in turn based on the prototype configuration for the Metropolitan Transportation Commission serving the San Francisco area <span class="citation" data-cites="erhardt_mtcs_2011">(<a href="references.html#ref-erhardt_mtcs_2011" role="doc-biblioref">Erhardt et al. 2011</a>)</span>.</p>
-<p>ActivitySim requires similar inputs to the WFRC model, though it does not assign traffic and so does not require any transportation networks. Additionally, ActivitySim requires population data at an individual level, including information such as age, household income, and home location. Due to privacy concerns, real data is rarely used for this purpose, and a synthetic population representative of the study area is used instead. <a href="#sec-populationsim" class="quarto-xref"><span>Section 3.2.1</span></a> discusses the population used in more detail.</p>
+<p>ActivitySim requires similar inputs to the WFRC model, though it does not assign traffic and so does not require any transportation networks. However, ActivitySim does require network skims for information on travel time, cost, etc. These skims are obtained from any network assignment process, though ActivitySim itself does not include network assignment. A discussion and comparison of network assignment processes is outside the scope of this project, so this ActivitySim implementation uses the travel skims output from the WFRC model directly.</p>
+<p>ActivitySim additionally requires population data at an individual level, including information such as age, household income, and home location. Due to privacy concerns, real data is rarely used for this purpose, and a synthetic population representative of the study area is used instead. <a href="#sec-populationsim" class="quarto-xref"><span>Section 3.2.1</span></a> discusses the population used in more detail.</p>
 <p>ActivitySim, like all ABMs, simulates transportation decisions on an individual level. ActivitySim has a hierarchical decision tree, where long-term decisions (such as auto ownership and telecommute frequency) are made first, followed by daily and tour- and trip-level decisions such as scheduling and mode choice (see <a href="#fig-asim-flowchart" class="quarto-xref">Figure&nbsp;<span>3.1</span></a>). Each of these steps determines information that will be used in subsequent steps, and many steps can be turned on or off depending on what is needed for the model implementation.</p>
 <div id="fig-asim-flowchart" class="quarto-figure quarto-figure-center quarto-float anchored" data-fig-scap="ActivitySim sub-model flowchart.">
 <figure class="quarto-float quarto-float-fig figure">
@@ -618,15 +619,9 @@ <h3 class="anchored" data-anchor-id="verification-and-calibration-of-activitysim
 </div>
 </div>
 <p>The WFRC model has basic support for predicting telecommuting and work-from-home trips. This includes a lookup table of telecommute percentages based on job type and year. ActivitySim also has this functionality, and can additionally use individual- and household-level variables in its predictions. It is worth noting that both the WFRC model and ActivitySim make a distinction between “telecommuting”, where an individual commutes to work some days and does not others, and “work-from-home” (or “home-based jobs” in the WFRC model), where an individual’s workplace is always at their home.</p>
-<p>wfh and telecommute_freq models, taken from SEMCOG prototype. had to adjust to work with MTC, not too bad. Also added job category coefficients to tf model, calibrated to wfrc percent (mention in methodology)</p>
+<p>wfh and telecommute_freq models, taken from SEMCOG prototype.</p>
 <p>Both models treat “work-from-home”/“home-based jobs” similarly. The WFRC model’s land use data contains employment by type in each TAZ, and it considers a “home-based job” as a separate job type, so these are not counted toward employment totals in trip generation and subsequent steps. <!-- @fig-wfrc-wfh shows the distribution of home-based job percentage by TAZ in the WFRC land use data. --><br>
 ActivitySim has a “work from home” submodel which assigns workers work-from-home status based on personal variables such as income, sex, and education (coefficients on these variables were left unchanged from the existing configuration). There is also a “target work-from-home percent” value that adjusts the model to reach the specified work-from-home proportion of all workers. Individuals with work-from-home status are then prohibited from making a mandatory tour. This target work-from-home percentage is set at 2.3%, based on a weighted average from the WFRC model data. There are no other adjustments to the ActivitySim work-from-home submodel.</p>
-<p><br>
-<!-- ```{r} --><br>
-<!-- #| label: fig-wfrc-wfh --><br>
-<!-- #| fig-cap: wfrc wfh data --><br>
-<!-- targets::tar_read(wfrc_hbj_base_plot) --><br>
-<!-- ``` --></p>
 <p>The two models differ in their approach to telecommuting, however. The WFRC model has a lookup table of telecommuting shares based on job type (see <a href="#tbl-baseline-telecommute" class="quarto-xref">Table&nbsp;<span>3.4</span></a>), including predictions for future years. ActivitySim has a “telecommute frequency” submodel which assigns workers a telecommute status indicating the number of days they work remotely per week. Based on this status, ActivitySim adjusts the likelihood of making a mandatory tour. Telecommute status depends on personal variables similar to those in the work-from-home submodel by default. In order to calibrate to the WFRC data, however, we added additional job type variables to ActivitySim to match those given in <a href="#tbl-baseline-telecommute" class="quarto-xref">Table&nbsp;<span>3.4</span></a>. Because these are choice coefficients rather than target percentages, the values needed to be calibrated to match the WFRC targets. The calibration allowed ActivitySim to match these targets exactly, and the coefficients are given in <a href="#tbl-baseline-telecommute" class="quarto-xref">Table&nbsp;<span>3.4</span></a>.</p>
 <div class="cell">
 <div id="tbl-baseline-telecommute" class="cell quarto-float anchored">
@@ -745,8 +740,7 @@ <h3 class="anchored" data-anchor-id="verification-and-calibration-of-activitysim
 </section>
 <section id="example-scenarios" class="level2" data-number="3.4">
 <h2 data-number="3.4" class="anchored" data-anchor-id="example-scenarios"><span class="header-section-number">3.4</span> Example Scenarios</h2>
-<p>There are three proposed scenarios to implement and run in each model for comparison. <!-- Each scenario intends to be representative of a real-world  --><br>
-This is not a comprehensive list covering all potential scenario possibilities, but is taken to represent many of the types of scenarios an agency would analyze.</p>
+<p>There are three proposed scenarios to implement and run in each model for comparison. This is not a comprehensive list covering all potential scenario possibilities, but is taken to represent many of the types of scenarios an agency would analyze.</p>
 <p>The first scenario involves a change in land use near the former state prison site in Draper, Utah. Current plans for this site involve new development known as “The Point”, which will add high-density housing and commercial development to the area. This research scenario will be based on this development, but will include only the land use changes. The actual development plans also include expansion of transit, but this will not be a part of this scenario.</p>
 <p>The second scenario centers around an augmentation of transit service along the Wasatch Front. The FrontRunner, a CRT line connecting Provo to Ogden, is slated for expansion. The expansion includes additional stations and increased travel speeds due to vehicle electrification. This scenario models these changes in accordance with the planned expansion of the service.</p>
 <p>The third scenario addresses the growing trend of remote work. Given technological advancements and the notable surge in remote work during the COVID-19 pandemic, this scenario models a substantial increase in remote work based on projections from WFRC.</p>
diff --git a/search.json b/search.json
index 9c3cd9d..d751ae9 100644
--- a/search.json
+++ b/search.json
@@ -24,7 +24,7 @@
     "href": "qmd/lit-review.html#comparison-of-modeling-frameworks",
     "title": "2  Literature Review",
     "section": "2.2 Comparison of Modeling Frameworks",
-    "text": "2.2 Comparison of Modeling Frameworks\nIn discussing the differences between ABMs and trip-based models, there are really two comparisons that need to be made: how the population data is structured, and how travel is organized. Trip-based models generally use aggregate population data while ABMs use a synthetic population, and trip-based models organize travel into trips while ABMs organize travel into activities and tours. The following sections will explain these aspects of travel demand modeling and discuss the claimed advantages and disadvantages of each model type.\n\nPopulation Data\nThe aggregate population data used in trip-based models can vary in origin and level of detail, but the basic concept is the same: the study area is organized into generally small zones, and certain demographic and socioeconomic data is known or obtained for each zone (National Academies 2012, 14). This includes data such as number of households, average household income, population, number of workers, etc. Based on this information, the zone can be segmented along arbitrary variables. For example, since households with more workers and more vehicles tend to make more work trips, it is useful to estimate the distribution of households in each zone along these two variables. Then, average trip production rates are determined for each household category (e.g. for households with each number of workers by vehicles), and the total number of trips produced in a zone is calculated based on these rates (National Academies 2012, 37).\nThis approach is relatively straightforward: the required input data is usually easy to obtain, the trip generation models are often simple, and it is computationally inexpensive (National Academies 2012). However, the types of analyses possible are limited by the initial segmentation of the aggregate population data. An analysis based on parents’/adults’ highest received education, for example, would require determining the number of households in each TAZ with each possible combination of education level. This can theoretically be done, but more detailed and varied analyses would require more levels of segmentation, greatly increasing the number of classifications needed. Aggregation at any point precludes that segmentation from use in subsequent model steps as well as in any post-hoc analysis. Since these segmentations need to be carried through each model step, trip rates, mode choice equations, etc. need to be estimated for every classification, and while relevant real-world data may exist, sample sizes approach zero very quickly, and so the estimates have little statistical value (Moeckel et al. 2020; National Academies 2012).\nThis becomes a particular issue in equity analysis because it is perhaps impossible to determine equitable distribution of “winners” and “losers” of a potential policy without using demographic variables in the trip generation and destination and mode choice steps (Bills and Walker 2017). Though many studies have shown that trip production and mode choice behavior differ by ethnic group even after controlling for income Bhat and Naumann (2013), including such variables in trip-based models is problematic. Does coding such a variable in a mode choice model represent discrimination? Or does doing so assert that present differences resulting from unequal opportunity will persist into the future planning years? Regardless the reasons for their exclusion, these variables consequently cannot be used in a post-hoc analysis of a transportation policy because the trip matrices do not contain the adequate segmentation.\nAn alternative approach to population data is to use a full synthetic population. A synthetic population takes demographic and socioeconomic data at various levels of detail to create a “population” with generally the same distribution as the study area (National Academies 2012, 93). The goal is to have a population that is functionally similar to the actual population, but without the privacy concerns of using real individual household data. Castiglione et al. (2006) argue that the major advantage with this approach is that the demographic and socioeconomic data is known at the person and household level, rather than the zone level, and this data remains available throughout the modeling process. This allows, for example, an equity analysis to determine the “winners” and “losers” of a proposed development without needing to encode demographic variables into each step of the model.\nBills and Walker (2017) used the 2000 Bay Area Travel Survey to create a synthetic population and compare the effects that certain scenarios had on high income and low income populations. With a 20% reduction in travel cost, they found that high income workers benefited more than low income workers. They did similar comparisons for scenarios involving reduced travel times for different mode choices and saw the effects each scenario had on the high and low income workers. These types of analysis, which are not possible with aggregate population data (this is actually possible; a lot of trip-based models segment high/low income), can be very valuable in transportation planning and policy making.