Final Report Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d21fc19d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "\n",
    "from datetime import datetime, timezone\n",
    "from dateutil import tz\n",
    "\n",
    "import branca.colormap as cm\n",
    "import folium\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pytz\n",
    "import seaborn as sns\n",
    "\n",
    "TZ = pytz.timezone('America/Los_Angeles')\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b149fb23",
   "metadata": {},
   "outputs": [],
   "source": [
    "corridor_segments = {\n",
    "    0: {\n",
    "        2749,\n",
    "        2751,\n",
    "        2752,\n",
    "        2753,\n",
    "        2754,\n",
    "        2755,\n",
    "        2756,\n",
    "        2757\n",
    "    },\n",
    "    1: {\n",
    "        12200,\n",
    "        12201,\n",
    "        12202,\n",
    "        12203,\n",
    "        12204\n",
    "    },\n",
    "    2: {\n",
    "        13532,\n",
    "        13533,\n",
    "        13534,\n",
    "        13535,\n",
    "        13536\n",
    "    },\n",
    "    3: {\n",
    "        14585,\n",
    "        14586,\n",
    "        14587,\n",
    "        14589,\n",
    "        14590,\n",
    "        14591,\n",
    "        14243\n",
    "    },\n",
    "    4: {\n",
    "        8510,\n",
    "        8511,\n",
    "        8512,\n",
    "        8513,\n",
    "        8514\n",
    "    },\n",
    "    5: {\n",
    "        13221,\n",
    "        13220,\n",
    "        13219,\n",
    "        13218,\n",
    "        13217\n",
    "    },\n",
    "    6: {\n",
    "        18045,\n",
    "        18046,\n",
    "        18047,\n",
    "        18048,\n",
    "        18049\n",
    "    },\n",
    "    7: {\n",
    "        19235,\n",
    "        19234,\n",
    "        11333,\n",
    "        11331,\n",
    "        11330,\n",
    "        11329,\n",
    "        19212\n",
    "    },\n",
    "    8: {\n",
    "        12369,\n",
    "        12366,\n",
    "        12365,\n",
    "        12364,\n",
    "        12363\n",
    "    }\n",
    "}\n",
    "corridor_names = ['2nd Ave; Pike to James',\n",
    "                 'Pike; 3rd to 9th',\n",
    "                 'Westlake; Denny to Mercer',\n",
    "                 'E Olive/E John; Denny to 10th',\n",
    "                 '9th Ave; Alder to Columbia',\n",
    "                 'University Way; Campus Pkwy to 45th',\n",
    "                 'Pacific; 15th to Montlake',\n",
    "                 'NW Market/Leary; 24th to 15th',\n",
    "                 'Rainier; S Bayview to MLK',\n",
    "                 '108th Ave; 4th to 12th']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4d4f2a",
   "metadata": {},
   "source": [
    "#### Aggregate Files Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b76d6e80",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#### Aggregate Files Approach# # Summarize days into months of the year\n",
    "# path = './transit_vis/data/to_upload/2021'\n",
    "# folders = os.listdir(path)\n",
    "# for folder in folders:\n",
    "#     if folder.startswith('.'):\n",
    "#         continue\n",
    "#     print(folder)\n",
    "#     files = os.listdir(f\"{path}/{folder}\")\n",
    "#     # Join all the data which is saved in different csv for each day\n",
    "#     frames = [pd.read_csv(f\"{path}/{folder}/{filename}\", dtype={'trip_short_name': str, 'route_short_name': str}) for filename in files]\n",
    "#     all_data = pd.concat(frames)\n",
    "#     all_data.to_csv(f\"{path}/{folder}/../all_data_{folder}.csv\")\n",
    "\n",
    "# # Summarize the individual months into single file\n",
    "# path = './transit_vis/data/to_upload/2021'\n",
    "# files = os.listdir(path)\n",
    "# new_files = []\n",
    "# for filename in files:\n",
    "#     if filename.startswith('a'):\n",
    "#         new_files.append(filename)\n",
    "# # Join all the data which is saved in different csv for each day\n",
    "# frames = [pd.read_csv(f\"{path}/{filename}\", dtype={'trip_short_name': str, 'route_short_name': str}) for filename in new_files]\n",
    "# all_data = pd.concat(frames)\n",
    "# all_data.to_csv(f\"{path}/../all_data.csv\")\n",
    "\n",
    "# If already processed, load the data here and comment out above\n",
    "# path = './transit_vis/data/to_upload/2021'\n",
    "# all_data = pd.read_csv(f\"{path}/../all_data.csv\", dtype={'trip_short_name': str, 'route_short_name': str}, chunksize=)\n",
    "\n",
    "# # Rename and style\n",
    "# all_data['speed_m_s'] = all_data['avg_speed_m_s']\n",
    "# all_data = all_data[all_data['at_stop'] == True]\n",
    "# sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "624b8968",
   "metadata": {},
   "source": [
    "#### Summarize Samples of All Files Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aac16e2c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# read_cols = ['tripid',\n",
    "#              'dist_diff',\n",
    "#              'speed_m_s',\n",
    "#              'deviation_change_s',\n",
    "#              'seg_compkey']\n",
    "# col_types = {'tripid':int,\n",
    "#              'dist_diff':float,\n",
    "#              'speed_m_s':float,\n",
