optimal_station_Equilibrium.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this python file we compute the cost for a network under a Wardrop Equilibrium.\n",
    "If you don't know what that is, two good places to start:\n",
    "- Pau's slides on this, slide 9 ish https://classroom.google.com/c/NDkyMDU5MTg0Mjg1/m/NTk4MTEzNTU4NzM3/details\n",
    "- check out the methodology section of the Networks II paper here:\n",
    "    https://classroom.google.com/c/NDkyMDU5MTg0Mjg1/p/NTQzNjcyNDMwMTkx?pli=1\n",
    "\n",
    "\n",
    "\n",
    "I use the code from [this notebook](https://nbviewer.org/github/PyTrans/Urban-Network-Analysis/blob/master/Trip_Assignment-Frank-Wolfe_Algorithm.ipynb) to solve the Wardrop Equilibrium  for a multi-source/sink traffic flow problem.\n",
    "\n",
    "you may need to download the pytrans github repository before running this code. you can do this by running the following, according to the readme for the respository:\n",
    "```\n",
    "git clone https://github.com/PyTrans/Urban-Network-Analysis.git\n",
    "```\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing the latency costs at equilbrium using pytrans\n",
    "\n",
    "First, we import required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from itertools import combinations\n",
    "from bike_functions import *\n",
    "import matplotlib.pyplot as plt\n",
    "from pytrans.UrbanNetworkAnalysis import TransportationNetworks as tn"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "link_file = 'Data/SiouxFalls_net.tntp'\n",
    "trip_file = 'Data/SiouxFalls_trips.tntp'\n",
    "node_file = 'Data/SiouxFalls_node.tntp'\n",
    "\n",
    "SO = False # True - System optimum, False - User equilibrium\n",
    "siouxFalls = tn.Network(link_file, trip_file, node_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Social Equilibrium - no stations\n",
    "### Implement iterative optimization steps (UE)\n",
    "#### 1. Initialization\n",
    "\n",
    "Perform the All-or-Nothing (AoN) assignment based on the free flow travel time (t0) and calculate the link performance (t1) based on the link volume (x0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## initialization\n",
    "\n",
    "# define output variables, network and fwResult\n",
    "network = {(u,v): {'t0':d['object'].t0, 'alpha':d['object'].alpha, \\\n",
    "           'beta':d['object'].beta, 'capa':d['object'].capacity, 'flow':[], \\\n",
    "           'auxiliary':[], 'cost':[]} for (u, v, d) in siouxFalls.graph.edges(data=True)}\n",
    "\n",
    "fwResult = {'theta':[], 'z':[]}\n",
    "\n",
    "# initial all-or-nothing assignment and update link travel time(cost)\n",
    "siouxFalls.all_or_nothing_assignment()\n",
    "siouxFalls.update_linkcost()\n",
    "\n",
    "for linkKey, linkVal in network.items():\n",
    "    linkVal['cost'].append(siouxFalls.graph[linkKey[0]][linkKey[1]]['weight'])\n",
    "    linkVal['auxiliary'].append(siouxFalls.graph[linkKey[0]][linkKey[1]]['object'].vol)\n",
    "    linkVal['flow'].append(siouxFalls.graph[linkKey[0]][linkKey[1]]['object'].vol)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Convergence Test \n",
    "\n",
    "Converge to the equilibrium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the cost from the last iteration, the equilibrium cost: 4231493.702767667\n"
     ]
    }
   ],
   "source": [
    "## iterations\n",
    "iterNum=0\n",
    "iteration = True\n",
    "while iteration:\n",
    "    iterNum += 1\n",
    "    siouxFalls.all_or_nothing_assignment()\n",
    "    siouxFalls.update_linkcost()\n",
    "    \n",
    "    # set auxiliary flow using updated link flow\n",
    "    for linkKey, linkVal in network.items():\n",
    "        linkVal['auxiliary'].append(siouxFalls.graph[linkKey[0]][linkKey[1]]['object'].vol)\n",
    "        \n",
    "    # getting optimal move size (theta)\n",
    "    theta = lineSearch(network, SO)\n",
    "    fwResult['theta'].append(theta)\n",
    "    \n",
    "    # set link flow (move) based on the theta, auxiliary flow, and link flow of previous iteration\n",
    "    for linkKey, linkVal in network.items():\n",
    "        aux = linkVal['auxiliary'][-1]\n",
    "        flow = linkVal['flow'][-1]\n",
    "        linkVal['flow'].append(flow + theta*(aux-flow))\n",
    "        \n",
    "        siouxFalls.graph[linkKey[0]][linkKey[1]]['object'].vol =  flow + theta * (aux - flow)\n",
    "        siouxFalls.graph[linkKey[0]][linkKey[1]]['object'].flow = flow + theta * (aux - flow)\n",
    "        \n",
    "    # update link travel time\n",
    "    siouxFalls.update_linkcost()\n",
    "    \n",
    "    # calculate objective function value\n",
    "    z=0\n",
    "    for linkKey, linkVal in network.items():\n",
    "        linkVal['cost'].append(siouxFalls.graph[linkKey[0]][linkKey[1]]['weight'])\n",
    "        totalcost = siouxFalls.graph[linkKey[0]][linkKey[1]]['object'].get_objective_function()\n",
    "        z+=totalcost\n",
    "        \n",
    "    fwResult['z'].append(z)        \n",
    "        \n",
    "    # convergence test\n",
    "    if iterNum == 1:\n",
    "        iteration = True\n",
    "    else:\n",
    "        if abs(fwResult['z'][-2] - fwResult['z'][-1]) <= 0.001 or iterNum==3000:\n",
    "            iteration = False\n",
    "\n",
    "print('the cost from the last iteration, the equilibrium cost:', fwResult['z'][-1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "#### 1.Link Flow\n",
    "Display the flows for each edge at social equilibrium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  ->  2 :  4494.541769852098\n",
      "1  ->  3 :  8118.778958173066\n",
      "2  ->  1 :  4519.221797815283\n",
      "2  ->  6 :  5967.415424119265\n",
      "3  ->  1 :  8094.098930209869\n",
      "3  ->  4 :  14011.734532471419\n",
      "3  ->  12 :  10027.635980459709\n",
      "6  ->  2 :  5992.095452082454\n",
      "6  ->  5 :  8806.41089044818\n",
      "6  ->  8 :  12492.075339039784\n",
      "4  ->  3 :  14035.300255070433\n",
      "4  ->  5 :  18010.561177776\n",
      "4  ->  11 :  5203.138655249939\n",
      "12  ->  3 :  9979.390229897474\n",
      "12  ->  11 :  8404.30673586717\n",
      "12  ->  13 :  12293.49958224446\n",
      "5  ->  4 :  18034.525517855654\n",
      "5  ->  6 :  8797.189587004616\n",
      "5  ->  9 :  15783.996289215766\n",
      "11  ->  4 :  5302.740037769327\n",
      "11  ->  10 :  17603.65214582616\n",
      "11  ->  12 :  8365.259304389087\n",
      "11  ->  14 :  9776.846543222036\n",
      "9  ->  5 :  15798.739325851839\n",
      "9  ->  8 :  6837.003588057352\n",
      "9  ->  10 :  21745.98823501208\n",
      "8  ->  6 :  12525.976670446556\n",
      "8  ->  7 :  12041.696631400406\n",
      "8  ->  9 :  6881.8139172902975\n",
      "8  ->  16 :  8387.782258644345\n",
      "7  ->  8 :  12102.268632503978\n",
      "7  ->  18 :  15797.41185102116\n",
      "18  ->  7 :  15857.983852124704\n",
      "18  ->  16 :  15338.763386034821\n",
      "18  ->  20 :  18981.85790212544\n",
      "16  ->  8 :  8405.921918180433\n",
      "16  ->  10 :  11073.099105880463\n",
      "16  ->  17 :  11692.431092589124\n",
      "16  ->  18 :  15282.953024347871\n",
      "10  ->  9 :  21815.92094241528\n",
      "10  ->  11 :  17726.175580713087\n",
      "10  ->  15 :  23127.1147689482\n",
      "10  ->  16 :  11046.400103756408\n",
      "10  ->  17 :  8100.25998000533\n",
      "15  ->  10 :  23193.033134018424\n",
      "15  ->  14 :  9081.050165267972\n",
      "15  ->  19 :  19083.482310532527\n",
      "15  ->  22 :  18407.035578008876\n",
      "17  ->  10 :  8100.098755101176\n",
      "17  ->  16 :  11681.45939256229\n",
      "17  ->  19 :  9952.457004746202\n",
      "14  ->  11 :  9814.877059376397\n",