\nIt is important to note that while many connect them only with ABMs, synthetic populations can be used in running trip-based models as well. Trip-based models using a synthetic population—often called trip-based microsimulation models—do exist (see Walker (2005) and Moeckel et al. (2020)), but these are relatively rare.\nFigure 2.2 gives a visualization of an example “information pipeline” for a trip-based model using aggregate data and an ABM using a synthetic population. In the aggregate data model, it is impossible to know which trips are made by e.g. 2-worker, 1-vehicle, low-income households; it only describes which trips are made by households with fewer vehicles than workers. With a synthetic population, however, individuals are being modeled, and so each trip can be traced to a specific person. All information is known at each point in the model regardless of which data is used in previous steps.\n\n\n\n\n\n\n\n\n\n\n\n(a) Trip-based\n\n\n\n\n\n\n\n\n\n\n\n\n\n(b) Activity-based\n\n\n\n\n\n\n\nFigure 2.1: Example “information pipeline” for a trip-based model and an ABM.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n(a) Trip-based\n\n\n\n\n\n\n\n\n\n\n\n\n\n(b) Activity-based\n\n\n\n\n\n\n\nFigure 2.2: Example “information pipeline” for a trip-based model and an ABM.\n\n\n\n\n\nTravel Behavior\nThe other primary difference between trip-based models and ABMs—and the main difference from trip-based microsimulation models—is that ABMs organize travel into “tours”, a sequence of trips that begin and end at the home, rather than just trips. It should be noted that Miller (2023) argues that many current “activity-based” models ought to be labeled “tour-based” due to this focus on building tours. This is contrasted with “activity scheduling” models, in which activity participation is modeled explicitly and trips emerge as the means to get from one activity to the next. However, in practice there are few true “activity scheduling” models, and the term “activity-based” is used to refer to both activity scheduling and tour-based models.\nIn a typical trip-based model, trips are forecasted based on empirical trip rates, usually by trip purpose and by household type (for example, low-income, 1-vehicle households make a certain number of “home-based work” trips) (McNally 2007). These trips are then assigned an origin and destination, mode, and often a time of day (peak/off-peak, etc.), resulting in a list of trips between each zone by mode and purpose. A trip-based microsimulation model may use regression models rather than aggregate data for some of the model steps (Moeckel et al. 2020), but the end result is similar: a list of trips by person, noting mode and purpose. However, this trip list may be inconsistent, and the forecasted trips may not be physically possible to complete in any sequence, as there is no sense of “trip-chaining”. The hope, though, is that over an area the inconsistencies would cancel out, leaving an overall accurate forecast.\nABMs, on the other hand, model tours rather than trips. This attempts to create consistency in trip origins/destinations, mode choice, and time of day: since each trip is a part of a “chain” (tour), the trips within a tour are dependent on each other (Rasouli and Timmermans 2014). The open-source ABM ActivitySim (Association of Metropolitan Planning Organizations 2023), for example, has a tour-scheduling model that determines the number of “mandatory” (work, school, etc.) and “discretionary” tours each individual will make, and performs tour-level mode and destination choice for each tour. After the tour-level decisions are made, trip-level mode/destination choice is done for each trip in the tour, including the possible addition of subtours (see Vovsha, Bradley, and Bowman (2005), fig. 18.1).\nFigures 2.3 and 2.4 show an example of the trips assigned to a network in the various model forms. Figure 2.3 depicts network assignment in a typical trip-based model where the total number of trips between each zone is given. With these results, the mode and purpose of each trip is known, but, with aggregate data, there is no way of telling who made which trips other than the segmentation in the previous steps (see Figure 2.2 (a)). It is also not possible to construct a coherent daily list of trips for individuals.\nFigure 2.4, on the other hand, depicts visual representations of an individual’s travel made possible by the use of a synthetic population. Figure 2.4 (a) depicts the trip list that could be given for an individual in a trip-based microsimulation model. Though each individual’s trips are known, there is no guarantee of consistency between trips. For example, it could predict that the individual takes transit to work but then drives home or that the individual makes two recreational trips without ever making a return trip. The activity-based approach, depicted in Figure 2.4 (b), attempts to add this consistency by modeling tours, and only generating trips consistent with each tour.\n\n\n\n\n\n\n\n\nFigure 2.3: Example network assignment using aggregate data. There is little information on who is making which trips, and it is not known how trips are related to each other.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n(a) Trip-based microsimulation\n\n\n\n\n\n\n\n\n\n\n\n(b) Activity (tour)-based\n\n\n\n\n\n\n\nFigure 2.4: Example network assignment using a synthetic population allows an individual’s travel to be tracked. In the tour-based approach, an attempt is made to make the trips consistent with each other.\n\n\n\nIn addition to intra-person dependencies, Rasouli and Timmermans (2014) note that ABMs can model dependencies between members of a household as well. A vehicle can’t be used by multiple people in the same household at the same time to travel to different destinations. Because the people within the household will have travel patterns that depend on the patterns of others in the household, a policy affecting one person in the household can affect everyone in the household no matter how directly the policy connects to them (Vovsha, Bradley, and Bowman 2005). These effects aren’t possible to forecast in a trip-based model.\nAnother advantage of organizing travel into tours comes regarding accessibility analyses. Dong et al. (2006) note that when trip-based models are used to analyze accessibility, each zone must be analyzed independently of travel behavior. This approach only analyzes zones’ proximity to each other and does not take into account individual travel patterns. They argue that this is a limited view of accessibility, and discuss the “activity-based accessibility measure” (ABA), which is evaluated based on all trips in a day rather than particular trips. As an example, if an individual doesn’t live within a 20-minute drive of a grocery store, traditional measures might rate this as poor accessibility. However, if they travel to a park with a nearby grocery store as part of their DAP, then in reality the accessibility should be rated much higher. This proximity may even influence which park is chosen. Overall, they found that the ABA predicts more reasonable accessibility outcomes compared to traditional measures.",
+    "text": "2.2 Comparison of Modeling Frameworks\nIn discussing the differences between ABMs and trip-based models, there are really two comparisons that need to be made: how the population data is structured, and how travel is organized. Trip-based models generally use aggregate population data while ABMs use a synthetic population, and trip-based models organize travel into trips while ABMs organize travel into activities and tours. The following sections will explain these aspects of travel demand modeling and discuss the claimed advantages and disadvantages of each model type.\n\nPopulation Data\nThe aggregate population data used in trip-based models can vary in origin and level of detail, but the basic concept is the same: the study area is organized into generally small zones, and certain demographic and socioeconomic data is known or obtained for each zone (National Academies 2012, 14). This includes data such as number of households, average household income, population, number of workers, etc. Based on this information, the zone can be segmented along arbitrary variables. For example, since households with more workers and more vehicles tend to make more work trips, it is useful to estimate the distribution of households in each zone along these two variables. Then, average trip production rates are determined for each household category (e.g. for households with each number of workers by vehicles), and the total number of trips produced in a zone is calculated based on these rates (National Academies 2012, 37).\nThis approach is relatively straightforward: the required input data is usually easy to obtain, the trip generation models are often simple, and it is computationally inexpensive (National Academies 2012). However, the types of analyses possible are limited by the initial segmentation of the aggregate population data. An analysis based on parents’/adults’ highest received education, for example, would require determining the number of households in each TAZ with each possible combination of education level. This can theoretically be done, but more detailed and varied analyses would require more levels of segmentation, greatly increasing the number of classifications needed. Aggregation at any point precludes that segmentation from use in subsequent model steps as well as in any post-hoc analysis. Since these segmentations need to be carried through each model step, trip rates, mode choice equations, etc. need to be estimated for every classification, and while relevant real-world data may exist, sample sizes approach zero very quickly, and so the estimates have little statistical value (Moeckel et al. 2020; National Academies 2012).\nThis becomes a particular issue in equity analysis because it is perhaps impossible to determine equitable distribution of “winners” and “losers” of a potential policy without using demographic variables in the trip generation and destination and mode choice steps (Bills and Walker 2017). Though many studies have shown that trip production and mode choice behavior differ by ethnic group even after controlling for income Bhat and Naumann (2013), including such variables in trip-based models is problematic. Does coding such a variable in a mode choice model represent discrimination? Or does doing so assert that present differences resulting from unequal opportunity will persist into the future planning years? Regardless the reasons for their exclusion, these variables consequently cannot be used in a post-hoc analysis of a transportation policy because the trip matrices do not contain the adequate segmentation.\nAn alternative approach to population data is to use a full synthetic population. A synthetic population takes demographic and socioeconomic data at various levels of detail to create a “population” with generally the same distribution as the study area (National Academies 2012, 93). The goal is to have a population that is functionally similar to the actual population, but without the privacy concerns of using real individual household data. Castiglione et al. (2006) argue that the major advantage with this approach is that the demographic and socioeconomic data is known at the person and household level, rather than the zone level, and this data remains available throughout the modeling process. This allows, for example, an equity analysis to determine the “winners” and “losers” of a proposed development without needing to encode demographic variables into each step of the model.\nBills and Walker (2017) used the 2000 Bay Area Travel Survey to create a synthetic population and compare the effects that certain scenarios had on high income and low income populations. With a 20% reduction in travel cost, they found that high income workers benefited more than low income workers. They did similar comparisons for scenarios involving reduced travel times for different mode choices and saw the effects each scenario had on the high and low income workers. These types of analysis, which are not possible with aggregate population data (this is actually possible; a lot of trip-based models segment high/low income), can be very valuable in transportation planning and policy making.\nIt is important to note that while many connect them only with ABMs, synthetic populations can be used in running trip-based models as well. Trip-based models using a synthetic population—often called trip-based microsimulation models—do exist (see Walker (2005) and Moeckel et al. (2020)), but these are relatively rare.