    "#              'deviation_change_s':int,\n",
    "#              'seg_compkey':int}\n",
    "\n",
    "# all_data = []\n",
    "# test_samples = []\n",
    "# chunksize = 10 ** 5\n",
    "\n",
    "# def ff_percentile(data):\n",
    "#     return np.percentile(data, 95)\n",
    "\n",
    "# def process(chunk):\n",
    "#     # Calculate Pace\n",
    "#     chunk['speed_mph'] = chunk['speed_m_s']*2.237\n",
    "#     chunk['pace_s_mi'] = 1 / (chunk['speed_m_s']*.0006214)\n",
    "\n",
    "#     # Add date to the data\n",
    "# #     chunk['day'] = pd.to_datetime(chunk['locationtime'], unit='s')\n",
    "# #     chunk['day'] = chunk['day'].dt.strftime('%Y-%m-%d')\n",
    "    \n",
    "#     # Get 95th percentile pace for segments\n",
    "#     freeflow = chunk[['seg_compkey','pace_s_mi']].groupby('seg_compkey').agg(ff_percentile).reset_index()    \n",
    "#     chunk = pd.merge(chunk, freeflow, on='seg_compkey', suffixes=[None, '_95'])\n",
    "        \n",
    "#     # Calculate metrics\n",
    "#     chunk['delay_stochastic'] = chunk['deviation_change_s']\n",
    "#     chunk['delay_total'] = (chunk['pace_s_mi_95'] - chunk['pace_s_mi']) * chunk['dist_diff'] * 0.0006214\n",
    "#     chunk['delay_systematic'] = chunk['delay_total'] - chunk['delay_stochastic']\n",
    "    \n",
    "#     # Aggregate by segments\n",
    "#     # Save 10% of data as samples\n",
    "#     return [chunk.sample(int(len(chunk)/10)), chunk.groupby('seg_compkey').agg([np.median,\n",
    "#                                              ff_percentile,\n",
    "#                                              pd.Series.count])]\n",
    "\n",
    "# # Read each month of data, a day at a time, broken into chunks for faster processing\n",
    "# # Calculate segment metrics + 10% sample from each chunk, and save\n",
    "# path = './transit_vis/data/to_upload/2021'\n",
    "# for month_num in range(1,13):\n",
    "#     print(f\"Month: {month_num}\")\n",
    "#     files = os.listdir(f\"{path}/{month_num}_21\")\n",
    "#     for day_num in files:\n",
    "#         print(f\"Day: {day_num}\")\n",
    "#         i = 0\n",
    "#         for chunk in pd.read_csv(f\"{path}/{month_num}_21/{day_num}\", chunksize=chunksize, usecols=read_cols, dtype=col_types):\n",
    "#             i+=1\n",
    "#             print(f\"Chunk: {i}\")\n",
    "#             result = process(chunk)\n",
    "#             test_samples.append(result[0])\n",
    "#             all_data.append(result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7210efea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Combine metrics calculated for each segment across all chunks\n",
    "# data = pd.concat(all_data).dropna().groupby('seg_compkey').mean()\n",
    "\n",
    "# # Save segment values for GIS mapping\n",
    "# segment_metrics_data = data[[('delay_total','median'),\n",
    "#                              ('delay_systematic','median'),\n",
    "#                              ('delay_stochastic','median'),\n",
    "#                              ('pace_s_mi','median')]]\n",
    "# segment_metrics_data = segment_metrics_data[segment_metrics_data < np.inf]\n",
    "# segment_metrics_data = segment_metrics_data[segment_metrics_data > -np.inf]\n",
    "# segment_metrics_data = segment_metrics_data.dropna().reset_index()\n",
    "# segment_metrics_data.columns = segment_metrics_data.columns.droplevel(1)\n",
    "\n",
    "# # SR520 Segment\n",
    "# x = [int(99999), int(99999), 5.0, 50.0, -45.0, 60.0]\n",
    "# df_length = len(segment_metrics_data)\n",
    "# segment_metrics_data.loc[df_length] = x\n",
    "\n",
    "# segment_metrics_data = segment_metrics_data.append(x, ignore_index=True)\n",
    "# segment_metrics_data.to_csv(f\"{path}/../segment_metrics_data.csv\")\n",
    "\n",
    "# # Combine samples to get 10% of overall dataset stored as individual delays\n",
    "# sample_data = pd.concat(test_samples)\n",
    "\n",
    "# # Save sample data for analysis in jupyter\n",
    "# sample_data = sample_data[sample_data < np.inf]\n",
    "# sample_data = sample_data[sample_data > -np.inf]\n",
    "# sample_data.dropna(inplace=True)\n",
    "# sample_data.to_csv(f\"{path}/../sample_metrics_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4259fc58",
   "metadata": {},
   "source": [
    "### Distributions of Key Characteristics/Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9188740",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data processed above using all files approach\n",
    "path = './transit_vis/data/to_upload/2021'\n",
    "# sample_data = pd.read_csv(f\"{path}/../sample_metrics_data.csv\")\n",
    "segment_data = pd.read_csv(f\"{path}/../segment_metrics_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c279b297",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sample_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/6p/76scpql109v8rhf5gm61sr4c0000gn/T/ipykernel_7122/4196331867.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'delay_stochastic'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'delay_systematic'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'delay_total'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'delay_stochastic'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sample_data' is not defined"