      "14  ->  15 :  9038.201593171207\n",
      "14  ->  23 :  8399.687160193645\n",
      "13  ->  12 :  12384.301263160376\n",
      "13  ->  24 :  11121.19271911619\n",
      "24  ->  13 :  11111.99440003203\n",
      "24  ->  21 :  10257.083853657046\n",
      "24  ->  23 :  7862.518017754149\n",
      "23  ->  14 :  8394.869104251218\n",
      "23  ->  22 :  9625.288009751668\n",
      "23  ->  24 :  7902.576837002738\n",
      "19  ->  15 :  19115.979008886592\n",
      "19  ->  17 :  9941.324079815235\n",
      "19  ->  20 :  8687.309218981964\n",
      "22  ->  15 :  18383.30581682183\n",
      "22  ->  20 :  7000.937506413678\n",
      "22  ->  21 :  8606.710833577832\n",
      "22  ->  23 :  9660.528773057873\n",
      "20  ->  18 :  18998.240264915967\n",
      "20  ->  19 :  8708.672992405021\n",
      "20  ->  21 :  6303.27072143744\n",
      "20  ->  22 :  7000.651706103385\n",
      "21  ->  20 :  6240.731057340766\n",
      "21  ->  22 :  8618.507636007262\n",
      "21  ->  24 :  10307.826715324341\n"
     ]
    }
   ],
   "source": [
    "for (u, v, d) in siouxFalls.graph.edges(data=True):\n",
    "    print(u, ' -> ', v, ': ', d['object'].vol)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Social Equilibrium: 1 Random station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "siouxFalls2 = siouxFalls\n",
    "station = pd.Series(filter_stations(list(combinations(np.array(siouxFalls2.graph.nodes),2)))).sample(1).values[0]\n",
    "all_costs = []\n",
    "\n",
    "# if len(g_copy.get_all_shortest_paths(station[0], station[1], weights='free_flow_time')) == 0:\n",
    "#     exit()\n",
    "# print('station 1: ', station[0], 'station 2:', station[1])\n",
    "\n",
    "# get the shortest path between two nodes\n",
    "shortest_path = nx.shortest_path(siouxFalls2.graph, station[0],station[1], weight='time')\n",
    "\n",
    "# Calculate time, length and capacity\n",
    "bike_edge_time = calculate_path_free_flow_time(shortest_path, siouxFalls2.graph) * 1/2\n",
    "path_length = calculate_path_length(shortest_path, siouxFalls2.graph)\n",
    "capacity = calculate_path_capacity(shortest_path, siouxFalls2.graph, path_length)\n",
    "\n",
    "\n",
    "# If not an edge yet then add one:    \n",
    "siouxFalls2.graph = add_or_modify_undirected_edge(siouxFalls2.graph, station[0], station[1], bike_edge_time, path_length, capacity)\n",
    "\n",
    "node_colors = ['red' if node in station else 'blue' for node in siouxFalls2.graph.nodes()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What happens if we add a path from 5 to 7, where the previous shortest path was ['5', '6', '8', '7']?\n"
     ]
    }
   ],
   "source": [
    "node_colors = ['red' if node in station else 'blue' for node in siouxFalls2.graph.nodes()]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "pos = nx.spring_layout(siouxFalls2.graph)\n",
    "nx.draw_networkx_nodes(siouxFalls2.graph, pos, node_color=node_colors)\n",
    "nx.draw_networkx_edges(siouxFalls2.graph, pos, edgelist=siouxFalls2.graph.edges())\n",
    "plt.axis('off')\n",
    "\n",
    "plt.show()\n",
    "print('What happens if we add a path from {} to {}, where the previous shortest path was {}?'.format(station[0], station[1], shortest_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialization\n",
    "\n",
    "# define output variables, network and fwResult\n",
    "network = {(u,v): {'t0':d['object'].t0, 'alpha':d['object'].alpha, \\\n",
    "           'beta':d['object'].beta, 'capa':d['object'].capacity, 'flow':[], \\\n",
    "           'auxiliary':[], 'cost':[]} for (u, v, d) in siouxFalls2.graph.edges(data=True)}\n",
    "\n",
    "fwResult = {'theta':[], 'z':[]}\n",
    "\n",
    "# initial all-or-nothing assignment and update link travel time(cost)\n",
    "siouxFalls2.all_1r_nothing_assignment()\n",
    "siouxFalls2.update_linkcost()\n",
    "\n",
    "for linkKey, linkVal in network.items():\n",
    "    linkVal['cost'].append(siouxFalls2.graph[linkKey[0]][linkKey[1]]['weight'])\n",
    "    linkVal['auxiliary'].append(siouxFalls2.graph[linkKey[0]][linkKey[1]]['object'].vol)\n",
    "    linkVal['flow'].append(siouxFalls2.graph[linkKey[0]][linkKey[1]]['object'].vol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## iterations\n",
    "iterNum = 0\n",
    "iteration = True\n",
    "while iteration:\n",
    "    iterNum += 1\n",
    "    siouxFalls2.all_1r_nothing_assignment()\n",
    "    siouxFalls2.update_linkcost()\n",
    "    \n",
    "    # set auxiliary flow using updated link flow\n",
    "    for linkKey, linkVal in network.items():\n",
    "        linkVal['auxiliary'].append(siouxFalls2.graph[linkKey[0]][linkKey[1]]['object'].vol)\n",
    "        \n",
    "    # getting optimal move size (theta)\n",
    "    theta = lineSearch(network, SO)\n",
    "    fwResult['theta'].append(theta)\n",
    "    \n",
    "    # set link flow (move) based on the theta, auxiliary flow, and link flow of previous iteration\n",
    "    for linkKey, linkVal in network.items():\n",
    "        aux = linkVal['auxiliary'][-1]\n",
    "        flow = linkVal['flow'][-1]\n",
    "        linkVal['flow'].append(flow + theta*(aux-flow))\n",
    "        \n",
    "        siouxFalls2.graph[linkKey[0]][linkKey[1]]['object'].vol =  flow + theta * (aux - flow)\n",
    "        siouxFalls2.graph[linkKey[0]][linkKey[1]]['object'].flow = flow + theta * (aux - flow)\n",
    "        \n",
    "    # update link travel time\n",
    "    siouxFalls2.update_linkcost()\n",
    "    \n",
    "    # calculate objective function value\n",
    "    z=0\n",
    "    for linkKey, linkVal in network.items():\n",
    "        linkVal['cost'].append(siouxFalls2.graph[linkKey[0]][linkKey[1]]['weight'])\n",
    "        totalcost = siouxFalls2.graph[linkKey[0]][linkKey[1]]['object'].get_1bjective_function()\n",
    "        z += totalcost\n",
    "        \n",
    "    fwResult['z'].append(z)        \n",
    "        \n",
    "    # convergence test\n",
    "    if iterNum == 1:\n",
    "        iteration = True\n",
    "    else:\n",
    "        if abs(fwResult['z'][-2] - fwResult['z'][-1]) <= 0.001 or iterNum==3000:\n",
    "            iteration = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the cost from the last iteration, the equilibrium cost: 3837733.645039034\n"
     ]
    }
   ],
   "source": [
    "print('the cost from the last iteration, the equilibrium cost:', fwResult['z'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  ->  2 :  2803.5526555695724\n",
      "1  ->  3 :  7995.96664765392\n",
      "2  ->  1 :  2835.3048231535818\n",
      "2  ->  6 :  4604.075926886986\n",
      "3  ->  1 :  7964.214480069909\n",
      "3  ->  4 :  17660.61174826328\n",
      "3  ->  12 :  11618.412619773035\n",
      "6  ->  2 :  4635.828094471004\n",
      "6  ->  5 :  7074.435555897129\n",
      "6  ->  8 :  9462.508654274014\n",
      "4  ->  3 :  17649.8984460409\n",
      "4  ->  5 :  23362.387553470435\n",
      "4  ->  11 :  6601.36537368627\n",
      "12  ->  3 :  11597.373754411467\n",
      "12  ->  11 :  8216.714208039064\n",
      "12  ->  13 :  12831.789475219883\n",
      "5  ->  4 :  23451.52315826434\n",
      "5  ->  6 :  7071.511603589964\n",
      "5  ->  9 :  12459.28884063951\n",
      "5  ->  7 :  12810.93520382071\n",
      "11  ->  4 :  6601.516466669927\n",
      "11  ->  10 :  16095.581696789164\n",
      "11  ->  12 :  8212.999845991717\n",
      "11  ->  14 :  8765.541950707393\n",
      "9  ->  5 :  12503.452189409369\n",
      "9  ->  8 :  5047.766516777317\n",
      "9  ->  10 :  19347.110482669\n",
      "8  ->  6 :  9497.184774165198\n",
      "8  ->  7 :  9568.889313832617\n",
      "8  ->  9 :  5080.160805304605\n",
      "8  ->  16 :  7108.412991459349\n",