\nFigure 2.1 gives a visualization of an example “information pipeline” for a trip-based model using aggregate data and an ABM using a synthetic population. In the aggregate data model, it is impossible to know which trips are made by e.g. 2-worker, 1-vehicle, low-income households; it only describes which trips are made by households with fewer vehicles than workers. With a synthetic population, however, individuals are being modeled, and so each trip can be traced to a specific person. All information is known at each point in the model regardless of which data is used in previous steps.\n\n\n\n\n\n\n\n\n\n\n\n(a) Trip-based\n\n\n\n\n\n\n\n\n\n\n\n\n\n(b) Activity-based\n\n\n\n\n\n\n\nFigure 2.1: Example “information pipeline” for a trip-based model and an ABM.\n\n\n\n\n\nTravel Behavior\nThe other primary difference between trip-based models and ABMs—and the main difference from trip-based microsimulation models—is that ABMs organize travel into “tours”, a sequence of trips that begin and end at the home, rather than just trips. It should be noted that Miller (2023) argues that many current “activity-based” models ought to be labeled “tour-based” due to this focus on building tours. This is contrasted with “activity scheduling” models, in which activity participation is modeled explicitly and trips emerge as the means to get from one activity to the next. However, in practice there are few true “activity scheduling” models, and the term “activity-based” is used to refer to both activity scheduling and tour-based models.\nIn a typical trip-based model, trips are forecasted based on empirical trip rates, usually by trip purpose and by household type (for example, low-income, 1-vehicle households make a certain number of “home-based work” trips) (McNally 2007). These trips are then assigned an origin and destination, mode, and often a time of day (peak/off-peak, etc.), resulting in a list of trips between each zone by mode and purpose. A trip-based microsimulation model may use regression models rather than aggregate data for some of the model steps (Moeckel et al. 2020), but the end result is similar: a list of trips by person, noting mode and purpose. However, this trip list may be inconsistent, and the forecasted trips may not be physically possible to complete in any sequence, as there is no sense of “trip-chaining”. The hope, though, is that over an area the inconsistencies would cancel out, leaving an overall accurate forecast.\nABMs, on the other hand, model tours rather than trips. This attempts to create consistency in trip origins/destinations, mode choice, and time of day: since each trip is a part of a “chain” (tour), the trips within a tour are dependent on each other (Rasouli and Timmermans 2014). The open-source ABM ActivitySim (Association of Metropolitan Planning Organizations 2023), for example, has a tour-scheduling model that determines the number of “mandatory” (work, school, etc.) and “discretionary” tours each individual will make, and performs tour-level mode and destination choice for each tour. After the tour-level decisions are made, trip-level mode/destination choice is done for each trip in the tour, including the possible addition of subtours (see Vovsha, Bradley, and Bowman (2005), fig. 18.1).\nFigures 2.2 and 2.3 show an example of the trips assigned to a network in the various model forms. Figure 2.2 depicts network assignment in a typical trip-based model where the total number of trips between each zone is given. With these results, the mode and purpose of each trip is known, but, with aggregate data, there is no way of telling who made which trips other than the segmentation in the previous steps (see Figure 2.1 (a)). It is also not possible to construct a coherent daily list of trips for individuals.\nFigure 2.3, on the other hand, depicts visual representations of an individual’s travel made possible by the use of a synthetic population. Figure 2.3 (a) depicts the trip list that could be given for an individual in a trip-based microsimulation model. Though each individual’s trips are known, there is no guarantee of consistency between trips. For example, it could predict that the individual takes transit to work but then drives home or that the individual makes two recreational trips without ever making a return trip. The activity-based approach, depicted in Figure 2.3 (b), attempts to add this consistency by modeling tours, and only generating trips consistent with each tour.\n\n\n\n\n\n\nFigure 2.2: Example network assignment using aggregate data. There is little information on who is making which trips, and it is not known how trips are related to each other.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n(a) Trip-based microsimulation\n\n\n\n\n\n\n\n\n\n\n\n(b) Activity (tour)-based\n\n\n\n\n\n\n\nFigure 2.3: Example network assignment using a synthetic population allows an individual’s travel to be tracked. In the tour-based approach, an attempt is made to make the trips consistent with each other.\n\n\n\nIn addition to intra-person dependencies, Rasouli and Timmermans (2014) note that ABMs can model dependencies between members of a household as well. A vehicle can’t be used by multiple people in the same household at the same time to travel to different destinations. Because the people within the household will have travel patterns that depend on the patterns of others in the household, a policy affecting one person in the household can affect everyone in the household no matter how directly the policy connects to them (Vovsha, Bradley, and Bowman 2005). These effects aren’t possible to forecast in a trip-based model.\nAnother advantage of organizing travel into tours comes regarding accessibility analyses. Dong et al. (2006) note that when trip-based models are used to analyze accessibility, each zone must be analyzed independently of travel behavior. This approach only analyzes zones’ proximity to each other and does not take into account individual travel patterns. They argue that this is a limited view of accessibility, and discuss the “activity-based accessibility measure” (ABA), which is evaluated based on all trips in a day rather than particular trips. As an example, if an individual doesn’t live within a 20-minute drive of a grocery store, traditional measures might rate this as poor accessibility. However, if they travel to a park with a nearby grocery store as part of their DAP, then in reality the accessibility should be rated much higher. This proximity may even influence which park is chosen. Overall, they found that the ABA predicts more reasonable accessibility outcomes compared to traditional measures.",
     "crumbs": [
       "<span class='chapter-number'>2</span>  <span class='chapter-title'>Literature Review</span>"
     ]
@@ -74,7 +74,7 @@
     "href": "qmd/methods.html#activitysim",
     "title": "3  Methodology",
     "section": "3.2 ActivitySim",
-    "text": "3.2 ActivitySim\nActivitySim is an open-source ABM led by a consortium of transportation planning agencies. ActivitySim is highly configurable, and many agencies have their own bespoke implementation. This paper uses an ActivitySim implementation based on Gregory S. Macfarlane and Nathan J. Lant (2021), which is in turn based on the prototype configuration for the Metropolitan Transportation Commission serving the San Francisco area (Erhardt et al. 2011).\nActivitySim requires similar inputs to the WFRC model, though it does not assign traffic and so does not require any transportation networks. Additionally, ActivitySim requires population data at an individual level, including information such as age, household income, and home location. Due to privacy concerns, real data is rarely used for this purpose, and a synthetic population representative of the study area is used instead. Section 3.2.1 discusses the population used in more detail.\nActivitySim, like all ABMs, simulates transportation decisions on an individual level. ActivitySim has a hierarchical decision tree, where long-term decisions (such as auto ownership and telecommute frequency) are made first, followed by daily and tour- and trip-level decisions such as scheduling and mode choice (see Figure 3.1). Each of these steps determines information that will be used in subsequent steps, and many steps can be turned on or off depending on what is needed for the model implementation.\n\n\n\n\n\n\nFigure 3.1: ActivitySim sub-model flowchart. Long-term decisions are made first, followed by more granular ones.\n\n\n\nThe steps can broadly be categorized into five groups, as shown in Figure 3.1: aggregate, household/personal, daily, tour-level, and trip-level steps. The aggregate steps mainly involve determining impedance measures between each pair of zones (travel time, distance, cost, etc.). In this case, these impedances are supplied directly as network skims, output from the WFRC model.\nThe household/personal steps relate to long-term decisions that are unlikely to change quickly based on daily transportation conditions. These steps include determining remote work status, work/school location, auto ownership, transit pass ownership, and free parking availability at work. Much of this information can be supplied directly or explicitly modeled. This ActivitySim implementation does not supply any of this information directly, and explicitly models remote work status, work/school location, auto ownership, and free parking availability. Transit pass ownership is not modeled.\nThe daily decisions primarily concern an individual’s DAP. ActivitySim contains a step to assign mandatory, non-mandatory, and joint tours based on personal and household information (joint tours combine both mandatory and non-mandatory activities). For example, full-time workers are more likely to make a mandatory tour than part-time workers, all else being equal.\nOnce a DAP is chosen, ActivitySim creates tours for each major activity in the day. Additionally, ActivitySim determines if an individual makes an “at-work” tour (e.g. leaving for lunch and returning to the workplace). Each tour is scheduled and assigned a primary mode, as well as a primary destination for non-mandatory and joint tours. The tours are then populated with trips, and ActivitySim assigns each trip a purpose, destination, time of day, and mode compatible with the tour-level assignment.\nThe final steps of ActivitySim are writing output trip matrices and other tables, including information on land use, persons, households, tours, and trips. \n\nPopulationSim\nThis paper uses PopulationSim (Association of Metropolitan Planning Organizations 2023) to create a synthetic population for ActivitySim. The synthetic population aims to be representative of the study area while maintaining privacy. PopulationSim takes as input a “seed” of individuals and households, and populates the area with copies of these to match given control totals.\nThe seed sample comes from the 2019 American Community Survey Public Use Microdata Sample (U.S. Census Bureau 2022), which contains a sample of actual (anonymized) individuals and households at the Public Use Microdata Area (PUMA) geography (PUMAs partition the United States into areas of around 100,000 people each (U.S. Census Bureau 2023)). The control totals come from two different sources: the U.S. Census and the WFRC model. Table 3.1 shows these controls as well as their geographic level and source. PopulationSim also allows setting different weights to each control, and Table 3.1 gives this information as well.\n\n\n\n\nTable 3.1: PopulationSim Control Totals by Geography and Source\n\n\n\n\n\n\n\nControl\nGeography\nSource\nWeight\n\n\n\n\nPopulation\nEntire Region\nCensus\n5,000\n\n\nNumber of Households\nTAZ\nWFRC Model\n1,000,000,000\n\n\nHousehold Size\nCensus Tract\nCensus\n10,000\n\n\nPersons by Age Group\nCensus Tract\nCensus\n10,000\n\n\nHouseholds by Income Group\nCensus Tract\nCensus\n500\n\n\nWorkers per Household\nCensus Tract\nCensus\n1,000\n\n\n\n\n\n\n\n\n\n\n\nMost of these controls come from Census data, with only the number of households per TAZ coming from the WFRC model data. Note also that there are many personal and household variables that are not accounted for in these controls, such as sex, vehicle ownership, internet access, etc. These variables are not controlled for and are dependent on which seed persons or households are copied in controlling for the other variables. However, this process is assumed to still give a representative enough estimate for the uncontrolled variables without needing to model them explicitly.\nThe outputs of PopulationSim include a persons and households table comprising the synthetic population, as well as summary tables.",