     ]
    }
   ],
   "source": [
    "print(np.median(sample_data['delay_stochastic']))\n",
    "print(np.median(sample_data['delay_systematic']))\n",
    "print(np.median(sample_data['delay_total']))\n",
    "print()\n",
    "print(np.std(sample_data['delay_stochastic']))\n",
    "print(np.std(sample_data['delay_systematic']))\n",
    "print(np.std(sample_data['delay_total']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9696036d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sample_data['delay_stochastic'].dropna()\n",
    "x = x[x < np.inf]\n",
    "x = x[x > -np.inf]\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,4))\n",
    "sns.histplot(x.values, binwidth=1, stat='density', ax=ax1).set(xlabel='Delay (s)', xlim=(-120,120), title=f\"Sample Change in Schedule Deviation\")\n",
    "sns.histplot(segment_data['delay_stochastic'], stat='density').set(xlabel='Delay (s)', title=f\"Segment Aggregated Change in Schedule Deviations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "165b9a49",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sample_data['delay_systematic'].dropna()\n",
    "x = x[x < np.inf]\n",
    "x = x[x > -np.inf]\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,4))\n",
    "sns.histplot(x.values, ax=ax1, stat='density').set(xlabel='Delay (s)', xlim=(-120,600), title=f\"Sample Delays from Freeflow\")\n",
    "sns.histplot(segment_data['delay_systematic'], ax=ax2, stat='density').set(xlabel='Delay (s)', title=f\"Segment Aggregated Delays from Freeflow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fd7488a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sample_data['delay_total'].dropna()\n",
    "x = x[x < np.inf]\n",
    "x = x[x > -np.inf]\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,4))\n",
    "sns.histplot(x.values[x!=0], ax=ax1, stat='density').set(xlabel='Delay (s)', xlim=(-120,600), title=f\"Total Delay\")\n",
    "sns.histplot(segment_data['delay_total'], ax=ax2, stat='density').set(xlabel='Delay (s)', title=f\"Segment Total Delay\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17dbf658",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sample_data['pace_s_mi'].dropna()\n",
    "x = x[x < np.inf]\n",
    "x = x[x > -np.inf]\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,4))\n",
    "sns.histplot(x.values, binwidth=1, stat='density', ax=ax1).set(xlabel='Pace (s/mi)', xlim=(0,400), title=f\"Sample Pace\")\n",
    "sns.histplot(segment_data['pace_s_mi'], stat='density').set(xlabel='Pace (s/mi)', title=f\"Segment Aggregated Pace\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff961f9",
   "metadata": {},
   "source": [
    "### Top Best/Worst Performing Segments Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7bf0414c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1142.0326535389297\n",
      "59.31571301358412\n",
      "\n",
      "52.66666666666666\n",
      "-119.58823529411764\n",
      "\n",
      "333.9749239913878\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Use as filters for joined shapefiles in QGIS\n",
    "print(min(segment_data.sort_values(['pace_s_mi'], ascending=False).iloc[:10,:]['pace_s_mi']))\n",
    "print(max(segment_data.sort_values(['pace_s_mi'], ascending=True).iloc[:10,:]['pace_s_mi']))\n",
    "print()\n",
    "print(min(segment_data.sort_values(['delay_stochastic'], ascending=False).iloc[:10,:]['delay_stochastic']))\n",
    "print(max(segment_data.sort_values(['delay_stochastic'], ascending=True).iloc[:10,:]['delay_stochastic']))\n",
    "print()\n",
    "print(min(segment_data.sort_values(['delay_total'], ascending=False).iloc[:10,:]['delay_total']))\n",
    "print(max(segment_data.sort_values(['delay_total'], ascending=True).iloc[:10,:]['delay_total']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e4902c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57107274",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cc29692",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a915147d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd8e2db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Pace\n",
    "all_data['speed_mph'] = all_data['speed_m_s']*2.237\n",
    "all_data['pace_s_mi'] = 1 / (all_data['speed_m_s']*.0006214)\n",
    "\n",
    "# Add date to the data\n",
    "all_data['day'] = pd.to_datetime(all_data['locationtime'], unit='s')\n",
    "all_data['day'] = all_data['day'].dt.strftime('%Y-%m-%d')\n",
    "\n",
    "# Add corridor id to the data\n",
    "def get_dict_key(value, corridor_dict):\n",
    "    for corridor in corridor_dict:\n",
    "        if value in corridor_dict[corridor]:\n",
    "            return corridor\n",
    "    return -1\n",
    "all_data['corridor_id'] = all_data['seg_compkey'].apply(get_dict_key, args=(corridor_segments,))\n",
    "\n",
    "# REMOVE ALL DATA NOT IN CORRIDORS\n",
    "bus_interference_data = all_data[all_data['corridor_id']>=0]\n",
    "\n",
    "# Add travel time to the data\n",
    "# Use difference in the first/last time each unique trip is recorded on a corridor's segments for each day\n",
    "tt_data = bus_interference_data.groupby(['corridor_id','day','tripid']).agg(['max','min'])[['locationtime']]\n",