      "7  ->  8 :  9621.82306488247\n",
      "7  ->  18 :  20598.19126938948\n",
      "7  ->  5 :  12852.983507537569\n",
      "18  ->  7 :  20693.173324156167\n",
      "18  ->  16 :  17229.525167781612\n",
      "18  ->  20 :  22462.025166097428\n",
      "16  ->  8 :  7122.549648827974\n",
      "16  ->  10 :  9998.646947495821\n",
      "16  ->  17 :  11748.78145173355\n",
      "16  ->  18 :  17203.42039713663\n",
      "10  ->  9 :  19458.879542911513\n",
      "10  ->  11 :  16165.230841716457\n",
      "10  ->  15 :  19937.286275519935\n",
      "10  ->  16 :  9992.900342474592\n",
      "10  ->  17 :  6853.72668372766\n",
      "15  ->  10 :  20010.291788229664\n",
      "15  ->  14 :  8286.993025948941\n",
      "15  ->  19 :  16434.646516486446\n",
      "15  ->  22 :  17595.406378832307\n",
      "17  ->  10 :  6856.392771166617\n",
      "17  ->  16 :  11742.559943478409\n",
      "17  ->  19 :  10104.613957221232\n",
      "14  ->  11 :  8792.32953671647\n",
      "14  ->  15 :  8254.165413466273\n",
      "14  ->  23 :  7953.174148452556\n",
      "13  ->  12 :  12914.46497190557\n",
      "13  ->  24 :  9630.800241251698\n",
      "24  ->  13 :  9613.475737937428\n",
      "24  ->  21 :  9280.925635725114\n",
      "24  ->  23 :  7753.9870415832675\n",
      "23  ->  14 :  7947.134121978968\n",
      "23  ->  22 :  8903.564057786343\n",
      "23  ->  24 :  7792.006794757144\n",
      "19  ->  15 :  16448.77150561697\n",
      "19  ->  17 :  10101.058536405033\n",
      "19  ->  20 :  8752.240062044975\n",
      "22  ->  15 :  17587.114514894147\n",
      "22  ->  20 :  7915.857546805521\n",
      "22  ->  21 :  9140.48387752155\n",
      "22  ->  23 :  8935.543784486637\n",
      "20  ->  18 :  22483.111991509137\n",
      "20  ->  19 :  8762.809630359292\n",
      "20  ->  21 :  5187.098268000692\n",
      "20  ->  22 :  7934.864952372009\n",
      "21  ->  20 :  5137.762067293244\n",
      "21  ->  22 :  9145.16433471721\n",
      "21  ->  24 :  9325.581379236948\n"
     ]
    }
   ],
   "source": [
    "for (u, v, d) in siouxFalls2.graph.edges(data=True):\n",
    "    print(u, ' -> ', v, ': ', d['object'].vol)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing all station possibilities\n",
    "\n",
    "First, let's generate all possible combinations of bike station placement for the graph. By using set(), we omit pairs like ('2','1'), since for our purposes it is equivalent to ('1','2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stations2 = list(set(combinations(np.array(siouxFalls.graph.nodes),2)))\n",
    "stations3 = list(set(combinations(np.array(siouxFalls.graph.nodes),3)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Doing this in parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_graph_tuples = [(station, siouxFalls) for station in stations2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "station ('2', '5') eq cost computed. Cost: 4172028.0978567037\n",
      "station ('1', '5') eq cost computed. Cost: 4143526.6854009884\n",
      "station ('6', '9') eq cost computed. Cost: 4129334.546850328\n",
      "station ('4', '17') eq cost computed. Cost: 3825973.516270378\n",
      "station ('6', '13') eq cost computed. Cost: 4057441.468389038\n",
      "station ('12', '20') eq cost computed. Cost: 3659183.3922009934\n",
      "station ('14', '19') eq cost computed. Cost: 4055667.635176408\n",
      "station ('11', '19') eq cost computed. Cost: 3890069.421989956\n",
      "station ('4', '23') eq cost computed. Cost: 3907820.9921352454\n",
      "station ('1', '12') eq cost computed. Cost: 4100199.5393438837\n",
      "station ('2', '24') eq cost computed. Cost: 3714732.349694933\n",
      "station ('12', '19') eq cost computed. Cost: 3709397.326412007\n",
      "station ('9', '7') eq cost computed. Cost: 3707140.511862214\n",
      "station ('6', '22') eq cost computed. Cost: 3506343.3260158757\n",
      "station ('4', '22') eq cost computed. Cost: 3678780.1121171177\n",
      "station ('18', '22') eq cost computed. Cost: 3688053.099988802\n",
      "station ('11', '20') eq cost computed. Cost: 3489094.623355184\n",
      "station ('12', '5') eq cost computed. Cost: 3570258.7438771445\n",
      "station ('6', '24') eq cost computed. Cost: 3830637.8649326223\n",
      "station ('16', '10') eq cost computed. Cost: 3659363.320690902\n",
      "station ('12', '7') eq cost computed. Cost: 3416802.1283353656\n",
      "station ('6', '8') eq cost computed. Cost: 3688053.415202155\n",
      "station ('13', '22') eq cost computed. Cost: 3519925.07006422\n",
      "station ('16', '23') eq cost computed. Cost: 3328569.5662031947\n",
      "station ('18', '16') eq cost computed. Cost: 3531918.403263215\n",
      "station ('3', '11') eq cost computed. Cost: 3712546.098110946\n",
      "station ('24', '23') eq cost computed. Cost: 3659362.7921805945\n",
      "station ('15', '20') eq cost computed. Cost: 3375514.268377293\n",
      "station ('18', '13') eq cost computed. Cost: 3387479.938279549\n",
      "station ('7', '21') eq cost computed. Cost: 3639483.960068877\n",
      "station ('1', '20') eq cost computed. Cost: 3360281.121184189\n",
      "station ('17', '23') eq cost computed. Cost: 3246086.525146832\n",
      "station ('7', '18') eq cost computed. Cost: 3531918.198516839\n",
      "station ('8', '19') eq cost computed. Cost: 3495145.246244433\n",
      "station ('9', '23') eq cost computed. Cost: 3609655.6421097834\n",
      "station ('13', '23') eq cost computed. Cost: 3283128.106923001\n",
      "station ('2', '20') eq cost computed. Cost: 3299704.4766026777\n",
      "station ('23', '20') eq cost computed. Cost: 3559886.9556664196\n",
      "station ('3', '10') eq cost computed. Cost: 3073747.6417818647\n",
      "station ('2', '3') eq cost computed. Cost: 3211920.128766465\n",
      "station ('12', '21') eq cost computed. Cost: 3287750.3879630575\n",
      "station ('19', '20') eq cost computed. Cost: 3458663.594716001\n",
      "station ('8', '23') eq cost computed. Cost: 3470331.9230946735\n",
      "station ('24', '20') eq cost computed. Cost: 3121715.3340467447\n",
      "station ('5', '19') eq cost computed. Cost: 3212760.4522206807\n",
      "station ('8', '17') eq cost computed. Cost: 3437441.6910719406\n",
      "station ('5', '23') eq cost computed. Cost: 3014193.4734765883\n",
      "station ('14', '21') eq cost computed. Cost: 3126670.173950701\n",
      "station ('7', '20') eq cost computed. Cost: 3229526.786275801\n",
      "station ('2', '7') eq cost computed. Cost: 3219981.979011691\n",
      "station ('10', '17') eq cost computed. Cost: 3322058.759861798\n",
      "station ('3', '20') eq cost computed. Cost: 2956207.242922152\n",
      "station ('1', '21') eq cost computed. Cost: 3089334.5232155635\n",
      "station ('18', '15') eq cost computed. Cost: 3354372.3589354157\n",
      "station ('4', '15') eq cost computed. Cost: 2984080.4211206916\n",
      "station ('7', '14') eq cost computed. Cost: 3084140.58504486\n",
      "station ('1', '14') eq cost computed. Cost: 3105603.8232600302\n",
      "station ('5', '21') eq cost computed. Cost: 3213783.9825610872\n",
      "station ('15', '19') eq cost computed. Cost: 3283103.1887140544\n",
      "station ('9', '21') eq cost computed. Cost: 2856722.5807837276\n",
      "station ('18', '24') eq cost computed. Cost: 3299441.212356056\n",
      "station ('15', '21') eq cost computed. Cost: 3021836.580502998\n",
      "station ('4', '9') eq cost computed. Cost: 2930358.246669215\n",
      "station ('10', '23') eq cost computed. Cost: 2967977.393759761\n",
      "station ('2', '18') eq cost computed. Cost: 3068672.5243796064\n",
      "station ('9', '19') eq cost computed. Cost: 3120756.907553672\n",