+    "text": "3.2 ActivitySim\nActivitySim is an open-source ABM led by a consortium of transportation planning agencies. ActivitySim is highly configurable, and many agencies have their own bespoke implementation. This paper uses an ActivitySim implementation based on Gregory S. Macfarlane and Nathan J. Lant (2021), which is in turn based on the prototype configuration for the Metropolitan Transportation Commission serving the San Francisco area (Erhardt et al. 2011).\nActivitySim requires similar inputs to the WFRC model, though it does not assign traffic and so does not require any transportation networks. However, ActivitySim does require network skims for information on travel time, cost, etc. These skims are obtained from any network assignment process, though ActivitySim itself does not include network assignment. A discussion and comparison of network assignment processes is outside the scope of this project, so this ActivitySim implementation uses the travel skims output from the WFRC model directly.\nActivitySim additionally requires population data at an individual level, including information such as age, household income, and home location. Due to privacy concerns, real data is rarely used for this purpose, and a synthetic population representative of the study area is used instead. Section 3.2.1 discusses the population used in more detail.\nActivitySim, like all ABMs, simulates transportation decisions on an individual level. ActivitySim has a hierarchical decision tree, where long-term decisions (such as auto ownership and telecommute frequency) are made first, followed by daily and tour- and trip-level decisions such as scheduling and mode choice (see Figure 3.1). Each of these steps determines information that will be used in subsequent steps, and many steps can be turned on or off depending on what is needed for the model implementation.\n\n\n\n\n\n\nFigure 3.1: ActivitySim sub-model flowchart. Long-term decisions are made first, followed by more granular ones.\n\n\n\nThe steps can broadly be categorized into five groups, as shown in Figure 3.1: aggregate, household/personal, daily, tour-level, and trip-level steps. The aggregate steps mainly involve determining impedance measures between each pair of zones (travel time, distance, cost, etc.). In this case, these impedances are supplied directly as network skims, output from the WFRC model.\nThe household/personal steps relate to long-term decisions that are unlikely to change quickly based on daily transportation conditions. These steps include determining remote work status, work/school location, auto ownership, transit pass ownership, and free parking availability at work. Much of this information can be supplied directly or explicitly modeled. This ActivitySim implementation does not supply any of this information directly, and explicitly models remote work status, work/school location, auto ownership, and free parking availability. Transit pass ownership is not modeled.\nThe daily decisions primarily concern an individual’s DAP. ActivitySim contains a step to assign mandatory, non-mandatory, and joint tours based on personal and household information (joint tours combine both mandatory and non-mandatory activities). For example, full-time workers are more likely to make a mandatory tour than part-time workers, all else being equal.\nOnce a DAP is chosen, ActivitySim creates tours for each major activity in the day. Additionally, ActivitySim determines if an individual makes an “at-work” tour (e.g. leaving for lunch and returning to the workplace). Each tour is scheduled and assigned a primary mode, as well as a primary destination for non-mandatory and joint tours. The tours are then populated with trips, and ActivitySim assigns each trip a purpose, destination, time of day, and mode compatible with the tour-level assignment.\nThe final steps of ActivitySim are writing output trip matrices and other tables, including information on land use, persons, households, tours, and trips. \n\nPopulationSim\nThis paper uses PopulationSim (Association of Metropolitan Planning Organizations 2023) to create a synthetic population for ActivitySim. The synthetic population aims to be representative of the study area while maintaining privacy. PopulationSim takes as input a “seed” of individuals and households, and populates the area with copies of these to match given control totals.\nThe seed sample comes from the 2019 American Community Survey Public Use Microdata Sample (U.S. Census Bureau 2022), which contains a sample of actual (anonymized) individuals and households at the Public Use Microdata Area (PUMA) geography (PUMAs partition the United States into areas of around 100,000 people each (U.S. Census Bureau 2023)). The control totals come from two different sources: the U.S. Census and the WFRC model. Table 3.1 shows these controls as well as their geographic level and source. PopulationSim also allows setting different weights to each control, and Table 3.1 gives this information as well.\n\n\n\n\nTable 3.1: PopulationSim Control Totals by Geography and Source\n\n\n\n\n\n\n\nControl\nGeography\nSource\nWeight\n\n\n\n\nPopulation\nEntire Region\nCensus\n5,000\n\n\nNumber of Households\nTAZ\nWFRC Model\n1,000,000,000\n\n\nHousehold Size\nCensus Tract\nCensus\n10,000\n\n\nPersons by Age Group\nCensus Tract\nCensus\n10,000\n\n\nHouseholds by Income Group\nCensus Tract\nCensus\n500\n\n\nWorkers per Household\nCensus Tract\nCensus\n1,000\n\n\n\n\n\n\n\n\n\n\n\nMost of these controls come from Census data, with only the number of households per TAZ coming from the WFRC model data. Note also that there are many personal and household variables that are not accounted for in these controls, such as sex, vehicle ownership, internet access, etc. These variables are not controlled for and are dependent on which seed persons or households are copied in controlling for the other variables. However, this process is assumed to still give a representative enough estimate for the uncontrolled variables without needing to model them explicitly.\nThe outputs of PopulationSim include a persons and households table comprising the synthetic population, as well as summary tables.",
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       "<span class='chapter-number'>3</span>  <span class='chapter-title'>Methodology</span>"
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     "href": "qmd/methods.html#initial-model-comparisoncalibration",
     "title": "3  Methodology",
     "section": "3.3 Initial Model Comparison/Calibration",
-    "text": "3.3 Initial Model Comparison/Calibration\nWhile this research does not directly compare the outputs of ActivitySim to those of the WFRC model, it is important to ensure similar performance between the two models for meaningful analyses. As such, a baseline scenario in both models is used in order to calibrate the ActivitySim implementation to the WFRC model. This baseline scenario uses the 2019 WFRC model as-is. For ActivitySim, the baseline scenario uses 2019 Census and WFRC data to create the synthetic population, and the choice models use land use data and network skims from the baseline WFRC scenario.\n\nVerification of the Synthetic Population\nThe controls for PopulationSim mostly come from the Census, as can be seen in Table 3.1. However, the WFRC model contains TAZ-level data including population and median income. The WFRC model also has a disaggregation step that estimates the number of households by size and income group. \nThis section compares the output of PopulationSim to the WFRC model on each of these variables at the TAZ level.\nFigure 3.2 shows the difference in TAZ population between PopulationSim and the WFRC data. It is worth noting that since the number of households was controlled at the TAZ level from the WFRC data with an extremely high weight, the number of households per TAZ in the synthetic population match exactly to the WFRC data. The average household size will therefore follow a similar error distribution to the one shown in Figure 3.2.\n\n\n\n\n\n\n\n\nFigure 3.2: population comparison\n\n\n\n\n\nThe population per TAZ is similar to the WFRC data in most places, though there are some discrepancies especially near Herriman and Lehi. Since total population is a region-level control, but number of households is a TAZ-level control, this shows PopulationSim is predicting a smaller average household size in Herriman and Lehi than the WFRC data suggests. \nIncome is also an important factor in travel behavior (Zegras and Srinivasan 2007), and Figure 3.3 shows a TAZ-level comparison of median income between the synthetic population and the WFRC data. The synthetic population does have a lower median income than the WFRC data in many TAZs, but the error is in most cases fairly small, especially in more populated areas. However, both the WFRC model and ActivitySim use household income groups rather than individual household income to inform travel decisions. These groups are taken from the WFRC model (see Table 3.2), and the groups in PopulationSim and ActivitySim were adjusted to match. Figure 3.4 shows the difference in number of households by income group, and this figure shows a similar trend of PopulationSim over-predicting low-income households.\n\n\n\n\nTable 3.2: income groups\n\n\n\n\n\n\n\nIncome Group\nIncome Range\n\n\n\n\n1\n≤ $45,000\n\n\n2\n$45,000–$75,000\n\n\n3\n$75,000–$125,000\n\n\n4\n≥ $125,000\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3.3: med inc\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3.4: inc groups\n\n\n\n\n\nIncome was not weighted very heavily as a control in PopulationSim (see Table 3.1), and this is in part why there are discrepancies between the models. However, the overall distribution of income is similar between the models, as Figure 3.5 shows. \n\n\n\n\n\n\n\n\nFigure 3.5: inc density\n\n\n\n\n\n\n\n\nVerification and Calibration of ActivitySim\nThis section compares the outputs of both models to verify that trip patterns roughly agree between them. There are three comparisons of interest between the outputs of the two models: mode split, trip length frequency distribution, and remote work.\nThe initial baseline ActivitySim scenario predicted a mode split significantly different to that from the WFRC model, and so calibration efforts were needed. We adjusted the alternative-specific constants in ActivitySim’s mode choice model to better match the mode split in the WFRC model. Table 3.3 compares the mode split of both models after several iterations of this calibration. Overall, the calibration resulted in a reasonably similar mode split between the two models, though there are still discrepancies (e.g. ActivitySim is predicting about twice as many transit trips as the WFRC model). However, further adjustment of the constants would cause their values to become unreasonably large, as ActivitySim’s mode split begins converging at this point regardless of the calibration constant values (see Figure 3.6). This is likely due to the mode choice coefficients being unrepresentative of the study area. This ActivitySim configuration is ultimately based on the San Francisco area, and so coefficients on variables such as travel time and income are calibrated for that area (hence in part why there are so many more transit trips).\n\n\n\n\nTable 3.3: mode split\n\n\n\n\n\n\n\nPurpose\nMode\nActivitySim predicted trips\nWFRC predicted trips\n% Error\n% Error (scaled to WFRC predicted trips)\n\n\n\n\nAuto\nHome-based Other\n3042754\n4096688\n-1053934\n-25.7%\n\n\nNon-motorized\nHome-based Other\n591164\n510143\n81021\n15.9%\n\n\nTransit\nHome-based Other\n218438\n37346\n181092\n484.9%\n\n\nAuto\nHome-based Work\n1288620\n1586414\n-297794\n-18.8%\n\n\nNon-motorized\nHome-based Work\n191880\n76506\n115374\n150.8%\n\n\nTransit\nHome-based Work\n189610\n48752\n140858\n288.9%\n\n\nAuto\nNon–home-based\n1645356\n2224878\n-579522\n-26.0%\n\n\nNon-motorized\nNon–home-based\n224248\n146404\n77844\n53.2%\n\n\nTransit\nNon–home-based\n103398\n13453\n89945\n668.6%\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3.6: calibration plot\n\n\n\n\n\nFigure 3.7 compares the trip length frequency distribution of the two models by mode and purpose. Both ActivitySim and the WFRC model contain destination choice models which can be adjusted to affect the distribution of trip length. However, as the figure shows, the two models have similar TLFDs, so no adjustment was necessary. The most significant discrepancies are with transit trips, again likely due to this configuration of ActivitySim being calibrated to San Francisco, making transit more attractive. Note that though these distributions match well enough for the purposes of this research, further calibration would be required to create a production-ready ActivitySim implementation.\n\n\n\n\n\n\n\n\nFigure 3.7: tlfd comparison\n\n\n\n\n\nThe WFRC model has basic support for predicting telecommuting and work-from-home trips. This includes a lookup table of telecommute percentages based on job type and year. ActivitySim also has this functionality, and can additionally use individual- and household-level variables in its predictions. It is worth noting that both the WFRC model and ActivitySim make a distinction between “telecommuting”, where an individual commutes to work some days and does not others, and “work-from-home” (or “home-based jobs” in the WFRC model), where an individual’s workplace is always at their home.