Adding a Vision2050 EIS long list summary

scripts/summarize/notebooks/Vision 2050 EIS - long list.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import collections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "mode_dict = {0: 'Other', 1:'Walk',2:'Bike',3:'SOV',4:'HOV2',5:'HOV3+',6:'Transit',8:'School Bus'}\n",
+    "annualization_factor = 300"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Relative path between notebooks and goruped output directories\n",
+    "relative_path = '../../../outputs'\n",
+    "output_vision_file = os.path.join(relative_path, 'Vision2050_longlist.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load network results\n",
+    "network_df = pd.read_excel(os.path.join(relative_path,'network/') + r'network_summary_detailed.xlsx',\n",
+    "                  sheetname='Network Summary')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load Daysim results\n",
+    "trip = pd.read_csv(os.path.join(relative_path,r'daysim/') + r'_trip.tsv', sep='\\t')\n",
+    "person = pd.read_csv(os.path.join(relative_path,r'daysim/') + r'_person.tsv', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Average HBW/ Non-HBW Trip Distance\n",
+    "trip['Trip Type'] = 'Not Home-Based Work'\n",
+    "trip.ix[(((trip['opurp']==0) & (trip['dpurp']==1)) | ((trip['opurp']==1) & (trip['dpurp']==0))),'Trip Type']= 'Home-Based Work'\n",
+    "hbw_trips = trip.loc[trip['Trip Type']=='Home-Based Work']\n",
+    "nhbw_trips = trip.loc[trip['Trip Type']=='Not Home-Based Work']\n",
+    "\n",
+    "output_dict = {'Average Commute Trip Length':hbw_trips['travdist'].mean()}\n",
+    "output_dict = collections.OrderedDict(output_dict)\n",
+    "output_dict['Average Other Trip Length']=nhbw_trips['travdist'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# VMT per person per day\n",
+    "network_df['all_facility_vmt'] = network_df['arterial_vmt']+ network_df['connectors_vmt']+ network_df['highway_vmt']\n",
+    "total_vmt = network_df['all_facility_vmt'].sum()\n",
+    "total_persons = person.sum()['psexpfac']\n",
+    "vmt_per_person = total_vmt/total_persons\n",
+    "output_dict['Daily VMT per person']=vmt_per_person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# VHT per person per day\n",
+    "network_df['all_facility_vht'] = network_df['arterial_vht']+ network_df['connectors_vht']+ network_df['highway_vht']\n",
+    "total_vht = network_df['all_facility_vht'].sum()\n",
+    "vht_per_person = total_vht/total_persons\n",
+    "output_dict['Daily VHT per person']= vht_per_person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Delay per person annually\n",
+    "network_df['all_facility_delay'] = network_df['arterial_delay']+ network_df['connectors_delay']+ network_df['highway_delay']\n",
+    "total_delay = network_df['all_facility_delay'].sum()\n",
+    "delay_per_person = (total_delay/total_persons)*annualization_factor\n",
+    "output_dict['Annual Delay Hours per Person']= vht_per_person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Total Delay Hours Daily by Facility Type\n",
+    "df_fac = pd.DataFrame(network_df.sum()[['arterial_delay','highway_delay']])\n",
+    "df_fac = df_fac.reset_index()\n",
+    "df_fac.columns = ['Facility Type', 'Delay']\n",
+    "#df_fac.index = df_fac['Facility Type']\n",
+    "#df_fac.drop('Facility Type', axis=1, inplace=True)\n",
+    "df_fac.loc['Total'] = df_fac.sum()\n",
+    "output_dict['Daily Arterial Delay Hours'] = df_fac['Delay'].loc[df_fac['Facility Type'] == 'arterial_delay'].values[0]\n",
+    "output_dict['Daily Highway Delay Hours'] = df_fac['Delay'].loc[df_fac['Facility Type'] == 'highway_delay'].values[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Daily Transit Boardings\n",