      "station ('1', '7') eq cost computed. Cost: 2910379.278952112\n",
      "station ('12', '14') eq cost computed. Cost: 2830360.2176358555\n",
      "station ('19', '21') eq cost computed. Cost: 3235626.000997324\n",
      "station ('4', '13') eq cost computed. Cost: 2937118.8534426657\n",
      "station ('16', '17') eq cost computed. Cost: 2967977.1890452574\n",
      "station ('4', '24') eq cost computed. Cost: 2920182.7626329153\n",
      "station ('4', '16') eq cost computed. Cost: 3730087.8491107333\n",
      "station ('1', '2') eq cost computed. Cost: 4186059.721394056\n",
      "station ('12', '11') eq cost computed. Cost: 4122282.982727344\n",
      "station ('7', '23') eq cost computed. Cost: 3945930.5383070847\n",
      "station ('23', '19') eq cost computed. Cost: 4005163.6041713003\n",
      "station ('9', '15') eq cost computed. Cost: 3918976.562744283\n",
      "station ('4', '14') eq cost computed. Cost: 3983834.334718642\n",
      "station ('9', '16') eq cost computed. Cost: 3903941.0936735203\n",
      "station ('6', '18') eq cost computed. Cost: 3874623.705324795\n",
      "station ('16', '13') eq cost computed. Cost: 3507999.178354013\n",
      "station ('11', '17') eq cost computed. Cost: 3750067.1676352443\n",
      "station ('18', '14') eq cost computed. Cost: 3636993.5572062577\n",
      "station ('3', '16') eq cost computed. Cost: 3471623.1615825193\n",
      "station ('10', '22') eq cost computed. Cost: 3667687.6367492476\n",
      "station ('1', '10') eq cost computed. Cost: 3646932.203583449\n",
      "station ('6', '19') eq cost computed. Cost: 3688922.8593903272\n",
      "station ('13', '24') eq cost computed. Cost: 3816349.656213688\n",
      "station ('2', '12') eq cost computed. Cost: 3462721.650052229\n",
      "station ('4', '18') eq cost computed. Cost: 3416164.6670045154\n",
      "station ('10', '24') eq cost computed. Cost: 3396242.715054516\n",
      "station ('12', '10') eq cost computed. Cost: 3221875.195580316\n",
      "station ('6', '12') eq cost computed. Cost: 3495965.700655144\n",
      "station ('3', '4') eq cost computed. Cost: 3611034.6863823812\n",
      "station ('12', '17') eq cost computed. Cost: 3391470.3311191685\n",
      "station ('5', '17') eq cost computed. Cost: 3630994.340151131\n",
      "station ('15', '23') eq cost computed. Cost: 3331174.6220159866\n",
      "station ('9', '10') eq cost computed. Cost: 3377607.275196365\n",
      "station ('2', '23') eq cost computed. Cost: 3233844.9415191794\n",
      "station ('6', '14') eq cost computed. Cost: 3138530.3898501797\n",
      "station ('12', '23') eq cost computed. Cost: 3348440.67215658\n",
      "station ('6', '7') eq cost computed. Cost: 3332294.96651526\n",
      "station ('2', '21') eq cost computed. Cost: 3538857.6879921462\n",
      "station ('1', '11') eq cost computed. Cost: 3485313.2917684386\n",
      "station ('11', '23') eq cost computed. Cost: 3177680.1254465785\n",
      "station ('3', '6') eq cost computed. Cost: 3321134.1063794736\n",
      "station ('1', '19') eq cost computed. Cost: 3067653.6773200226\n",
      "station ('16', '22') eq cost computed. Cost: 2993301.140397444\n",
      "station ('4', '5') eq cost computed. Cost: 3348440.706616541\n",
      "station ('5', '15') eq cost computed. Cost: 3228324.484413542\n",
      "station ('14', '20') eq cost computed. Cost: 3377643.9566972204\n",
      "station ('16', '24') eq cost computed. Cost: 3337733.747945618\n",
      "station ('3', '17') eq cost computed. Cost: 3203804.6921742666\n",
      "station ('17', '13') eq cost computed. Cost: 3134391.414011659\n",
      "station ('8', '15') eq cost computed. Cost: 2947501.2910689116\n",
      "station ('17', '22') eq cost computed. Cost: 2946790.730389936\n",
      "station ('3', '13') eq cost computed. Cost: 3284756.5545585724\n",
      "station ('8', '16') eq cost computed. Cost: 3179303.224904338\n",
      "station ('7', '19') eq cost computed. Cost: 3265225.708838865\n",
      "station ('17', '24') eq cost computed. Cost: 3316248.4404513002\n",
      "station ('6', '5') eq cost computed. Cost: 2912079.528755623\n",
      "station ('23', '21') eq cost computed. Cost: 3165091.190279091\n",
      "station ('9', '17') eq cost computed. Cost: 2830712.5387694095\n",
      "station ('3', '23') eq cost computed. Cost: 2931951.120282798\n",
      "station ('11', '10') eq cost computed. Cost: 3191424.5004071947\n",
      "station ('5', '9') eq cost computed. Cost: 3083278.6574665094\n",
      "station ('5', '10') eq cost computed. Cost: 2828707.8693125765\n",
      "station ('1', '3') eq cost computed. Cost: 3208628.990266517\n",
      "station ('10', '15') eq cost computed. Cost: 3203970.269501803\n",
      "station ('5', '11') eq cost computed. Cost: 3115151.016794159\n",
      "station ('8', '24') eq cost computed. Cost: 2757788.055487698\n",
      "station ('10', '13') eq cost computed. Cost: 2858135.7489661495\n",
      "station ('4', '21') eq cost computed. Cost: 2991737.674018127\n",
      "station ('5', '22') eq cost computed. Cost: 3063653.33604687\n",
      "station ('16', '15') eq cost computed. Cost: 2776617.340905156\n",
      "station ('4', '8') eq cost computed. Cost: 3105804.7814488043\n",
      "station ('6', '21') eq cost computed. Cost: 3100280.8250464234\n",
      "station ('2', '6') eq cost computed. Cost: 2740787.0429213527\n",
      "station ('8', '10') eq cost computed. Cost: 2992904.90461461\n",
      "station ('2', '19') eq cost computed. Cost: 2810974.355027526\n",
      "station ('5', '24') eq cost computed. Cost: 2939032.4893607944\n",
      "station ('22', '21') eq cost computed. Cost: 4231493.348292817\n",
      "station ('18', '21') eq cost computed. Cost: 2916820.3879457964\n",
      "station ('4', '12') eq cost computed. Cost: 4113036.687309607\n",
      "station ('11', '9') eq cost computed. Cost: 4090260.648268242\n",
      "station ('15', '17') eq cost computed. Cost: 4125103.0705013773\n",
      "station ('8', '22') eq cost computed. Cost: 3016534.3003746737\n",
      "station ('11', '16') eq cost computed. Cost: 3776236.1874364736\n",
      "station ('2', '4') eq cost computed. Cost: 4177820.2906937418\n",
      "station ('7', '17') eq cost computed. Cost: 4117287.592874848\n",
      "station ('10', '21') eq cost computed. Cost: 3892717.7600248517\n",
      "station ('5', '16') eq cost computed. Cost: 3698360.470498845\n",
      "station ('14', '23') eq cost computed. Cost: 4072960.333856336\n",
      "station ('1', '13') eq cost computed. Cost: 4164279.788176276\n",
      "station ('3', '15') eq cost computed. Cost: 3663133.5116177555\n",
      "station ('16', '14') eq cost computed. Cost: 3657691.5918616047\n",
      "station ('6', '20') eq cost computed. Cost: 3895776.6378831104\n",
      "station ('11', '18') eq cost computed. Cost: 3617045.491793625\n",
      "station ('9', '24') eq cost computed. Cost: 3799050.1415035175\n",
      "station ('11', '22') eq cost computed. Cost: 3461265.6635037116\n",
      "station ('12', '22') eq cost computed. Cost: 3657888.4370293226\n",
      "station ('8', '13') eq cost computed. Cost: 3641508.5308945226\n",
      "station ('1', '6') eq cost computed. Cost: 3546203.6640423997\n",
      "station ('12', '13') eq cost computed. Cost: 3630577.7916436046\n",
      "station ('12', '16') eq cost computed. Cost: 3512960.9119661967\n",
      "station ('8', '14') eq cost computed. Cost: 3474872.5257340157\n",
      "station ('15', '22') eq cost computed. Cost: 3412528.7405150034\n",
      "station ('7', '16') eq cost computed. Cost: 3760312.5733848587\n",
      "station ('5', '14') eq cost computed. Cost: 3541119.2786531434\n",
      "station ('2', '10') eq cost computed. Cost: 3475799.611208159\n",
      "station ('5', '8') eq cost computed. Cost: 3419441.719917038\n",