\nwfh and telecommute_freq models, taken from SEMCOG prototype. had to adjust to work with MTC, not too bad. Also added job category coefficients to tf model, calibrated to wfrc percent (mention in methodology)\nBoth models treat “work-from-home”/“home-based jobs” similarly. The WFRC model’s land use data contains employment by type in each TAZ, and it considers a “home-based job” as a separate job type, so these are not counted toward employment totals in trip generation and subsequent steps. \nActivitySim has a “work from home” submodel which assigns workers work-from-home status based on personal variables such as income, sex, and education (coefficients on these variables were left unchanged from the existing configuration). There is also a “target work-from-home percent” value that adjusts the model to reach the specified work-from-home proportion of all workers. Individuals with work-from-home status are then prohibited from making a mandatory tour. This target work-from-home percentage is set at 2.3%, based on a weighted average from the WFRC model data. There are no other adjustments to the ActivitySim work-from-home submodel.\n\n\n\n\n\n\nThe two models differ in their approach to telecommuting, however. The WFRC model has a lookup table of telecommuting shares based on job type (see Table 3.4), including predictions for future years. ActivitySim has a “telecommute frequency” submodel which assigns workers a telecommute status indicating the number of days they work remotely per week. Based on this status, ActivitySim adjusts the likelihood of making a mandatory tour. Telecommute status depends on personal variables similar to those in the work-from-home submodel by default. In order to calibrate to the WFRC data, however, we added additional job type variables to ActivitySim to match those given in Table 3.4. Because these are choice coefficients rather than target percentages, the values needed to be calibrated to match the WFRC targets. The calibration allowed ActivitySim to match these targets exactly, and the coefficients are given in Table 3.4.\n\n\n\n\nTable 3.4: wfrc telecommute data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nActivitySim Telecommute Utility Coefficients\n\n\n\nJob Type\nWFRC Telecommute Proportion\n1 day\n2–3 days\n4 days\n\n\n\n\nRetail\n2.70%\n0.312\n0.125\n0.078\n\n\nFood\n1.87%\n-0.368\n-0.148\n-0.092\n\n\nManufacturing\n2.02%\n0.038\n0.015\n0.010\n\n\nOffice\n6.66%\n1.782\n0.712\n0.445\n\n\nGov't/Education\n1.67%\n-0.560\n-0.224\n-0.140\n\n\nHealth\n2.86%\n0.158\n0.063\n0.039\n\n\nAgriculture\n6.93%\n2.262\n0.904\n0.566\n\n\nMining\n0.53%\n-2.030\n-0.810\n-0.511\n\n\nConstruction\n3.28%\n0.816\n0.326\n0.204\n\n\nOther\n5.37%\n1.535\n0.614\n0.384",
+    "text": "3.3 Initial Model Comparison/Calibration\nWhile this research does not directly compare the outputs of ActivitySim to those of the WFRC model, it is important to ensure similar performance between the two models for meaningful analyses. As such, a baseline scenario in both models is used in order to calibrate the ActivitySim implementation to the WFRC model. This baseline scenario uses the 2019 WFRC model as-is. For ActivitySim, the baseline scenario uses 2019 Census and WFRC data to create the synthetic population, and the choice models use land use data and network skims from the baseline WFRC scenario.\n\nVerification of the Synthetic Population\nThe controls for PopulationSim mostly come from the Census, as can be seen in Table 3.1. However, the WFRC model contains TAZ-level data including population and median income. The WFRC model also has a disaggregation step that estimates the number of households by size and income group. \nThis section compares the output of PopulationSim to the WFRC model on each of these variables at the TAZ level.\nFigure 3.2 shows the difference in TAZ population between PopulationSim and the WFRC data. It is worth noting that since the number of households was controlled at the TAZ level from the WFRC data with an extremely high weight, the number of households per TAZ in the synthetic population match exactly to the WFRC data. The average household size will therefore follow a similar error distribution to the one shown in Figure 3.2.\n\n\n\n\n\n\n\n\nFigure 3.2: population comparison\n\n\n\n\n\nThe population per TAZ is similar to the WFRC data in most places, though there are some discrepancies especially near Herriman and Lehi. Since total population is a region-level control, but number of households is a TAZ-level control, this shows PopulationSim is predicting a smaller average household size in Herriman and Lehi than the WFRC data suggests. \nIncome is also an important factor in travel behavior (Zegras and Srinivasan 2007), and Figure 3.3 shows a TAZ-level comparison of median income between the synthetic population and the WFRC data. The synthetic population does have a lower median income than the WFRC data in many TAZs, but the error is in most cases fairly small, especially in more populated areas. However, both the WFRC model and ActivitySim use household income groups rather than individual household income to inform travel decisions. These groups are taken from the WFRC model (see Table 3.2), and the groups in PopulationSim and ActivitySim were adjusted to match. Figure 3.4 shows the difference in number of households by income group, and this figure shows a similar trend of PopulationSim over-predicting low-income households.\n\n\n\n\nTable 3.2: income groups\n\n\n\n\n\n\n\nIncome Group\nIncome Range\n\n\n\n\n1\n≤ $45,000\n\n\n2\n$45,000–$75,000\n\n\n3\n$75,000–$125,000\n\n\n4\n≥ $125,000\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3.3: med inc\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3.4: inc groups\n\n\n\n\n\nIncome was not weighted very heavily as a control in PopulationSim (see Table 3.1), and this is in part why there are discrepancies between the models. However, the overall distribution of income is similar between the models, as Figure 3.5 shows. \n\n\n\n\n\n\n\n\nFigure 3.5: inc density\n\n\n\n\n\n\n\n\nVerification and Calibration of ActivitySim\nThis section compares the outputs of both models to verify that trip patterns roughly agree between them. There are three comparisons of interest between the outputs of the two models: mode split, trip length frequency distribution, and remote work.\nThe initial baseline ActivitySim scenario predicted a mode split significantly different to that from the WFRC model, and so calibration efforts were needed. We adjusted the alternative-specific constants in ActivitySim’s mode choice model to better match the mode split in the WFRC model. Table 3.3 compares the mode split of both models after several iterations of this calibration. Overall, the calibration resulted in a reasonably similar mode split between the two models, though there are still discrepancies (e.g. ActivitySim is predicting about twice as many transit trips as the WFRC model). However, further adjustment of the constants would cause their values to become unreasonably large, as ActivitySim’s mode split begins converging at this point regardless of the calibration constant values (see Figure 3.6). This is likely due to the mode choice coefficients being unrepresentative of the study area. This ActivitySim configuration is ultimately based on the San Francisco area, and so coefficients on variables such as travel time and income are calibrated for that area (hence in part why there are so many more transit trips).\n\n\n\n\nTable 3.3: mode split\n\n\n\n\n\n\n\nPurpose\nMode\nActivitySim predicted trips\nWFRC predicted trips\n% Error\n% Error (scaled to WFRC predicted trips)\n\n\n\n\nAuto\nHome-based Other\n3042754\n4096688\n-1053934\n-25.7%\n\n\nNon-motorized\nHome-based Other\n591164\n510143\n81021\n15.9%\n\n\nTransit\nHome-based Other\n218438\n37346\n181092\n484.9%\n\n\nAuto\nHome-based Work\n1288620\n1586414\n-297794\n-18.8%\n\n\nNon-motorized\nHome-based Work\n191880\n76506\n115374\n150.8%\n\n\nTransit\nHome-based Work\n189610\n48752\n140858\n288.9%\n\n\nAuto\nNon–home-based\n1645356\n2224878\n-579522\n-26.0%\n\n\nNon-motorized\nNon–home-based\n224248\n146404\n77844\n53.2%\n\n\nTransit\nNon–home-based\n103398\n13453\n89945\n668.6%\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3.6: calibration plot\n\n\n\n\n\nFigure 3.7 compares the trip length frequency distribution of the two models by mode and purpose. Both ActivitySim and the WFRC model contain destination choice models which can be adjusted to affect the distribution of trip length. However, as the figure shows, the two models have similar TLFDs, so no adjustment was necessary. The most significant discrepancies are with transit trips, again likely due to this configuration of ActivitySim being calibrated to San Francisco, making transit more attractive. Note that though these distributions match well enough for the purposes of this research, further calibration would be required to create a production-ready ActivitySim implementation.\n\n\n\n\n\n\n\n\nFigure 3.7: tlfd comparison\n\n\n\n\n\nThe WFRC model has basic support for predicting telecommuting and work-from-home trips. This includes a lookup table of telecommute percentages based on job type and year. ActivitySim also has this functionality, and can additionally use individual- and household-level variables in its predictions. It is worth noting that both the WFRC model and ActivitySim make a distinction between “telecommuting”, where an individual commutes to work some days and does not others, and “work-from-home” (or “home-based jobs” in the WFRC model), where an individual’s workplace is always at their home.\nwfh and telecommute_freq models, taken from SEMCOG prototype.\nBoth models treat “work-from-home”/“home-based jobs” similarly. The WFRC model’s land use data contains employment by type in each TAZ, and it considers a “home-based job” as a separate job type, so these are not counted toward employment totals in trip generation and subsequent steps. \nActivitySim has a “work from home” submodel which assigns workers work-from-home status based on personal variables such as income, sex, and education (coefficients on these variables were left unchanged from the existing configuration). There is also a “target work-from-home percent” value that adjusts the model to reach the specified work-from-home proportion of all workers. Individuals with work-from-home status are then prohibited from making a mandatory tour. This target work-from-home percentage is set at 2.3%, based on a weighted average from the WFRC model data. There are no other adjustments to the ActivitySim work-from-home submodel.\nThe two models differ in their approach to telecommuting, however. The WFRC model has a lookup table of telecommuting shares based on job type (see Table 3.4), including predictions for future years. ActivitySim has a “telecommute frequency” submodel which assigns workers a telecommute status indicating the number of days they work remotely per week. Based on this status, ActivitySim adjusts the likelihood of making a mandatory tour. Telecommute status depends on personal variables similar to those in the work-from-home submodel by default. In order to calibrate to the WFRC data, however, we added additional job type variables to ActivitySim to match those given in Table 3.4. Because these are choice coefficients rather than target percentages, the values needed to be calibrated to match the WFRC targets. The calibration allowed ActivitySim to match these targets exactly, and the coefficients are given in Table 3.4.\n\n\n\n\nTable 3.4: wfrc telecommute data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nActivitySim Telecommute Utility Coefficients\n\n\n\nJob Type\nWFRC Telecommute Proportion\n1 day\n2–3 days\n4 days\n\n\n\n\nRetail\n2.70%\n0.312\n0.125\n0.078\n\n\nFood\n1.87%\n-0.368\n-0.148\n-0.092\n\n\nManufacturing\n2.02%\n0.038\n0.015\n0.010\n\n\nOffice\n6.66%\n1.782\n0.712\n0.445\n\n\nGov't/Education\n1.67%\n-0.560\n-0.224\n-0.140\n\n\nHealth\n2.86%\n0.158\n0.063\n0.039\n\n\nAgriculture\n6.93%\n2.262\n0.904\n0.566\n\n\nMining\n0.53%\n-2.030\n-0.810\n-0.511\n\n\nConstruction\n3.28%\n0.816\n0.326\n0.204\n\n\nOther\n5.37%\n1.535\n0.614\n0.384",
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     "href": "qmd/methods.html#example-scenarios",
     "title": "3  Methodology",
     "section": "3.4 Example Scenarios",