    "tt_data.reset_index(inplace=True)\n",
    "tt_data['tt'] = tt_data[('locationtime','max')] - tt_data[('locationtime','min')]\n",
    "tt_data.columns = tt_data.columns.droplevel(1)\n",
    "\n",
    "# Some interferences are matched to GTFS data that was assigned to segments outside of the corridor; these are left empty\n",
    "# This only affects traversal times. The tt cannot be calculated for tracked locations outside of a corridor\n",
    "bus_interference_data = pd.merge(bus_interference_data, tt_data[['corridor_id','day','tripid','tt']], on=['corridor_id','day','tripid'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "06bb7068",
   "metadata": {},
   "outputs": [
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       "       Unnamed: 0      tripid  vehicleid        lat         lon  orientation  \\\n",
       "53555       65292  44053224.0       7236  47.745049 -122.346085          180   \n",
       "53556       65309  44053225.0       7236  47.745049 -122.346054          180   \n",
       "53557       65327  44053227.0       7236  47.745045 -122.348518            0   \n",
       "53558       65328  44053227.0       7236  47.743958 -122.345528          275   \n",
       "53559       65345  44053228.0       7236  47.745049 -122.346306          272   \n",
       "...           ...         ...        ...        ...         ...          ...   \n",
       "71639      223598  49967173.0       6229  47.744873 -122.345551          270   \n",
       "71640      223768  49967199.0       6217  47.744617 -122.345337           91   \n",
       "71641      223794  49967243.0       6219  47.744495 -122.345543          273   \n",
       "71642      223795  49967243.0       6219  47.744747 -122.345551          272   \n",
       "71643      223962  49967348.0       6203  47.745125 -122.345558          270   \n",
       "\n",
       "       scheduledeviation  prev_deviation  totaltripdistance  tripdistance  \\\n",
       "53555                 56            26.0        8362.511920   7272.659037   \n",
       "53556                -62           -30.0        8362.511920   7265.360523   \n",
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       "53558                200           170.0        8105.325712   1419.635822   \n",
       "53559                158           176.0        8105.325712   1311.265021   \n",
       "...                  ...             ...                ...           ...   \n",
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       "71640                496           480.0       20327.704428  16549.009916   \n",
       "71641                168           186.0       20282.831177   3539.123291   \n",
       "71642                209           168.0       20282.831177   3553.571436   \n",
       "71643                 80            79.0       20282.831177   3506.504512   \n",
       "\n",
       "       ...  seg_compkey  seg_length  seg_route_id   seg_lat    seg_lon  \\\n",
       "53555  ...         2755          62        100195  47.74505 -122.34556   \n",
       "53556  ...         2755          62        100195  47.74505 -122.34556   \n",
       "53557  ...         2755          62        100195  47.74505 -122.34556   \n",
       "53558  ...         2755          62        100195  47.74450 -122.34556   \n",
       "53559  ...         2755          62        100195  47.74505 -122.34556   \n",
       "...    ...          ...         ...           ...       ...        ...   \n",
       "71639  ...         2756         202        102615  47.74505 -122.34556   \n",
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       "71641  ...         2755          62        102615  47.74450 -122.34556   \n",
       "71642  ...         2755          62        102615  47.74450 -122.34556   \n",
       "71643  ...         2756         202        102615  47.74505 -122.34556   \n",
       "\n",
       "       speed_mph   pace_s_mi         day  corridor_id  tt  \n",
       "53555      8.948  402.317348  2021-03-20            0   0  \n",
       "53556     20.133  178.807710  2021-03-20            0   0  \n",
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       "53558      6.711  536.423131  2021-03-20            0  85  \n",
       "53559     20.133  178.807710  2021-03-20            0   0  \n",
       "...          ...         ...         ...          ...  ..  \n",
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       "71640     11.185  321.853878  2021-03-20            0   0  \n",
       "71641     24.607  146.297217  2021-03-20            0  43  \n",
       "71642      0.000         inf  2021-03-20            0  43  \n",
       "71643     17.896  201.158674  2021-03-20            0   0  \n",
       "\n",
       "[127 rows x 34 columns]"
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     },
     "execution_count": 13,
     "metadata": {},
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   "source": [
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   "id": "f5592f06",
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     "execution_count": 18,
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   "id": "5a3fc5ab",