+    "transit_df = pd.read_excel(r'../../../outputs/network/network_summary_detailed.xlsx', sheetname='Transit Summaries')\n",
+    "tod_list = ['5to6','6to7','7to8','8to9','9to10','10to14','14to15','15to16','16to17','17to18','18to20']\n",
+    "transit_df = transit_df[[tod+'_board' for tod in tod_list]+['route_code']]\n",
+    "transit_df = transit_df.fillna(0)\n",
+    "transit_df['line_total'] = transit_df[[tod+'_board' for tod in tod_list]].sum(axis=1)\n",
+    "output_dict['Daily Transit Boardings']=transit_df['line_total'].sum()\n",
+    "output_df = pd.DataFrame(output_dict.items(), columns = ['Data Item', 'Value'])\n",
+    "output_df['Group'] = 'Total'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# All Trip Mode Share\n",
+    "model_df = trip[['mode','trexpfac']].groupby('mode').sum()[['trexpfac']]/trip[['trexpfac']].sum()\n",
+    "model_df = model_df.reset_index()\n",
+    "\n",
+    "model_df.replace({'mode':mode_dict}, inplace=True)\n",
+    "model_df.columns = ['Data Item', 'Value']\n",
+    "model_df['Group'] = 'All Trip Mode Share'\n",
+    "\n",
+    "output_df =pd.concat([output_df, model_df])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Commute Trip Mode Share\n",
+    "model_df_hbw = hbw_trips[['mode','trexpfac']].groupby('mode').sum()[['trexpfac']]/trip[['trexpfac']].sum()\n",
+    "model_df_hbw = model_df_hbw.reset_index()\n",
+    "model_df_hbw.replace({'mode':mode_dict}, inplace=True)\n",
+    "model_df_hbw.columns = ['Data Item', 'Value']\n",
+    "model_df_hbw['Group'] = 'Commute Trip Mode Share'\n",
+    "\n",
+    "output_df =pd.concat([output_df, model_df_hbw])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Non-Commute Trip Mode Share\n",
+    "model_df_nhbw = nhbw_trips[['mode','trexpfac']].groupby(['mode']).sum()[['trexpfac']]/nhbw_trips[['trexpfac']].sum()\n",
+    "model_df_nhbw.reset_index(inplace=True)\n",
+    "model_df_nhbw.replace({'mode':mode_dict}, inplace=True)\n",
+    "model_df_nhbw.columns = ['Data Item', 'Value']\n",
+    "model_df_nhbw['Group'] = 'Non-Commute Trip Mode Share'\n",
+    "\n",
+    "output_df =pd.concat([output_df, model_df_nhbw])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Number and Percent of People Walking, Biking, or Transiting\n",
+    "trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')\n",
+    "bike_walk_t_trips = trip_person[trip_person['mode'].isin([1,2,6])]\n",
+    "\n",
+    "df = bike_walk_t_trips.groupby(['hhno','pno']).count()\n",
+    "df = df.reset_index()\n",
+    "df = df[['hhno','pno']]\n",
+    "df['bike_walk_t'] = True\n",
+    "\n",
+    "df = pd.merge(person,df,on=['hhno','pno'], how='left')\n",
+    "df['bike_walk_t'] = df['bike_walk_t'].fillna(False)\n",
+    "\n",
+    "df_share = pd.DataFrame(df.groupby('bike_walk_t').sum()['psexpfac']/df['psexpfac'].sum())\n",
+    "df_total =  pd.DataFrame(df.groupby('bike_walk_t').sum()['psexpfac'])\n",
+    "bike_walk_dict= {'Share of People Walking, Biking, and Using Transit':df_share.loc[True]['psexpfac'], \n",
+    "                  'Total People Walking, Biking, and Using Transit':df_total.loc[True]['psexpfac']}\n",
+    "bike_walk_df = pd.DataFrame(bike_walk_dict.items(), columns = ['Data Item', 'Value'])\n",
+    "\n",
+    "bike_walk_df['Group'] = 'Total'\n",
+    "output_df =pd.concat([output_df, bike_walk_df])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "output_df.to_csv(output_vision_file, index= False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
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+}