-    "text": "3.4 Example Scenarios\nThere are three proposed scenarios to implement and run in each model for comparison. \nThis is not a comprehensive list covering all potential scenario possibilities, but is taken to represent many of the types of scenarios an agency would analyze.\nThe first scenario involves a change in land use near the former state prison site in Draper, Utah. Current plans for this site involve new development known as “The Point”, which will add high-density housing and commercial development to the area. This research scenario will be based on this development, but will include only the land use changes. The actual development plans also include expansion of transit, but this will not be a part of this scenario.\nThe second scenario centers around an augmentation of transit service along the Wasatch Front. The FrontRunner, a CRT line connecting Provo to Ogden, is slated for expansion. The expansion includes additional stations and increased travel speeds due to vehicle electrification. This scenario models these changes in accordance with the planned expansion of the service.\nThe third scenario addresses the growing trend of remote work. Given technological advancements and the notable surge in remote work during the COVID-19 pandemic, this scenario models a substantial increase in remote work based on projections from WFRC.\nEach of these scenarios is based on the baseline 2019 scenario in the respective model, and ignores any additional expected growth or development beyond the specific changes of each scenario. For example, the increased WFH scenario uses WFH projections from 2050, but land use and socioeconomic data from 2019. These scenarios are therefore not realistic, but they serve as illustrative examples of the types of planning and development scenarios agencies may wish to analyze.\nAll three of these scenarios are coded in both the WFRC model and ActivitySim. The results (Chapters 4–6) describe the process of coding each scenario and analyzing them, as well as the analyses themselves.\n\n\n\n\nAssociation of Metropolitan Planning Organizations. 2023. “PopulationSim.” https://activitysim.github.io/populationsim.\n\n\nBentley Systems. n.d. “CUBE Modeling Software.” Accessed October 3, 2023. https://www.bentley.com/software/cube/.\n\n\nErhardt, Greg, David Ory, San Francisco, Amar Sarvepalli, Joel Freedman, Jeff Hood, and Ben Stabler. 2011. “MTC’s Travel Model One: Applications of an Activity‐Based Model in Its First Year.” Innovations in Travel Modeling 2012, November.\n\n\nGregory S. Macfarlane, and Nathan J. Lant. 2021. “Estimation and Simulation of Daily Activity Patterns for Individuals Using Wheelchairs.” Final UT-21.10. Utah Department of Transportation. https://drive.google.com/file/d/1PMJ_7zOPqghjelPl7QdwAB_fK-0RLmx4/view?usp=sharing&usp=embed_facebook.\n\n\nU.S. Census Bureau. 2022. “Public Use Microdata Sample (PUMS).” Census.gov. https://www.census.gov/programs-surveys/acs/microdata.html.\n\n\n———. 2023. “Public Use Microdata Areas (PUMAs).” Census.gov. https://www.census.gov/programs-surveys/geography/guidance/geo-areas/pumas.html.\n\n\nZegras, P. Christopher, and Sumeeta Srinivasan. 2007. “Household Income, Travel Behavior, Location, and Accessibility: Sketches from Two Different Developing Contexts.” Transportation Research Record 2038 (1): 128–38. https://doi.org/10.3141/2038-17.",
+    "text": "3.4 Example Scenarios\nThere are three proposed scenarios to implement and run in each model for comparison. This is not a comprehensive list covering all potential scenario possibilities, but is taken to represent many of the types of scenarios an agency would analyze.\nThe first scenario involves a change in land use near the former state prison site in Draper, Utah. Current plans for this site involve new development known as “The Point”, which will add high-density housing and commercial development to the area. This research scenario will be based on this development, but will include only the land use changes. The actual development plans also include expansion of transit, but this will not be a part of this scenario.\nThe second scenario centers around an augmentation of transit service along the Wasatch Front. The FrontRunner, a CRT line connecting Provo to Ogden, is slated for expansion. The expansion includes additional stations and increased travel speeds due to vehicle electrification. This scenario models these changes in accordance with the planned expansion of the service.\nThe third scenario addresses the growing trend of remote work. Given technological advancements and the notable surge in remote work during the COVID-19 pandemic, this scenario models a substantial increase in remote work based on projections from WFRC.\nEach of these scenarios is based on the baseline 2019 scenario in the respective model, and ignores any additional expected growth or development beyond the specific changes of each scenario. For example, the increased WFH scenario uses WFH projections from 2050, but land use and socioeconomic data from 2019. These scenarios are therefore not realistic, but they serve as illustrative examples of the types of planning and development scenarios agencies may wish to analyze.\nAll three of these scenarios are coded in both the WFRC model and ActivitySim. The results (Chapters 4–6) describe the process of coding each scenario and analyzing them, as well as the analyses themselves.\n\n\n\n\nAssociation of Metropolitan Planning Organizations. 2023. “PopulationSim.” https://activitysim.github.io/populationsim.\n\n\nBentley Systems. n.d. “CUBE Modeling Software.” Accessed October 3, 2023. https://www.bentley.com/software/cube/.\n\n\nErhardt, Greg, David Ory, San Francisco, Amar Sarvepalli, Joel Freedman, Jeff Hood, and Ben Stabler. 2011. “MTC’s Travel Model One: Applications of an Activity‐Based Model in Its First Year.” Innovations in Travel Modeling 2012, November.\n\n\nGregory S. Macfarlane, and Nathan J. Lant. 2021. “Estimation and Simulation of Daily Activity Patterns for Individuals Using Wheelchairs.” Final UT-21.10. Utah Department of Transportation. https://drive.google.com/file/d/1PMJ_7zOPqghjelPl7QdwAB_fK-0RLmx4/view?usp=sharing&usp=embed_facebook.\n\n\nU.S. Census Bureau. 2022. “Public Use Microdata Sample (PUMS).” Census.gov. https://www.census.gov/programs-surveys/acs/microdata.html.\n\n\n———. 2023. “Public Use Microdata Areas (PUMAs).” Census.gov. https://www.census.gov/programs-surveys/geography/guidance/geo-areas/pumas.html.\n\n\nZegras, P. Christopher, and Sumeeta Srinivasan. 2007. “Household Income, Travel Behavior, Location, and Accessibility: Sketches from Two Different Developing Contexts.” Transportation Research Record 2038 (1): 128–38. https://doi.org/10.3141/2038-17.",
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@@ -104,7 +104,7 @@
     "href": "qmd/1_land_use.html",
     "title": "4  Scenario 1: Change in Land Use",
     "section": "",
-    "text": "4.1 Scenario Creation\nIn the WFRC model the change is trivial to implement. We just copy/pasted the se data from 2050 to 2019 for the affected zones.\nThis zonal data is used directly in the WFRC model, but a new synthetic population is needed for ActivitySim. We used the zonal data as new targets….\nOnly created a new synthetic population for the 5 zones\nUsed census targets from Gateway area in SLC\nActivitySim requires two things for this scenario: updated SE data and a new pop. The updated SE data is the same as in the WFRC scenario, so is also trivial to implement. The pop, however, is not. New controls.\nNew population for only the affected area, then joined to existing. There were no existing households so we didn’t need to remove any (we’re not double-counting).\n? Note in realistic scenario you could use just the land use forecast as controls directly, rahter than the whole census shenanigains but we didn’t do that.\nThis is trivial to implement in the WFRC/MAG model, where all that is needed is a change in the TAZ- level land use data file for the affected TAZs. The new land use data is the WFRC/MAG predicted 2050 data for these zones, as this is when development of the site is scheduled for completion. That is, the 2050 socioeconomic data for these five zones (only) was brought into the 2019 scenario. This added 7,430 new households and 22,200 new jobs to the area.\nIn ActivitySim, a modified synthetic population with these new households is needed. Currently, the synthetic population model (PopulationSim) is set up to use Census tract data for many control variables including income. At the suggestion of Bert Granberg, the BYU team used the Census tract covering the Gateway area in SLC to represent the new development patterns at The Point. The income distribution, etc. of this area will therefore match that of the Gateway area, though the number of households and zonal SE data will match the WFRC projections for 2050. The new synthetic population was run for this new development only and added to the base scenario population",
+    "text": "4.1 Scenario Creation\nIn the WFRC model, this change is trivial to implement. The model uses the land use/socioeconomic data directly, so the only adjustment needed is replacing the data for the specific TAZs with the 2050 data. As noted previously, all other TAZs have the same land use data as in the 2019 baseline scenario.\nActivitySim requires two changes for this scenario. The first is an update to the TAZ-level land use and socioeconomic data, which is identical to the process for the WFRC model. The second is an updated synthetic population. In order to keep consistency between model scenarios, a new population was created only for the 5 affected TAZs and joined to the existing synthetic population. There were no individuals or households in the affected zones in the existing synthetic population, so no individuals or households needed to be removed before joining the two populations.\nCreating the new synthetic population followed a similar process as in the baseline scenario (Section 3.2.1), but used the new land use data as new TAZ-level controls. However, many of the controls for PopulationSim use tract-level data from the Census, and existing Census data for these controls is unrepresentative of the new development. Because of this, the Census tract covering the Gateway area in Salt Lake City (see ?fig-gateway-tract) is used to represent the new development patterns at The Point. The income distribution, etc. of this area will therefore match that of the Gateway area, though the TAZ-level controls and land use/socioeconomic data will match the WFRC projections for 2050.\n? Note in realistic scenario you could use just the land use forecast as controls directly, rahter than the whole census shenanigans but we didn’t do that since we wanted the models to be independent.",
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@@ -114,7 +114,7 @@
     "href": "qmd/1_land_use.html#scenario-analysis",
     "title": "4  Scenario 1: Change in Land Use",
     "section": "4.2 Scenario Analysis",
-    "text": "4.2 Scenario Analysis\nWe looked at person-miles for both scenarios. Figure 4.2 shows the increase in person-miles for the ABM. Shaded based on “in-zone”. (Not many transit trips since we didn’t add transit). Figure 4.3 shows for TBM.\nIn a trip-based model, it is possible to calculate VMT/person-miles traveled/etc. from trips produced in and/or attracted to the new development zones (see Figure 4). Additionally, Figure 5 shows desire lines for trips produced in these zones. However, those living in the new development make many more trips than only those produced in their home zone. Figure 6 shows a desire line plot of all trips taken by the newly-added households in ActivitySim, and Figure 7 shows the distribution of these trips based on their production zone. Though trip-based models do include non–home-based trips, it is difficult to model accurately where and by whom these trips are made. Analyzing these trips and capturing the full impact of new development in this way is only possible in an ABM. Note that in a trip-based model, a round-trip from home to work and back is regarded as two trips produced in the home zone. ActivitySim does not deal with productions and attractions in the same way, so we are counting a trip with an origin or destination in the home zone as produced by that zone e.g. in Figure 6Figures 6 Figure 7and 7.\n\n\n\n\n\n\n\n\nFigure 4.2: personmiles abm\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4.3: personmiles tbm\n\n\n\n\n\nCan’t really shade since nhb relocation happens before mode choice. Best you can do is difference the NHB matrices (Figure 4.4 (2-panel)) (If there are nhb trips in/out of zones): clearly some of these are not nhb from residents since they occur in these zones.\nAn output of the trip-based model is a large trip matrix that contains the number of trips that were produced and attracted between each zone. Along with this matrix, there are matrices showing productions and attractions based on each mode and each trip purpose. By selecting just the zones we wanted to analyze, we could see how many trips the new development produced and attracted, and we could see what modes and purposes were connected to those trips. To visualize this, we used the matrix file and the TAZ shape file to create desire lines as seen in (some figure we can add).\nImplications: To compare this to the base scenario, we simply subtracted the base scenario trip matrix by the trip matrix from the land use scenario to get desire lines showing the increase in trips. As could be reasonably expected, we saw that more trips were produced and attracted by the zones containing the new land use area. We were also able to get an idea of where each of the trips to and from the area were coming from or going to, and what types of modes where being used.\nTalk about the numbers we found and describe the results.\nLimitations: While we could see the different trips leaving and entering the new development, we weren’t able to see what types of people were making the trips. We also couldn’t tell if those trips were part of a series of trips made by the same person.\n\n\n\n\n\n\n\n\n\n\n\n(a) hb\n\n\n\n\n\n\n\n\n\n\n\n(b) nhb\n\n\n\n\n\n\nFigure 4.4: lu desire tbm\n\n\n\n\nWith abm, you can follow people and trace their exact paths (Figure 4.5). Implications: Along with seeing where each trip produced in the new development was going, the activity-based model allowed us to see how trips from the development were related to other trips in a travel tour. With the synthetic population, we were also able to see which types of people were taking trips from the new development.\n\n\n\n\n\n\n\n\nFigure 4.5: desire abm",