      "station ('1', '17') eq cost computed. Cost: 3397205.331278997\n",
      "station ('17', '14') eq cost computed. Cost: 3599850.0892346627\n",
      "station ('3', '18') eq cost computed. Cost: 3191714.610924643\n",
      "station ('1', '23') eq cost computed. Cost: 3298202.984284025\n",
      "station ('2', '15') eq cost computed. Cost: 3457691.0346746426\n",
      "station ('11', '13') eq cost computed. Cost: 3364934.0987160844\n",
      "station ('8', '18') eq cost computed. Cost: 3400083.175616792\n",
      "station ('11', '8') eq cost computed. Cost: 3325155.9707675674\n",
      "station ('7', '15') eq cost computed. Cost: 3481618.6878708936\n",
      "station ('11', '24') eq cost computed. Cost: 3210873.6512589254\n",
      "station ('9', '22') eq cost computed. Cost: 2996401.181557186\n",
      "station ('24', '22') eq cost computed. Cost: 3211534.8428113833\n",
      "station ('6', '4') eq cost computed. Cost: 3347477.4504955215\n",
      "station ('9', '8') eq cost computed. Cost: 3230496.8373027123\n",
      "station ('18', '19') eq cost computed. Cost: 3214670.449991772\n",
      "station ('3', '9') eq cost computed. Cost: 3075439.349005794\n",
      "station ('19', '22') eq cost computed. Cost: 3405610.3553526644\n",
      "station ('4', '20') eq cost computed. Cost: 3044017.559181534\n",
      "station ('16', '21') eq cost computed. Cost: 2893878.6200240343\n",
      "station ('3', '22') eq cost computed. Cost: 3125129.2515394525\n",
      "station ('2', '9') eq cost computed. Cost: 3282212.2144510937\n",
      "station ('9', '13') eq cost computed. Cost: 2997944.217229634\n",
      "station ('15', '13') eq cost computed. Cost: 3107076.7070850786\n",
      "station ('7', '10') eq cost computed. Cost: 3008022.92529013\n",
      "station ('5', '13') eq cost computed. Cost: 3263105.623773303\n",
      "station ('13', '20') eq cost computed. Cost: 2948336.41758611\n",
      "station ('17', '21') eq cost computed. Cost: 2872368.3184365323\n",
      "station ('11', '15') eq cost computed. Cost: 3095148.8170304936\n",
      "station ('8', '21') eq cost computed. Cost: 3143530.0587053667\n",
      "station ('3', '24') eq cost computed. Cost: 2932104.314419902\n",
      "station ('1', '4') eq cost computed. Cost: 3186372.2506186776\n",
      "station ('12', '15') eq cost computed. Cost: 3094169.555226963\n",
      "station ('2', '11') eq cost computed. Cost: 2964438.535907804\n",
      "station ('3', '8') eq cost computed. Cost: 2893565.899181333\n",
      "station ('4', '7') eq cost computed. Cost: 2950136.028037317\n",
      "station ('2', '22') eq cost computed. Cost: 2813812.2924421052\n",
      "station ('12', '9') eq cost computed. Cost: 3885554.4230022426\n",
      "station ('2', '17') eq cost computed. Cost: 2995700.465738688\n",
      "station ('18', '20') eq cost computed. Cost: 2878472.351408559\n",
      "station ('10', '14') eq cost computed. Cost: 3079692.23478575\n",
      "station ('16', '19') eq cost computed. Cost: 2954396.927388742\n",
      "station ('22', '20') eq cost computed. Cost: 2817206.2378990646\n",
      "station ('6', '10') eq cost computed. Cost: 4070246.577547827\n",
      "station ('20', '21') eq cost computed. Cost: 2778757.608461051\n",
      "station ('12', '24') eq cost computed. Cost: 3646692.4810269075\n",
      "station ('10', '20') eq cost computed. Cost: 3847296.311255132\n",
      "station ('1', '9') eq cost computed. Cost: 3971960.890113645\n",
      "station ('6', '11') eq cost computed. Cost: 2954225.7792015187\n",
      "station ('24', '21') eq cost computed. Cost: 4195263.91385513\n",
      "station ('16', '20') eq cost computed. Cost: 3955641.4095755927\n",
      "station ('2', '8') eq cost computed. Cost: 2855223.9708700194\n",
      "station ('1', '16') eq cost computed. Cost: 3726909.0983072375\n",
      "station ('17', '19') eq cost computed. Cost: 3646692.451059699\n",
      "station ('12', '18') eq cost computed. Cost: 3491933.814787627\n",
      "station ('3', '7') eq cost computed. Cost: 3679848.3488317803\n",
      "station ('1', '22') eq cost computed. Cost: 3510093.386585237\n",
      "station ('3', '21') eq cost computed. Cost: 3808656.767377078\n",
      "station ('9', '18') eq cost computed. Cost: 3567525.2566589597\n",
      "station ('7', '22') eq cost computed. Cost: 3535575.5410490544\n",
      "station ('14', '13') eq cost computed. Cost: 3522374.665479773\n",
      "station ('1', '24') eq cost computed. Cost: 3283746.993757573\n",
      "station ('9', '14') eq cost computed. Cost: 3470960.1168596307\n",
      "station ('3', '14') eq cost computed. Cost: 3405091.0187658663\n",
      "station ('4', '10') eq cost computed. Cost: 3550188.453058757\n",
      "station ('8', '7') eq cost computed. Cost: 3553981.978559087\n",
      "station ('6', '17') eq cost computed. Cost: 3433334.4102555094\n",
      "station ('10', '19') eq cost computed. Cost: 3398775.723115538\n",
      "station ('5', '7') eq cost computed. Cost: 3180004.759303212\n",
      "station ('24', '19') eq cost computed. Cost: 3351849.0190533428\n",
      "station ('9', '20') eq cost computed. Cost: 3364438.0568301333\n",
      "station ('2', '13') eq cost computed. Cost: 3470677.5255746176\n",
      "station ('17', '20') eq cost computed. Cost: 3516599.932579184\n",
      "station ('3', '12') eq cost computed. Cost: 3369366.1923777843\n",
      "station ('2', '16') eq cost computed. Cost: 3241960.1810270944\n",
      "station ('15', '24') eq cost computed. Cost: 3046928.115507931\n",
      "station ('1', '8') eq cost computed. Cost: 3309422.6148041766\n",
      "station ('3', '5') eq cost computed. Cost: 3277086.8713011206\n",
      "station ('6', '16') eq cost computed. Cost: 3296530.4099096134\n",
      "station ('15', '14') eq cost computed. Cost: 3432555.2201705966\n",
      "station ('13', '21') eq cost computed. Cost: 3162141.1612519664\n",
      "station ('11', '7') eq cost computed. Cost: 3088728.737454623\n",
      "station ('1', '15') eq cost computed. Cost: 3204801.862478763\n",
      "station ('13', '19') eq cost computed. Cost: 2987493.1370234718\n",
      "station ('2', '14') eq cost computed. Cost: 3187483.261090992\n",
      "station ('6', '23') eq cost computed. Cost: 3217889.694694219\n",
      "station ('7', '13') eq cost computed. Cost: 3074719.638490305\n",
      "station ('23', '22') eq cost computed. Cost: 3121324.8211019025\n",
      "station ('7', '24') eq cost computed. Cost: 2993948.812661246\n",
      "station ('8', '20') eq cost computed. Cost: 3071858.750325338\n",
      "station ('18', '17') eq cost computed. Cost: 3083747.1867774683\n",
      "station ('11', '21') eq cost computed. Cost: 2889099.6881445446\n",
      "station ('14', '24') eq cost computed. Cost: 3149891.9162194803\n",
      "station ('4', '11') eq cost computed. Cost: 3025841.7847917955\n",
      "station ('12', '8') eq cost computed. Cost: 2867224.293929391\n",
      "station ('18', '23') eq cost computed. Cost: 2961295.1306684846\n",
      "station ('18', '10') eq cost computed. Cost: 3007580.4705004874\n",
      "station ('14', '22') eq cost computed. Cost: 2853639.870980282\n",
      "station ('6', '15') eq cost computed. Cost: 3038099.9870207026\n",
      "station ('11', '14') eq cost computed. Cost: 2969367.6103340853\n",
      "station ('3', '19') eq cost computed. Cost: 2851188.5903329905\n",
      "station ('4', '19') eq cost computed. Cost: 2954374.537151938\n",
      "station ('5', '18') eq cost computed. Cost: 2838203.6139428876\n",
      "station ('5', '20') eq cost computed. Cost: 2894930.6146434196\n",
      "station ('1', '18') eq cost computed. Cost: 2835823.507815282\n"
     ]
    }
   ],
   "source": [
    "import multiprocessing as mp\n",
    "\n",
    "# Step 3: Define the number of processes to use\n",