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     "execution_count": 17,
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  {
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   "execution_count": 78,
   "id": "0c396039",
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      "Index(['Unnamed: 0', 'tripid', 'vehicleid', 'lat', 'lon', 'orientation',\n",
      "       'scheduledeviation', 'prev_deviation', 'totaltripdistance',\n",
      "       'tripdistance', 'prev_tripdistance', 'closeststop', 'nextstop',\n",
      "       'prev_stopid', 'locationtime', 'prev_locationtime', 'dist_diff',\n",
      "       'time_diff', 'speed_m_s', 'deviation_change_s', 'at_stop', 'route_id',\n",
      "       'trip_short_name', 'route_short_name', 'seg_compkey', 'seg_length',\n",
      "       'seg_route_id', 'seg_lat', 'seg_lon', 'speed_mph', 'pace_s_mi', 'day',\n",
      "       'corridor_id'],\n",
      "      dtype='object')\n"
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       "      <td>inf</td>\n",
       "      <td>2021-03-12</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>34746102.0</td>\n",
       "      <td>8188</td>\n",
       "      <td>47.579819</td>\n",
       "      <td>-122.147911</td>\n",
       "      <td>358</td>\n",
       "      <td>42</td>\n",
       "      <td>80.0</td>\n",
       "      <td>21489.267668</td>\n",
       "      <td>517.963526</td>\n",
       "      <td>...</td>\n",
       "      <td>271</td>\n",
       "      <td>493119</td>\n",
       "      <td>2341</td>\n",
       "      <td>100162</td>\n",
       "      <td>47.6409</td>\n",
       "      <td>-122.25898</td>\n",
       "      <td>26.844</td>\n",
       "      <td>134.105783</td>\n",
       "      <td>2021-03-12</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>34746102.0</td>\n",
       "      <td>8188</td>\n",
       "      <td>47.579685</td>\n",
       "      <td>-122.144455</td>\n",
       "      <td>358</td>\n",
       "      <td>6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>21489.267668</td>\n",
       "      <td>771.837350</td>\n",
       "      <td>...</td>\n",
       "      <td>271</td>\n",
       "      <td>493119</td>\n",
       "      <td>2341</td>\n",
       "      <td>100162</td>\n",
       "      <td>47.6409</td>\n",
       "      <td>-122.25898</td>\n",
       "      <td>44.740</td>\n",
       "      <td>80.463470</td>\n",
       "      <td>2021-03-12</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>34746102.0</td>\n",
       "      <td>8188</td>\n",
       "      <td>47.581799</td>\n",
       "      <td>-122.142326</td>\n",
       "      <td>133</td>\n",
       "      <td>-1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21489.267668</td>\n",
       "      <td>1207.902715</td>\n",
       "      <td>...</td>\n",
       "      <td>271</td>\n",
       "      <td>493119</td>\n",
       "      <td>2341</td>\n",
       "      <td>100162</td>\n",
       "      <td>47.6409</td>\n",
       "      <td>-122.25898</td>\n",
       "      <td>13.422</td>\n",
       "      <td>268.211565</td>\n",
       "      <td>2021-03-12</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>34746102.0</td>\n",
       "      <td>8188</td>\n",
       "      <td>47.585056</td>\n",
       "      <td>-122.142754</td>\n",
       "      <td>90</td>\n",
       "      <td>-42</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>21489.267668</td>\n",
       "      <td>1572.377132</td>\n",
       "      <td>...</td>\n",
       "      <td>271</td>\n",
       "      <td>493119</td>\n",
       "      <td>2341</td>\n",
       "      <td>100162</td>\n",
       "      <td>47.6409</td>\n",
       "      <td>-122.25898</td>\n",
       "      <td>29.081</td>\n",
       "      <td>123.789953</td>\n",
       "      <td>2021-03-12</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      tripid  vehicleid        lat         lon  orientation  \\\n",
       "0           0  34746102.0       8188  47.580441 -122.152702          180   \n",
       "1           1  34746102.0       8188  47.579819 -122.147911          358   \n",
       "2           2  34746102.0       8188  47.579685 -122.144455          358   \n",
       "3           3  34746102.0       8188  47.581799 -122.142326          133   \n",
       "4           4  34746102.0       8188  47.585056 -122.142754           90   \n",
       "\n",
       "   scheduledeviation  prev_deviation  totaltripdistance  tripdistance  ...  \\\n",
       "0                 80            48.0       21489.267668     10.359279  ...   \n",
       "1                 42            80.0       21489.267668    517.963526  ...   \n",
       "2                  6            42.0       21489.267668    771.837350  ...   \n",
       "3                 -1             6.0       21489.267668   1207.902715  ...   \n",
       "4                -42            -1.0       21489.267668   1572.377132  ...   \n",
       "\n",
       "   route_short_name  seg_compkey  seg_length  seg_route_id  seg_lat  \\\n",
       "0               271       493119        2341        100162  47.6409   \n",
       "1               271       493119        2341        100162  47.6409   \n",
       "2               271       493119        2341        100162  47.6409   \n",