+    "text": "4.2 Scenario Analysis\nWe looked at person-miles for both scenarios. Figure 4.3 shows the increase in person-miles for the ABM. Shaded based on “in-zone”. (Not many transit trips since we didn’t add transit). Figure 4.4 shows for TBM.\nIn a trip-based model, it is possible to calculate VMT/person-miles traveled/etc. from trips produced in and/or attracted to the new development zones (see Figure 4). Additionally, Figure 5 shows desire lines for trips produced in these zones. However, those living in the new development make many more trips than only those produced in their home zone. Figure 6 shows a desire line plot of all trips taken by the newly-added households in ActivitySim, and Figure 7 shows the distribution of these trips based on their production zone. Though trip-based models do include non–home-based trips, it is difficult to model accurately where and by whom these trips are made. Analyzing these trips and capturing the full impact of new development in this way is only possible in an ABM. Note that in a trip-based model, a round-trip from home to work and back is regarded as two trips produced in the home zone. ActivitySim does not deal with productions and attractions in the same way, so we are counting a trip with an origin or destination in the home zone as produced by that zone e.g. in Figure 6Figures 6 Figure 7and 7.\n\n\n\n\n\n\n\n\nFigure 4.3: personmiles abm\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4.4: personmiles tbm\n\n\n\n\n\nCan’t really shade since nhb relocation happens before mode choice. Best you can do is difference the NHB matrices (Figure 4.5 (2-panel)) (If there are nhb trips in/out of zones): clearly some of these are not nhb from residents since they occur in these zones.\nAn output of the trip-based model is a large trip matrix that contains the number of trips that were produced and attracted between each zone. Along with this matrix, there are matrices showing productions and attractions based on each mode and each trip purpose. By selecting just the zones we wanted to analyze, we could see how many trips the new development produced and attracted, and we could see what modes and purposes were connected to those trips. To visualize this, we used the matrix file and the TAZ shape file to create desire lines as seen in (some figure we can add).\nImplications: To compare this to the base scenario, we simply subtracted the base scenario trip matrix by the trip matrix from the land use scenario to get desire lines showing the increase in trips. As could be reasonably expected, we saw that more trips were produced and attracted by the zones containing the new land use area. We were also able to get an idea of where each of the trips to and from the area were coming from or going to, and what types of modes where being used.\nTalk about the numbers we found and describe the results.\nLimitations: While we could see the different trips leaving and entering the new development, we weren’t able to see what types of people were making the trips. We also couldn’t tell if those trips were part of a series of trips made by the same person.\n\n\n\n\n\n\n\n\n\n\n\n(a) hb\n\n\n\n\n\n\n\n\n\n\n\n(b) nhb\n\n\n\n\n\n\nFigure 4.5: lu desire tbm\n\n\n\n\nWith abm, you can follow people and trace their exact paths (Figure 4.6). Implications: Along with seeing where each trip produced in the new development was going, the activity-based model allowed us to see how trips from the development were related to other trips in a travel tour. With the synthetic population, we were also able to see which types of people were taking trips from the new development.\n\n\n\n\n\n\n\n\nFigure 4.6: desire abm",
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     "href": "qmd/2_transit.html",
     "title": "5  Scenario 2: Increased Transit Service",
     "section": "",
-    "text": "5.1 Scenario Creation\nWFRC model needs three adjustments: edit the headways, point the model to the new frontrunner network (which includes speeds), and add park-and-ride option at new station locations in highway? network.\nTo model this scenario in the Wasatch Front Regional Council (WFRC) model, we simply needed to add the new stations and adjust the headway and speed inputs. Since transit is a direct input in the WFRC model, we accessed and updated the file that stores the FrontRunner headway and node data. We changed the headways from 60 minutes during off-peak and 30 minutes during peak times to 30 minutes during off-peak and 15 minutes during peak times. In the same file, we added the extra nodes corresponding to the nodes on the extended rail line in the network file. In the network file, each node on the rail line that is a train station is flagged as a place to park and ride, and, because there were new stations added, we had to flag the new stations’ nodes as places to park and ride. The last thing we did was change the posted speed of each link to the speeds that WFRC had predicted for a partially electrified train system. After changing the speeds, headways, and train stations, we ran the model.\nThe WFRC/MAG model contains data on transit headways (including FrontRunner). The BYU team will change this data from 30-minute to 15-minute peak headways and 60-minute to 30-minute off-peak headways. To increase the speed, the team will modify the model to use the predicted speeds after electrification instead of the base year speeds. The 2050 transit networks from the WFRC/MAG model appear to have these changes made, and so the team will use these speed values for the FrontRunner. These networks will be paired with the 2019 socioeconomic scenario for comparison with the base scenario.\nThe output of the WFRC/MAG model for this scenario will contain new transit network skims. These updated skims will be used in ActivitySim instead of the baseline scenario skims. Because the mode share of transit is relatively low, it is not expected that the highway travel times will be affected very much by this change, and so the baseline highway skims will remain fixed. No other changes to ActivitySim are necessary to model this scenario.\nAsim just needs new skims, gotten from the new WFRC scenario.\nIn the WFRC model, this change was relatively easy to implement. The headways are stored in the input data and are easily modified, and the 2050 network is already built in to the model for future-year analysis. Including network assignment is outside the scope of this research, and the BYU implementation of ActivitySim uses the same skim files that the WFRC demand model uses. In this case, the team used the new congested network skims from the WFRC transit scenario to feed the ActivitySim demand model representing this scenario. This is the only change needed for this scenario.",
+    "text": "5.1 Scenario Creation\nIn the WFRC model, this change is relatively easy to implement. The headways are stored directly in the input data and are easily modified, and a year-2050 network with increased speeds and additional stations is already built into the model for future-year analysis. The only additional change needed was to turn on the “park and ride” flag in the highway network at each new station. Wasatch Front Regional Council (WFRC)\nTo implement this scenario in ActivitySim, only updated travel skims are needed. As in the baseline scenario, the updated transit skims output from the WFRC model’s network assignment in this model scenario are taken directly as inputs to ActivitySim. Because the mode share of transit is relatively low, it is not expected that the highway travel times will be affected very much by this change, and so the highway skims from the baseline scenario are used directly and not updated for this scenario. No other changes to ActivitySim are necessary to model this scenario.",
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@@ -164,7 +164,7 @@
     "href": "qmd/3_wfh.html#scenario-creation",
     "title": "6  Scenario 3: Increase in Remote Work",
     "section": "6.2 Scenario Creation",
-    "text": "6.2 Scenario Creation\nWFRC needs only to move 2050 data to 2019 for TC% and HBJ.\n\n\n\nTable 6.1: wfrc telecommute data\n\n\n\n# A tibble: 10 × 3\n   name            wfrc_2019 wfrc_2050\n   &lt;chr&gt;               &lt;dbl&gt;     &lt;dbl&gt;\n 1 Retail             0.027          0\n 2 Food               0.0187        NA\n 3 Manufacturing      0.0202        NA\n 4 Office             0.0666        NA\n 5 Gov't/Education    0.0167        NA\n 6 Health             0.0286        NA\n 7 Agriculture        0.0693        NA\n 8 Mining             0.0053        NA\n 9 Construction       0.0328        NA\n10 Other              0.0537        NA\n\n\n\n\nThis model is already designed to represent work from home using a lookup table of telecommute percentages segmented by job type. This data is used to create telecommuting trips that don’t get assigned during trip distribution. Additionally, there is a global scaling factor to adjust all telecommute percentages. The BYU Team proposes using the WFRC/MAG predicted 2040 telecommute percentages in place of the base year values. Figure 3 shows a plot of the average telecommute percentage by year as forecasted in the WFRC/MAG model.\nIn the WFRC trip-based model, it was already programmed to reduce home-based work trips using a telecommute percentage for each type of employment and a percentage of home-base jobs. We simply changed the telecommute and home-based job percentages to align with projections for 2050. which increased telecommute trips from 3.7 to 10.2 percent and increased home-based jobs from 2.9 to 3.5 percent. If there was more guidance in the literature on how telecommuting affects other types of trips, then we could have tried to represent that in the model by adding a certain amount of discretionary trips according to the telecommute percentages. With the Wasatch Front Regional Council (WFRC) model, it might be difficult to have the additional discretionary trips be coming from the same zones as the telecommute trips. (I’m not sure about this. I’ll have to look more into it)\nAsim is the same as calibration for the baseline scenario, but with different target %s (show table).\nThe WFRC model is already designed to represent work from home using a lookup table of telecommute percentages segmented by job type, as well as SE data indicating the number of fully-remote jobs by TAZ. The BYU Team used the WFRC predicted 2050 data for these in place of the base year values. The WFRC model does not include a potential increase in discretionary trips for those who work remotely, though some literature suggests this may happen in reality.\nThe team adjusted ActivitySim’s two remote work models to match the WFRC targets in both the base year and the increased WFH scenario. An additional note about the WFH implementation in ActivitySim is that because it is done before tour generation, an individual who does not need to make a work tour may be free to make other discretionary tours, and so this does at least somewhat model an increase in other trip types coinciding with increased telecommuting. Note that only the remote work parameters were adjusted in this scenario. The scenario still uses the “Base” 2019 population and SE data.",
+    "text": "6.2 Scenario Creation\nWFRC needs only to move 2050 data to 2019 for TC% and HBJ.\n\n\n\n\nTable 6.1: wfrc telecommute data\n\n\n\n\n\n\n\nname\nwfrc_2019\nwfrc_2050\n\n\n\n\nRetail\n0.027\n0\n\n\nFood\n0.019\nNA\n\n\nManufacturing\n0.020\nNA\n\n\nOffice\n0.067\nNA\n\n\nGov't/Education\n0.017\nNA\n\n\nHealth\n0.029\nNA\n\n\nAgriculture\n0.069\nNA\n\n\nMining\n0.005\nNA\n\n\nConstruction\n0.033\nNA\n\n\nOther\n0.054\nNA\n\n\n\n\n\n\n\n\n\n\n\nThis model is already designed to represent work from home using a lookup table of telecommute percentages segmented by job type. This data is used to create telecommuting trips that don’t get assigned during trip distribution. Additionally, there is a global scaling factor to adjust all telecommute percentages. The BYU Team proposes using the WFRC/MAG predicted 2040 telecommute percentages in place of the base year values. Figure 3 shows a plot of the average telecommute percentage by year as forecasted in the WFRC/MAG model.\nIn the WFRC trip-based model, it was already programmed to reduce home-based work trips using a telecommute percentage for each type of employment and a percentage of home-base jobs. We simply changed the telecommute and home-based job percentages to align with projections for 2050. which increased telecommute trips from 3.7 to 10.2 percent and increased home-based jobs from 2.9 to 3.5 percent. If there was more guidance in the literature on how telecommuting affects other types of trips, then we could have tried to represent that in the model by adding a certain amount of discretionary trips according to the telecommute percentages. With the Wasatch Front Regional Council (WFRC) model, it might be difficult to have the additional discretionary trips be coming from the same zones as the telecommute trips. (I’m not sure about this. I’ll have to look more into it)\nAsim is the same as calibration for the baseline scenario, but with different target %s (show table).\nThe WFRC model is already designed to represent work from home using a lookup table of telecommute percentages segmented by job type, as well as SE data indicating the number of fully-remote jobs by TAZ. The BYU Team used the WFRC predicted 2050 data for these in place of the base year values. The WFRC model does not include a potential increase in discretionary trips for those who work remotely, though some literature suggests this may happen in reality.\nThe team adjusted ActivitySim’s two remote work models to match the WFRC targets in both the base year and the increased WFH scenario. An additional note about the WFH implementation in ActivitySim is that because it is done before tour generation, an individual who does not need to make a work tour may be free to make other discretionary tours, and so this does at least somewhat model an increase in other trip types coinciding with increased telecommuting. Note that only the remote work parameters were adjusted in this scenario. The scenario still uses the “Base” 2019 population and SE data.",