    "num_processes = mp.cpu_count()\n",
    "\n",
    "# Step 4: Create a multiprocessing pool object with the desired number of processes\n",
    "pool = mp.Pool(num_processes)\n",
    "\n",
    "# Step 5: Map the function to the input data using the multiprocessing pool\n",
    "all_costs = pool.starmap(compute_eq_cost, station_graph_tuples)\n",
    "\n",
    "# Step 6: Close the pool to free up resources\n",
    "pool.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_bike_cost = fwResult['z'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_cost_df = pd.DataFrame({'stations':stations2, 'cost':all_costs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count of node pairs')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "\n",
    "fig = ((station_cost_df.cost-  no_bike_cost)*100/no_bike_cost).hist(bins=25)\n",
    "\n",
    "fig.set_xlabel(\"% Change in cost\")\n",
    "fig.set_ylabel(\"Count of node pairs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute degree centrality for all nodes\n",
    "out_degree_centrality = nx.out_degree_centrality(siouxFalls.graph)\n",
    "in_degree_centrality = nx.in_degree_centrality(siouxFalls.graph)\n",
    "\n",
    "# compute betweenness centrality for all nodes\n",
    "betweenness_centrality = nx.betweenness_centrality(siouxFalls.graph)\n",
    "\n",
    "# compute eigenvector centrality for all nodes\n",
    "eigenvector_centrality = nx.eigenvector_centrality(siouxFalls.graph)\n",
    "\n",
    "# closeness_centrality\n",
    "closeness_centrality = nx.closeness_centrality(siouxFalls.graph)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "centrality = pd.DataFrame([betweenness_centrality, out_degree_centrality, in_degree_centrality, eigenvector_centrality, closeness_centrality]).T.rename(columns={0:'betweenness',1:'out_degree', 2:'in_degree', 3:'eigenvector', 4:'closeness'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "station1 = []\n",
    "station2 = []\n",
    "for station in station_cost_df.stations:\n",
    "    station1.append(station[0])\n",
    "    station2.append(station[1])\n",
    "\n",
    "station_cost_df['station1'] = station1\n",
    "station_cost_df['station2'] = station2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stations</th>\n",
       "      <th>cost</th>\n",
       "      <th>station1</th>\n",
       "      <th>station2</th>\n",
       "      <th>betweenness</th>\n",
       "      <th>out_degree</th>\n",
       "      <th>in_degree</th>\n",
       "      <th>eigenvector</th>\n",
       "      <th>closeness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(2, 5)</td>\n",
       "      <td>4.172028e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.036797</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.041609</td>\n",
       "      <td>0.267442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(4, 23)</td>\n",
       "      <td>3.907821e+06</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>0.100838</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.128455</td>\n",
       "      <td>0.348485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(11, 20)</td>\n",
       "      <td>3.489095e+06</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>0.226379</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.239538</td>\n",
       "      <td>0.403509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(15, 20)</td>\n",
       "      <td>3.375514e+06</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>0.122476</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.317121</td>\n",
       "      <td>0.370968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(13, 23)</td>\n",
       "      <td>3.283128e+06</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>0.060277</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.066920</td>\n",
       "      <td>0.306667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>(9, 14)</td>\n",
       "      <td>3.470960e+06</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>0.075878</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.208734</td>\n",
       "      <td>0.365079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>(24, 19)</td>\n",
       "      <td>3.351849e+06</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>0.070422</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.122064</td>\n",
       "      <td>0.302632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>(6, 16)</td>\n",
       "      <td>3.296530e+06</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>0.097666</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.110150</td>\n",
       "      <td>0.310811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>(2, 14)</td>\n",
       "      <td>3.187483e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0.036797</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.041609</td>\n",
       "      <td>0.267442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>(18, 17)</td>\n",
       "      <td>3.083747e+06</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>0.097956</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.194776</td>\n",
       "      <td>0.343284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     stations          cost station1 station2  betweenness  out_degree  \\\n",
       "0      (2, 5)  4.172028e+06        2        5     0.036797    0.086957   \n",
       "1     (4, 23)  3.907821e+06        4       23     0.100838    0.130435   \n",
       "2    (11, 20)  3.489095e+06       11       20     0.226379    0.173913   \n",
       "3    (15, 20)  3.375514e+06       15       20     0.122476    0.173913   \n",
       "4    (13, 23)  3.283128e+06       13       23     0.060277    0.086957   \n",
       "..        ...           ...      ...      ...          ...         ...   \n",
       "271   (9, 14)  3.470960e+06        9       14     0.075878    0.130435   \n",
       "272  (24, 19)  3.351849e+06       24       19     0.070422    0.130435   \n",
       "273   (6, 16)  3.296530e+06        6       16     0.097666    0.130435   \n",
       "274   (2, 14)  3.187483e+06        2       14     0.036797    0.086957   \n",
       "275  (18, 17)  3.083747e+06       18       17     0.097956    0.130435   \n",
       "\n",
       "     in_degree  eigenvector  closeness  \n",
       "0     0.086957     0.041609   0.267442  \n",
       "1     0.130435     0.128455   0.348485  \n",
       "2     0.173913     0.239538   0.403509  \n",
       "3     0.173913     0.317121   0.370968  \n",
       "4     0.086957     0.066920   0.306667  \n",
       "..         ...          ...        ...  \n",
       "271   0.130435     0.208734   0.365079  \n",
       "272   0.130435     0.122064   0.302632  \n",
       "273   0.130435     0.110150   0.310811  \n",
       "274   0.086957     0.041609   0.267442  \n",
       "275   0.130435     0.194776   0.343284  \n",
       "\n",
       "[276 rows x 9 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_cost_df.merge(centrality, how='left', left_on='station1', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_cost_centrality = station_cost_df.merge(centrality, how='left', left_on='station1', right_index=True).merge(centrality, how='left', left_on='station2', right_index=True,  suffixes=(\"_o\", \"_d\"))\n",
    "station_cost_centrality['int_in_degree_centrality'] = station_cost_centrality.in_degree_o * station_cost_centrality.in_degree_d\n",
    "station_cost_centrality['int_out_degree_centrality'] = station_cost_centrality.out_degree_o * station_cost_centrality.out_degree_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_cost_centrality['pct_change']  = (station_cost_centrality.cost - no_bike_cost)/no_bike_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sum_edge_volumes(n, graph):\n",