       "3               271       493119        2341        100162  47.6409   \n",
       "4               271       493119        2341        100162  47.6409   \n",
       "\n",
       "     seg_lon  speed_mph   pace_s_mi         day  corridor_id  \n",
       "0 -122.25898      0.000         inf  2021-03-12           -1  \n",
       "1 -122.25898     26.844  134.105783  2021-03-12           -1  \n",
       "2 -122.25898     44.740   80.463470  2021-03-12           -1  \n",
       "3 -122.25898     13.422  268.211565  2021-03-12           -1  \n",
       "4 -122.25898     29.081  123.789953  2021-03-12           -1  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(all_data.columns)\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a83d095b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num unique vehicles: 1314\n",
      "Num unique segments: 12308\n",
      "Num delays at stop: 4233552\n"
     ]
    }
   ],
   "source": [
    "print(f\"Num unique vehicles: {len(pd.unique(all_data['vehicleid']))}\")\n",
    "print(f\"Num unique segments: {len(pd.unique(all_data['seg_compkey']))}\")\n",
    "print(f\"Num delays at stop: {len(all_data[all_data['at_stop']==True])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "71c5595e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5026896266012459"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of data that is stop delays\n",
    "4233552/len(all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc185e8b",
   "metadata": {},
   "source": [
    "### Create Distributions of Metrics for Buses with/without Interference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d846791a",
   "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>Unnamed: 0</th>\n",
       "      <th>corridor_id</th>\n",
       "      <th>corridor_name</th>\n",
       "      <th>vehicle_id</th>\n",
       "      <th>int_location_tyoe</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>mode</th>\n",
       "      <th>int_start_time</th>\n",
       "      <th>int_stop_time</th>\n",
       "      <th>first_name</th>\n",
       "      <th>difference</th>\n",
       "      <th>report_date</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pike;\\r\\n3rd to 9th</td>\n",
       "      <td>7151</td>\n",
       "      <td>In a driving lane</td>\n",
       "      <td>47.610556</td>\n",
       "      <td>-122.335928</td>\n",
       "      <td>Construction</td>\n",
       "      <td>2021-03-05 07:19:24</td>\n",
       "      <td>2021-03-05 07:19:26</td>\n",
       "      <td>Travis</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-05</td>\n",
       "      <td>1614957564</td>\n",
       "      <td>1614957566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Pike;\\r\\n3rd to 9th</td>\n",
       "      <td>9647</td>\n",
       "      <td>In a driving lane</td>\n",
       "      <td>47.610657</td>\n",
       "      <td>-122.335785</td>\n",
       "      <td>Construction</td>\n",
       "      <td>2021-03-05 07:36:34</td>\n",
       "      <td>2021-03-05 07:36:36</td>\n",
       "      <td>Travis</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-05</td>\n",
       "      <td>1614958594</td>\n",
       "      <td>1614958596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Pike;\\r\\n3rd to 9th</td>\n",
       "      <td>4508</td>\n",
       "      <td>In a driving lane</td>\n",
       "      <td>47.610609</td>\n",
       "      <td>-122.336568</td>\n",
       "      <td>Construction</td>\n",
       "      <td>2021-03-05 07:38:58</td>\n",
       "      <td>2021-03-05 07:39:00</td>\n",
       "      <td>Travis</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-05</td>\n",
       "      <td>1614958738</td>\n",
       "      <td>1614958740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>University Way;\\r\\nCampus to 45th</td>\n",
       "      <td>6895</td>\n",
       "      <td>In a driving lane</td>\n",
       "      <td>47.658530</td>\n",
       "      <td>-122.313270</td>\n",
       "      <td>Service Vehicle</td>\n",
       "      <td>2021-03-08 07:48:09</td>\n",
       "      <td>2021-03-08 07:48:11</td>\n",
       "      <td>Andrew</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-08</td>\n",
       "      <td>1615218489</td>\n",
       "      <td>1615218491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>University Way;\\r\\nCampus to 45th</td>\n",
       "      <td>8048</td>\n",
       "      <td>In a driving lane</td>\n",
       "      <td>47.658636</td>\n",
       "      <td>-122.313270</td>\n",
       "      <td>Pedestrian</td>\n",
       "      <td>2021-03-08 08:04:34</td>\n",
       "      <td>2021-03-08 08:04:38</td>\n",
       "      <td>Andrew</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-03-08</td>\n",
       "      <td>1615219474</td>\n",
       "      <td>1615219478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  corridor_id                      corridor_name  vehicle_id  \\\n",
       "0           1            1                Pike;\\r\\n3rd to 9th        7151   \n",
       "1           2            1                Pike;\\r\\n3rd to 9th        9647   \n",
       "2           3            1                Pike;\\r\\n3rd to 9th        4508   \n",