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       "<span class='chapter-number'>6</span>  <span class='chapter-title'>Scenario 3: Increase in Remote Work</span>"
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@@ -174,7 +174,7 @@
     "href": "qmd/3_wfh.html#scenario-analysis",
     "title": "6  Scenario 3: Increase in Remote Work",
     "section": "6.3 Scenario Analysis",
-    "text": "6.3 Scenario Analysis\n\n\n# A tibble: 9 × 8\n  purpose mode     cube_wfh cube_by cube_diff_pct asim_wfh asim_by asim_diff_pct\n  &lt;chr&gt;   &lt;chr&gt;       &lt;dbl&gt;   &lt;dbl&gt;         &lt;dbl&gt;    &lt;int&gt;   &lt;int&gt;         &lt;dbl&gt;\n1 hbo     auto     4097821.  4.10e6      0.000277  3078596 3042754        0.0118\n2 hbo     nonmotor  508869.  5.10e5     -0.00250    597323  591164        0.0104\n3 hbo     transit    37359.  3.73e4      0.000344   220937  218438        0.0114\n4 hbw     auto     1483120.  1.59e6     -0.0651    1209400 1288620       -0.0615\n5 hbw     nonmotor   71063.  7.65e4     -0.0712     180968  191880       -0.0569\n6 hbw     transit    44977.  4.88e4     -0.0774     178056  189610       -0.0609\n7 nhb     auto     2193201.  2.22e6     -0.0142    1606889 1645356       -0.0234\n8 nhb     nonmotor  144126.  1.46e5     -0.0156     216257  224248       -0.0356\n9 nhb     transit    13199.  1.35e4     -0.0189     100654  103398       -0.0265\n\n\nUsing the outputs from the trip-based model that show lengths and number of trips between zones, we calculated the vehicle miles traveled for each trip. This allowed us to see which zones had the biggest decrease in VMT. This also allowed us to find the number of trips of certain length to create a trip length frequency distribution that can be seen in (another figure).\n\n\n# A tibble: 9 × 8\n  purpose mode     wfh_trips by_trips trips_pct   wfh_pmt    by_pmt   pmt_pct\n  &lt;chr&gt;   &lt;chr&gt;        &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;\n1 hbo     auto      4097821. 4096688.  0.000277 19571300. 19509401.  0.00317 \n2 hbo     nonmotor   508869.  510143. -0.00250    590349.   591297. -0.00160 \n3 hbo     transit     37359.   37346.  0.000344   264432.   264203.  0.000867\n4 hbw     auto      1483120. 1586414. -0.0651   15015364. 15941522. -0.0581  \n5 hbw     nonmotor    71063.   76506. -0.0712     122930.   132216. -0.0702  \n6 hbw     transit     44977.   48752. -0.0774     500953.   547804. -0.0855  \n7 nhb     auto      2193201. 2224878. -0.0142   12275574. 12427922. -0.0123  \n8 nhb     nonmotor   144126.  146404. -0.0156     134784.   136914. -0.0156  \n9 nhb     transit     13199.   13453. -0.0189      72018.    73563. -0.0210  \n\n\n\n\n# A tibble: 9 × 8\n  purpose mode     wfh_trips by_trips trips_pct   wfh_pmt    by_pmt  pmt_pct\n  &lt;fct&gt;   &lt;fct&gt;        &lt;int&gt;    &lt;int&gt;     &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;\n1 hbw     auto       1209400  1288620   -0.0615 12012008. 12831073. -0.0638 \n2 hbw     transit     178056   189610   -0.0609  1726785.  1840776. -0.0619 \n3 hbw     nonmotor    180968   191880   -0.0569   429773.   456097. -0.0577 \n4 hbo     auto       3078596  3042754    0.0118 17079259. 16886907.  0.0114 \n5 hbo     transit     220937   218438    0.0114  1597334.  1589804.  0.00474\n6 hbo     nonmotor    597323   591164    0.0104   972346.   961824.  0.0109 \n7 nhb     auto       1606889  1645356   -0.0234  7982697.  8218401. -0.0287 \n8 nhb     transit     100654   103398   -0.0265   582557.   600727. -0.0302 \n9 nhb     nonmotor    216257   224248   -0.0356   254643.   263963. -0.0353 \n\n\nIt is straightforward to determine decreased VMT by production zone (and similar analyses) in both models (see Figure 11Figures 11 andFigure 12 12). Additionally, the team made a desire line plot in the WFRC model of decreased trips. This, however, is essentially a scaled-down version of the total trips in the Base scenario (see Figure 13). Remote work percentages do vary by industry, but there is no accounting for trips lengths or personal variables. Figure 14 shows these same plots for ActivitySim. At first glance, this also seems like a scaled-down version of the total trips, even though ActivitySim’s remote work models do include distance to work and personal attributes. It is difficult to tell from the figure alone. The team plans to do more analysis on this point.\nInclude a table of the asim tc and wfh coefficients. Maybe put it in the methodology.\n\n\n\n\n\n\n\n\n\n\n\n\n\nBick, Alexander, Adam Blandin, and Karel Mertens. 2021. “Work from Home Before and After the COVID-19 Outbreak.” https://doi.org/https://doi.org/10.24149/wp2017r2.\n\n\nBramberga, Katrina Aleksandra. 2023. “Teleworking in Four-Step Transport Demand Models.” PhD thesis, KTH Royal Institute of Technology.\n\n\nElldér, Erik. 2020. “Telework and Daily Travel: New Evidence from Sweden.” Journal of Transport Geography 86 (June): 102777. https://doi.org/10.1016/j.jtrangeo.2020.102777.\n\n\nKim, Seung-Nam. 2017. “Is Telecommuting Sustainable? An Alternative Approach to Estimating the Impact of Home-Based Telecommuting on Household Travel.” International Journal of Sustainable Transportation 11 (2): 72–85. https://doi.org/10.1080/15568318.2016.1193779.\n\n\nMoeckel, Rolf. 2017. “Working from Home: Modeling the Impact of Telework on Transportation and Land Use.” Transportation Research Procedia, Emerging technologies and models for transport and mobility, 26 (January): 207–14. https://doi.org/10.1016/j.trpro.2017.07.021.\n\n\nMoreno, Ana Tsui, and Rolf Moeckel. 2017. “Microscopic Destination Choice: Incorporating Travel Time Budgets as Constraints.” Transportation Research Procedia.\n\n\nSener, Ipek N, and Chandra R Bhat. 2011. “A Copula-Based Sample Selection Model of Telecommuting Choice and Frequency.” Environment and Planning A: Economy and Space 43 (1): 126–45. https://doi.org/10.1068/a43133.\n\n\nYasenov, Vasil I. 2020. “Who Can Work from Home?” {SSRN} {Scholarly} {Paper}. Rochester, NY. https://doi.org/10.2139/ssrn.3590895.\n\n\nZhu, P., and S. G. Mason. 2014. “The Impact of Telecommuting on Personal Vehicle Usage and Environmental Sustainability.” International Journal of Environmental Science and Technology 11 (8): 2185–2200. https://doi.org/10.1007/s13762-014-0556-5.",
+    "text": "6.3 Scenario Analysis\n\n\n\n\n\n\npurpose\nmode\ncube_wfh\ncube_by\ncube_diff_pct\nasim_wfh\nasim_by\nasim_diff_pct\n\n\n\n\nhbo\nauto\n4097821\n4096688\n0.000\n3078596\n3042754\n0.012\n\n\nhbo\nnonmotor\n508869\n510143\n-0.002\n597323\n591164\n0.010\n\n\nhbo\ntransit\n37359\n37346\n0.000\n220937\n218438\n0.011\n\n\nhbw\nauto\n1483120\n1586414\n-0.065\n1209400\n1288620\n-0.061\n\n\nhbw\nnonmotor\n71063\n76506\n-0.071\n180968\n191880\n-0.057\n\n\nhbw\ntransit\n44977\n48752\n-0.077\n178056\n189610\n-0.061\n\n\nnhb\nauto\n2193201\n2224878\n-0.014\n1606889\n1645356\n-0.023\n\n\nnhb\nnonmotor\n144126\n146404\n-0.016\n216257\n224248\n-0.036\n\n\nnhb\ntransit\n13199\n13453\n-0.019\n100654\n103398\n-0.027\n\n\n\n\n\n\n\n\nUsing the outputs from the trip-based model that show lengths and number of trips between zones, we calculated the vehicle miles traveled for each trip. This allowed us to see which zones had the biggest decrease in VMT. This also allowed us to find the number of trips of certain length to create a trip length frequency distribution that can be seen in (another figure).\n\n\n# A tibble: 9 × 8\n  purpose mode     wfh_trips by_trips trips_pct   wfh_pmt    by_pmt   pmt_pct\n  &lt;chr&gt;   &lt;chr&gt;        &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;\n1 hbo     auto      4097821. 4096688.  0.000277 19571300. 19509401.  0.00317 \n2 hbo     nonmotor   508869.  510143. -0.00250    590349.   591297. -0.00160 \n3 hbo     transit     37359.   37346.  0.000344   264432.   264203.  0.000867\n4 hbw     auto      1483120. 1586414. -0.0651   15015364. 15941522. -0.0581  \n5 hbw     nonmotor    71063.   76506. -0.0712     122930.   132216. -0.0702  \n6 hbw     transit     44977.   48752. -0.0774     500953.   547804. -0.0855  \n7 nhb     auto      2193201. 2224878. -0.0142   12275574. 12427922. -0.0123  \n8 nhb     nonmotor   144126.  146404. -0.0156     134784.   136914. -0.0156  \n9 nhb     transit     13199.   13453. -0.0189      72018.    73563. -0.0210  \n\n\n\n\n# A tibble: 9 × 8\n  purpose mode     wfh_trips by_trips trips_pct   wfh_pmt    by_pmt  pmt_pct\n  &lt;fct&gt;   &lt;fct&gt;        &lt;int&gt;    &lt;int&gt;     &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;\n1 hbw     auto       1209400  1288620   -0.0615 12012008. 12831073. -0.0638 \n2 hbw     transit     178056   189610   -0.0609  1726785.  1840776. -0.0619 \n3 hbw     nonmotor    180968   191880   -0.0569   429773.   456097. -0.0577 \n4 hbo     auto       3078596  3042754    0.0118 17079259. 16886907.  0.0114 \n5 hbo     transit     220937   218438    0.0114  1597334.  1589804.  0.00474\n6 hbo     nonmotor    597323   591164    0.0104   972346.   961824.  0.0109 \n7 nhb     auto       1606889  1645356   -0.0234  7982697.  8218401. -0.0287 \n8 nhb     transit     100654   103398   -0.0265   582557.   600727. -0.0302 \n9 nhb     nonmotor    216257   224248   -0.0356   254643.   263963. -0.0353 \n\n\nIt is straightforward to determine decreased VMT by production zone (and similar analyses) in both models (see Figure 11Figures 11 andFigure 12 12). Additionally, the team made a desire line plot in the WFRC model of decreased trips. This, however, is essentially a scaled-down version of the total trips in the Base scenario (see Figure 13). Remote work percentages do vary by industry, but there is no accounting for trips lengths or personal variables. Figure 14 shows these same plots for ActivitySim. At first glance, this also seems like a scaled-down version of the total trips, even though ActivitySim’s remote work models do include distance to work and personal attributes. It is difficult to tell from the figure alone. The team plans to do more analysis on this point.\nInclude a table of the asim tc and wfh coefficients. Maybe put it in the methodology.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBick, Alexander, Adam Blandin, and Karel Mertens. 2021. “Work from Home Before and After the COVID-19 Outbreak.” https://doi.org/https://doi.org/10.24149/wp2017r2.\n\n\nBramberga, Katrina Aleksandra. 2023. “Teleworking in Four-Step Transport Demand Models.” PhD thesis, KTH Royal Institute of Technology.\n\n\nElldér, Erik. 2020. “Telework and Daily Travel: New Evidence from Sweden.” Journal of Transport Geography 86 (June): 102777. https://doi.org/10.1016/j.jtrangeo.2020.102777.\n\n\nKim, Seung-Nam. 2017. “Is Telecommuting Sustainable? An Alternative Approach to Estimating the Impact of Home-Based Telecommuting on Household Travel.” International Journal of Sustainable Transportation 11 (2): 72–85. https://doi.org/10.1080/15568318.2016.1193779.\n\n\nMoeckel, Rolf. 2017. “Working from Home: Modeling the Impact of Telework on Transportation and Land Use.” Transportation Research Procedia, Emerging technologies and models for transport and mobility, 26 (January): 207–14. https://doi.org/10.1016/j.trpro.2017.07.021.\n\n\nMoreno, Ana Tsui, and Rolf Moeckel. 2017. “Microscopic Destination Choice: Incorporating Travel Time Budgets as Constraints.” Transportation Research Procedia.\n\n\nSener, Ipek N, and Chandra R Bhat. 2011. “A Copula-Based Sample Selection Model of Telecommuting Choice and Frequency.” Environment and Planning A: Economy and Space 43 (1): 126–45. https://doi.org/10.1068/a43133.\n\n\nYasenov, Vasil I. 2020. “Who Can Work from Home?” {SSRN} {Scholarly} {Paper}. Rochester, NY. https://doi.org/10.2139/ssrn.3590895.\n\n\nZhu, P., and S. G. Mason. 2014. “The Impact of Telecommuting on Personal Vehicle Usage and Environmental Sustainability.” International Journal of Environmental Science and Technology 11 (8): 2185–2200. https://doi.org/10.1007/s13762-014-0556-5.",
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       "<span class='chapter-number'>6</span>  <span class='chapter-title'>Scenario 3: Increase in Remote Work</span>"
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