    "    volume_sum = 0\n",
    "    for neighbor in graph.neighbors(n):\n",
    "        volume_sum += graph[n][neighbor]['object'].vol\n",
    "    return volume_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "def calculate_hypothetical_travel_time_reduction(hyp_edge, graph, traffic_dict):\n",
    "    # Copy the original graph\n",
    "    new_graph = graph.copy()\n",
    "\n",
    "    # Add the hypothetical edge to the new graph\n",
    "    shortest_path = nx.shortest_path(graph, hyp_edge[0], hyp_edge[1], weight='time')\n",
    "    edge_time = 0\n",
    "    for i in range(len(shortest_path) - 1):\n",
    "        edge_time += new_graph[shortest_path[i]][shortest_path[i+1]]['time']\n",
    "    new_graph.add_edge(hyp_edge[0], hyp_edge[1], time=edge_time*.5)\n",
    "\n",
    "    # Calculate the original travel time\n",
    "    orig_travel_time = 0\n",
    "    for origin, dest in traffic_dict.keys():\n",
    "        volume = traffic_dict[(origin, dest)]\n",
    "        shortest_path = nx.shortest_path(graph, origin, dest, weight='time')\n",
    "        edge_time = 0\n",
    "        for i in range(len(shortest_path) - 1):\n",
    "            edge_time += graph[shortest_path[i]][shortest_path[i+1]]['time']\n",
    "        orig_travel_time += edge_time * volume\n",
    "\n",
    "    # Calculate the hypothetical travel time\n",
    "    hyp_travel_time = 0\n",
    "    for origin, dest in traffic_dict.keys():\n",
    "        volume = traffic_dict[(origin, dest)]\n",
    "        shortest_path = nx.shortest_path(new_graph, origin, dest, weight='time')\n",
    "        edge_time = 0\n",
    "        for i in range(len(shortest_path) - 1):\n",
    "            edge_time += new_graph[shortest_path[i]][shortest_path[i+1]]['time']\n",
    "        hyp_travel_time += edge_time * volume\n",
    "\n",
    "    # Calculate the travel time reduction\n",
    "    travel_time_reduction = orig_travel_time - hyp_travel_time\n",
    "\n",
    "    return travel_time_reduction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_cost_centrality['volume_x'] = station_cost_centrality.station1.apply(sum_edge_volumes, graph=siouxFalls.graph)\n",
    "station_cost_centrality['volume_y'] = station_cost_centrality.station2.apply(sum_edge_volumes, graph=siouxFalls.graph)\n",
    "station_cost_centrality['hyp_time_reduction'] = station_cost_centrality.stations.apply( calculate_hypothetical_travel_time_reduction, graph= siouxFalls.graph, traffic_dict= siouxFalls.od_vols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_cost_centrality['log_hyp_time_reduction'] = np.log((station_cost_centrality.hyp_time_reduction / np.linalg.norm(station_cost_centrality.hyp_time_reduction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_cost_centrality['shortest_path_length'] = station_cost_centrality.apply(lambda x: nx.shortest_path_length(siouxFalls.graph, x.station1, x.station2, weight='time'), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['stations', 'cost', 'station1', 'station2', 'betweenness_o',\n",
       "       'out_degree_o', 'in_degree_o', 'eigenvector_o', 'closeness_o',\n",
       "       'betweenness_d', 'out_degree_d', 'in_degree_d', 'eigenvector_d',\n",
       "       'closeness_d', 'int_in_degree_centrality', 'int_out_degree_centrality',\n",
       "       'pct_change', 'volume_x', 'volume_y', 'hyp_time_reduction',\n",
       "       'log_hyp_time_reduction', 'shortest_path_length'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_cost_centrality.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_cost_centrality.columns = ['stations', 'cost', 'station1', 'station2', 'betweenness_1',\n",
    "       'out_degree_1', 'degree_1', 'eigenvector_1', 'closeness_1',\n",
    "       'betweenness_2', 'degree_2', 'in_degree_2', 'eigenvector_2',\n",
    "       'closeness_2', 'int_degree_centrality', 'int_out_degree_centrality',\n",
    "       'pct_change', 'volume_1', 'volume_2', 'hyp_time_reduction',\n",
    "       'log_hyp_time_reduction', 'shortest_path_length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "X_df = station_cost_centrality[['betweenness_1','betweenness_2', \n",
    "       'degree_1', 'degree_2', \n",
    "       'eigenvector_1', 'eigenvector_2', \n",
    "        'closeness_1','closeness_2', \n",
    "        'int_degree_centrality',\n",
    "       'volume_1', 'volume_2', \n",
    "       'shortest_path_length']]\n",
    "y = station_cost_centrality[['pct_change']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now you can scale the data using any of the available methods, such as StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(X_df), columns=X_df.columns)\n",
    "\n",
    "scaled_df = sm.add_constant(scaled_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{center}\n",
      "\\begin{tabular}{lclc}\n",
      "\\toprule\n",
      "\\textbf{Dep. Variable:}          &   pct\\_change    & \\textbf{  R-squared:         } &     0.071   \\\\\n",
      "\\textbf{Model:}                  &       OLS        & \\textbf{  Adj. R-squared:    } &     0.029   \\\\\n",
      "\\textbf{Method:}                 &  Least Squares   & \\textbf{  F-statistic:       } &     1.683   \\\\\n",
      "\\textbf{Date:}                   & Tue, 04 Apr 2023 & \\textbf{  Prob (F-statistic):} &   0.0705    \\\\\n",
      "\\textbf{Time:}                   &     20:20:27     & \\textbf{  Log-Likelihood:    } &    298.91   \\\\\n",
      "\\textbf{No. Observations:}       &         276      & \\textbf{  AIC:               } &    -571.8   \\\\\n",
      "\\textbf{Df Residuals:}           &         263      & \\textbf{  BIC:               } &    -524.7   \\\\\n",
      "\\textbf{Df Model:}               &          12      & \\textbf{                     } &             \\\\\n",
      "\\textbf{Covariance Type:}        &    nonrobust     & \\textbf{                     } &             \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\\begin{tabular}{lcccccc}\n",
      "                                 & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
      "\\midrule\n",
      "\\textbf{const}                   &      -0.2067  &        0.005     &   -40.911  &         0.000        &       -0.217    &       -0.197     \\\\\n",
      "\\textbf{betweenness\\_1}          &       0.0183  &        0.014     &     1.336  &         0.183        &       -0.009    &        0.045     \\\\\n",
      "\\textbf{betweenness\\_2}          &      -0.0290  &        0.016     &    -1.832  &         0.068        &       -0.060    &        0.002     \\\\\n",
      "\\textbf{degree\\_1}               &      -0.0376  &        0.031     &    -1.213  &         0.226        &       -0.099    &        0.023     \\\\\n",
      "\\textbf{degree\\_2}               &       0.0046  &        0.029     &     0.161  &         0.872        &       -0.052    &        0.061     \\\\\n",
      "\\textbf{eigenvector\\_1}          &       0.0175  &        0.017     &     1.022  &         0.308        &       -0.016    &        0.051     \\\\\n",
      "\\textbf{eigenvector\\_2}          &      -0.0351  &        0.020     &    -1.710  &         0.088        &       -0.075    &        0.005     \\\\\n",
      "\\textbf{closeness\\_1}            &      -0.0065  &        0.016     &    -0.413  &         0.680        &       -0.037    &        0.024     \\\\\n",
      "\\textbf{closeness\\_2}            &       0.0416  &        0.015     &     2.699  &         0.007        &        0.011    &        0.072     \\\\\n",
      "\\textbf{int\\_degree\\_centrality} &       0.0233  &        0.033     &     0.711  &         0.478        &       -0.041    &        0.088     \\\\\n",
      "\\textbf{volume\\_1}               &      -0.0118  &        0.011     &    -1.080  &         0.281        &       -0.033    &        0.010     \\\\\n",