       "3           4            5  University Way;\\r\\nCampus to 45th        6895   \n",
       "4           5            5  University Way;\\r\\nCampus to 45th        8048   \n",
       "\n",
       "   int_location_tyoe        lat         lon             mode  \\\n",
       "0  In a driving lane  47.610556 -122.335928     Construction   \n",
       "1  In a driving lane  47.610657 -122.335785     Construction   \n",
       "2  In a driving lane  47.610609 -122.336568     Construction   \n",
       "3  In a driving lane  47.658530 -122.313270  Service Vehicle   \n",
       "4  In a driving lane  47.658636 -122.313270       Pedestrian   \n",
       "\n",
       "        int_start_time        int_stop_time first_name  difference  \\\n",
       "0  2021-03-05 07:19:24  2021-03-05 07:19:26     Travis           2   \n",
       "1  2021-03-05 07:36:34  2021-03-05 07:36:36     Travis           2   \n",
       "2  2021-03-05 07:38:58  2021-03-05 07:39:00     Travis           2   \n",
       "3  2021-03-08 07:48:09  2021-03-08 07:48:11     Andrew           2   \n",
       "4  2021-03-08 08:04:34  2021-03-08 08:04:38     Andrew           4   \n",
       "\n",
       "  report_date  start_time   stop_time  \n",
       "0  2021-03-05  1614957564  1614957566  \n",
       "1  2021-03-05  1614958594  1614958596  \n",
       "2  2021-03-05  1614958738  1614958740  \n",
       "3  2021-03-08  1615218489  1615218491  \n",
       "4  2021-03-08  1615219474  1615219478  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interference_data = pd.read_csv(f\"{path}/../interference_instances_new.csv\")\n",
    "interference_data['start_time'] = pd.to_datetime(interference_data['int_start_time']).dt.tz_localize(tz.tzlocal()).astype(int).astype(str).str.slice(0,10).astype(int)\n",
    "interference_data['stop_time'] = pd.to_datetime(interference_data['int_stop_time']).dt.tz_localize(tz.tzlocal()).astype(int).astype(str).str.slice(0,10).astype(int)\n",
    "interference_data['corridor_id'] = interference_data['corridor_id'] - 1\n",
    "interference_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c01974d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interference 1 vehicle id 9647 No data found\n",
      "Interference 14 vehicle id 4370 No data found\n",
      "Interference 19 vehicle id 8142 Fell outside data\n",
      "Interference 21 vehicle id 7196 No data found\n",
      "Interference 22 vehicle id 9641 No data found\n",
      "Interference 23 vehicle id 303 No data found\n",
      "Interference 24 vehicle id 7425 No data found\n",
      "Interference 25 vehicle id 41514 No data found\n",
      "Interference 26 vehicle id 9672 No data found\n"
     ]
    }
   ],
   "source": [
    "# Track down the datapoints in GTFS-RT that correspond to each interference instance\n",
    "idx_interference = []  # The index of the interference in the interference data\n",
    "data_interference = []  # Data for +/- xmins from the interference in GTFS-RT\n",
    "datarow_interference = []  # What row of the +/- xmins GTFS-RT data the interference corresponds to\n",
    "\n",
    "for idx, row in interference_data.iterrows():\n",
    "\n",
    "    # Get the tracked datapoints corresponding to +/- xmins of the data; FROM THE START of the interference\n",
    "    found = False\n",
    "    margin_mins = 4\n",
    "    margin = margin_mins*60  # xmins in seconds\n",
    "    data = bus_interference_data[bus_interference_data['vehicleid'] == row.vehicle_id]\n",
    "    data = data[(data['locationtime']>=(row.start_time - margin)) & (data['locationtime']<=(row.start_time + margin))]\n",
    "    \n",
    "    # Get the metric across the time period where the interference occurred\n",
    "    if len(data) > 0:\n",
    "        data = data.sort_values(by='locationtime', ascending=True)\n",
    "        for j, time in enumerate(data['locationtime']):\n",
    "            if row.start_time <= time:\n",
    "                idx_interference.append(idx)\n",
    "                datarow_interference.append(j-1)\n",
    "                # Get time variable for each interference that is +/- seconds from 0; where 0 is the time of the interference\n",
    "                data['time_from_interference'] = data['locationtime'] - data['locationtime'].iloc[j-1]\n",
    "                # Get schedule deviation variable for each location with interference at 0\n",
    "                data['deviation_from_interference'] = data['scheduledeviation'] - data['scheduledeviation'].iloc[j-1]\n",
    "                data['interference_id'] = str(idx)\n",
    "                data_interference.append(data)\n",
    "                found = True\n",
    "                break\n",
    "        if not found:\n",
    "            print(f\"Interference {idx} vehicle id {row.vehicle_id} Fell outside data\")\n",
    "    else:\n",
    "        print(f\"Interference {idx} vehicle id {row.vehicle_id} No data found\")\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c020d8dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3,6,figsize=(12,9))\n",
    "fig.tight_layout(h_pad=4, w_pad=2)\n",
    "\n",
    "for i, idx in enumerate(idx_interference):\n",
    "    sns.ecdfplot(data=data_interference[i], x='speed_m_s', ax=axes.flat[i]).set(title=f\"Interference #{idx+1}\")\n",
    "    axes.flat[i].axvline(data_interference[i]['speed_m_s'].iloc[datarow_interference[i]]-.1, color='red')\n",
    "    axes.flat[i].axvline(np.median(data_interference[i]['speed_m_s']), color='blue')\n",
    "\n",
    "fig.legend(labels=['Speed Distribution','Speed of Interfered Route','Median'], loc='lower right')\n",
    "fig.savefig(fname=\"interference_speeds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "50c48bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3,6,figsize=(12,9))\n",
    "fig.tight_layout(h_pad=4, w_pad=2)\n",
    "\n",
    "for i, idx in enumerate(idx_interference):\n",
    "    sns.ecdfplot(data=data_interference[i], x='deviation_change_s', ax=axes.flat[i]).set(title=f\"Interference #{idx+1}\")\n",
    "    axes.flat[i].axvline(data_interference[i]['deviation_change_s'].iloc[datarow_interference[i]]-1, color='red')\n",
    "    axes.flat[i].axvline(np.median(data_interference[i]['deviation_change_s']), color='blue')\n",
    "\n",
    "fig.legend(labels=['Deviation Distribution','Deviation of Interfered Route','Median'], loc='lower right')\n",
    "fig.savefig(fname=\"interference_deviation_changes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e7085fdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HACK TO GET LEGEND COLORS RIGHT\n",
    "x = np.linspace(0, 10, 1000)\n",
    "y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5))\n",
    "z = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 1, 0.25))\n",
    "lines = np.array(plt.plot(x, '#3090dc', y, 'r', z, 'b'))\n",
    "select = [0,2,-1]\n",
    "plt.legend(lines[select], ['Travel Time Distribution','Travel Time of Interfered Route','Median']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "85a74ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,3,figsize=(10,3))\n",
    "fig.tight_layout(h_pad=4, w_pad=2)\n",
    "\n",
    "last_ax_used = -1\n",
    "for i in range(9):  # No Bellevue corridor because using the Seattle Streets dataset to map match coords\n",
    "    if i in [0,2,4,6,7,8]:\n",
    "        continue\n",
    "    else:\n",
    "        last_ax_used += 1\n",
    "    full_data = bus_interference_data[bus_interference_data['corridor_id']==i]\n",
    "    sns.ecdfplot(data=full_data, x='tt_x', ax=axes.flat[last_ax_used]).set(xlim=(0,60*5), title=f\"{corridor_names[i]}\")\n",
    "    for j in range(len(idx_interference)):\n",
    "        if data_interference[j]['corridor_id'].iloc[datarow_interference[j]]==i:\n",
    "            axes.flat[last_ax_used].axvline(data_interference[j]['tt_x'].iloc[datarow_interference[j]]+1, color='red', label='TT')\n",
    "    axes.flat[last_ax_used].axvline(np.median(full_data['tt_x']), color='blue', label='Median')\n",
    "\n",
    "fig.legend(lines[select], ['Travel Time Distribution','Travel Time of Interfered Route','Median'], loc='lower right')\n",
    "fig.savefig(fname=\"corridor_traversal_times\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6afc6325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create column for de-meaned values in the timeseries data\n",
    "for i in range(len(data_interference)):\n",
    "    norm = np.mean(data_interference[i]['scheduledeviation'])\n",
    "    data_interference[i]['scheduledeviation_norm'] = data_interference[i]['scheduledeviation'] - norm\n",
    "\n",
    "fig, axes = plt.subplots(1,1,figsize=(20,10))\n",
    "\n",
    "# Event study timeseries plot per interference\n",
    "sns.lineplot(data=pd.concat(data_interference),\n",
    "             x=\"time_from_interference\",\n",
    "             y=\"scheduledeviation_norm\",\n",
    "             hue=\"interference_id\",\n",
    "             palette='Set2',\n",
    "             linewidth=.8,\n",
    "             ax=axes).set(xlim=(-120,120), xlabel='Time from Interference (s)', ylabel='Schedule Deviation (s)', title=f\"De-Meaned Schedule Deviation +/-{2}min from Moment of Interference\")\n",
    "\n",
    "# LOWESS regression for data before interference\n",
    "sns.regplot(data=pd.concat(data_interference)[pd.concat(data_interference)['time_from_interference']<=0],\n",
    "             x=\"time_from_interference\",\n",
    "             y=\"scheduledeviation_norm\",\n",
    "             lowess=True,\n",
    "             marker='',\n",
    "             line_kws={'linewidth':6},\n",
    "             color='black').set(xlim=(-110,200), xlabel='Time from Interference (s)', ylabel='Schedule Deviation from Interference (s)')\n",
    "\n",
    "# LOWESS regression for data after interference\n",
    "sns.regplot(data=pd.concat(data_interference)[pd.concat(data_interference)['time_from_interference']>0],\n",
    "             x=\"time_from_interference\",\n",
    "             y=\"scheduledeviation_norm\",\n",
    "             lowess=True,\n",
    "             marker='',\n",
    "             line_kws={'linewidth':6},\n",
    "             color='black').set(xlim=(-120,120), xlabel='Time from Interference (s)', ylabel='Schedule Deviation from Interference (s)')\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "fig.savefig(fname=\"event_study_plot\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}