      "\\textbf{volume\\_2}               &      -0.0033  &        0.010     &    -0.336  &         0.737        &       -0.022    &        0.016     \\\\\n",
      "\\textbf{shortest\\_path\\_length}  &      -0.0101  &        0.006     &    -1.756  &         0.080        &       -0.021    &        0.001     \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\\begin{tabular}{lclc}\n",
      "\\textbf{Omnibus:}       & 15.090 & \\textbf{  Durbin-Watson:     } &    1.184  \\\\\n",
      "\\textbf{Prob(Omnibus):} &  0.001 & \\textbf{  Jarque-Bera (JB):  } &   10.809  \\\\\n",
      "\\textbf{Skew:}          &  0.369 & \\textbf{  Prob(JB):          } &  0.00450  \\\\\n",
      "\\textbf{Kurtosis:}      &  2.371 & \\textbf{  Cond. No.          } &     21.9  \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "%\\caption{OLS Regression Results}\n",
      "\\end{center}\n",
      "\n",
      "Notes: \\newline\n",
      " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "big_model = sm.OLS(station_cost_centrality['pct_change'], scaled_df).fit()\n",
    "latex_model = big_model.summary().as_latex()\n",
    "print(latex_model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison between the lowest and highest costs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# low 4- lowest costs\n",
    "low_4 = np.argsort(station_cost_centrality.cost)[0:4].values\n",
    "# top 4 - 4 highest costs\n",
    "top_4 = np.argsort(station_cost_centrality.cost)[-4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_cost_centrality.to_csv('station_cost_centrality.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "# create four color sets\n",
    "node_colors1 = ['red' if node in station_cost_centrality.loc[low_4].iloc[0].stations else 'gray' for node in siouxFalls.graph.nodes()]\n",
    "node_colors2 = ['red' if node in station_cost_centrality.loc[low_4].iloc[1].stations else 'gray' for node in siouxFalls.graph.nodes()]\n",
    "node_colors3 = ['red' if node in station_cost_centrality.loc[low_4].iloc[2].stations else 'gray' for node in siouxFalls.graph.nodes()]\n",
    "node_colors4 = ['red' if node in station_cost_centrality.loc[low_4].iloc[3].stations else 'gray' for node in siouxFalls.graph.nodes()]\n",
    "# create a 2x2 plot grid\n",
    "fig, axs = plt.subplots(2, 2, figsize=(7, 7))\n",
    "pos = nx.kamada_kawai_layout(siouxFalls.graph, weight='time')\n",
    "\n",
    "\n",
    "# plot each graph in a different subplot\n",
    "nx.draw(siouxFalls.graph.copy(), ax=axs[0, 0], pos=pos, node_color=node_colors1)\n",
    "nx.draw(siouxFalls.graph.copy(), ax=axs[0, 1],pos=pos, node_color=node_colors2)\n",
    "nx.draw(siouxFalls.graph.copy(), ax=axs[1, 0],pos=pos, node_color=node_colors3)\n",
    "nx.draw(siouxFalls.graph.copy(), ax=axs[1, 1],pos=pos, node_color=node_colors4)\n",
    "\n",
    "# these are the 4 lowest costs\n",
    "# set plot title and axis labels\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "# create four color sets\n",
    "node_colors1 = ['red' if node in station_cost_centrality.loc[top_4].iloc[0].stations else 'gray' for node in siouxFalls.graph.nodes()]\n",
    "node_colors2 = ['red' if node in station_cost_centrality.loc[top_4].iloc[1].stations else 'gray' for node in siouxFalls.graph.nodes()]\n",
    "node_colors3 = ['red' if node in station_cost_centrality.loc[top_4].iloc[2].stations else 'gray' for node in siouxFalls.graph.nodes()]\n",
    "node_colors4 = ['red' if node in station_cost_centrality.loc[top_4].iloc[3].stations else 'gray' for node in siouxFalls.graph.nodes()]\n",
    "# create a 2x2 plot grid\n",
    "fig, axs = plt.subplots(2, 2, figsize=(7, 7))\n",
    "pos = nx.kamada_kawai_layout(siouxFalls.graph, weight='time')\n",
    "\n",
    "\n",
    "# plot each graph in a different subplot\n",
    "nx.draw(siouxFalls.graph.copy(), ax=axs[0, 0], pos=pos, node_color=node_colors1)\n",
    "nx.draw(siouxFalls.graph.copy(), ax=axs[0, 1],pos=pos, node_color=node_colors2)\n",
    "nx.draw(siouxFalls.graph.copy(), ax=axs[1, 0],pos=pos, node_color=node_colors3)\n",
    "nx.draw(siouxFalls.graph.copy(), ax=axs[1, 1],pos=pos, node_color=node_colors4)\n",
    "\n",
    "# set plot title and axis labels\n",
    "\n",
    "# these are the four highest costs\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we actually plot path length and cost, the relationship is weaker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Closeness_2')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = station_cost_centrality.plot.scatter(y='pct_change', x='closeness_2')\n",
    "fig.set_ylabel('% Change in Cost')\n",
    "fig.set_xlabel('Closeness_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "centrality_dict = station_cost_centrality[['station1','closeness_x', 'betweenness_x', 'degree_x']].set_index('station1').to_dict(orient='dict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_topsis(closeness_centr,between_centr,degr_centr):\n",
    "    centralities=dict()\n",
    "    for i in zip(closeness_centr,closeness_centr.values(),between_centr.values(),degr_centr.values()):\n",
    "        centralities[i[0]]=i[1],i[2],i[3]\n",
    "    centrdf=pd.DataFrame.from_dict(centralities)\n",
    "    centrdf=centrdf.T\n",
    "    centrdf.columns=['cc','bc','dc']    \n",
    "    wv=np.array([0.1180,0.2689,0.6131])\n",
    "    wv=wv.reshape(-1,1)\n",
    "    centrmatrix=centrdf.copy().to_numpy()\n",
    "    centrmatrix[:,0]=(centrmatrix[:,0]*wv[0])/(np.sqrt(np.sum(np.square(centrmatrix[:,0]))))\n",
    "    centrmatrix[:,1]=(centrmatrix[:,1]*wv[1])/(np.sqrt(np.sum(np.square(centrmatrix[:,1]))))\n",
    "    centrmatrix[:,2]=(centrmatrix[:,2]*wv[2])/(np.sqrt(np.sum(np.square(centrmatrix[:,2]))))\n",
    "    nis=np.array([centrmatrix[:,0].min(),centrmatrix[:,1].min(),centrmatrix[:,2].min()])\n",
    "    pis=np.array([centrmatrix[:,0].max(),centrmatrix[:,1].max(),centrmatrix[:,2].max()])\n",
    "    centrdf['s+']=np.sum(np.square(centrmatrix-pis),axis=1)\n",
    "    centrdf['s-']=np.sum(np.square(centrmatrix-nis),axis=1)\n",
    "    # np.sum(np.square(centrmatrix-pis),axis=1)/(np.sum(np.square(centrmatrix-pis),axis=1)+np.sum(np.square(centrmatrix-nis),axis=1))\n",
    "    centrdf['topsis']=np.sum(np.square(centrmatrix-pis),axis=1)/(np.sum(np.square(centrmatrix-pis),axis=1)+np.sum(np.square(centrmatrix-nis),axis=1))\n",
    "    return centrdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "topsis = get_topsis(centrality_dict['closeness_x'], centrality_dict['betweenness_x'], centrality_dict['degree_x']).sort_values(by='topsis', ascending=False)\n",
    "station_cost_topsis = station_cost_centrality.merge(topsis[['topsis']], how='left', left_on='station1', right_index=True).merge(topsis[['topsis']], how='left', left_on='station2', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_cost_centrality.to_csv('station_cost_centrality_capacity.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "lowest_costs = pd.Series(stations2).iloc[top_4]\n",
    "\n",
    "station_graph_tuples = [(station, siouxFalls) for station in stations2]\n",
    "\n",
    "import multiprocessing as mp\n",
    "\n",
    "# Step 3: Define the number of processes to use\n",
    "num_processes = mp.cpu_count()\n",
    "\n",
    "# Step 4: Create a multiprocessing pool object with the desired number of processes\n",
    "pool = mp.Pool(num_processes)\n",
    "\n",
    "# Step 5: Map the function to the input data using the multiprocessing pool\n",
    "all_costs = pool.starmap(compute_eq_cost, station_graph_tuples)\n",
    "\n",
    "# Step 6: Close the pool to free up resources\n",
    "pool.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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