Datasets:

jwsouza13
/

ved

@@ -111,3 +111,58 @@ ved_2/VED_181017_week.csv filter=lfs diff=lfs merge=lfs -text
 ved_2/VED_181024_week.csv filter=lfs diff=lfs merge=lfs -text
 ved_2/VED_181031_week.csv filter=lfs diff=lfs merge=lfs -text
 ved_2/VED_181107_week.csv filter=lfs diff=lfs merge=lfs -text

 ved_2/VED_181024_week.csv filter=lfs diff=lfs merge=lfs -text
 ved_2/VED_181031_week.csv filter=lfs diff=lfs merge=lfs -text
 ved_2/VED_181107_week.csv filter=lfs diff=lfs merge=lfs -text
+code/visualize-osm-data.ipynb filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_171101_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_171108_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_171115_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_171122_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_171129_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_171206_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_171213_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_171220_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_171227_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180103_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180110_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180117_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180124_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180131_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180207_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180214_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180221_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180228_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180307_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180314_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180321_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180328_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180404_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180411_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180418_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180425_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180502_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180509_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180516_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180523_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180530_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180606_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180613_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180620_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180627_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180704_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180711_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180718_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180725_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180801_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180808_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180815_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180822_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180829_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180905_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180912_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180919_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_180926_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_181003_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_181010_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_181017_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_181024_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_181031_week.csv filter=lfs diff=lfs merge=lfs -text
+eVED/eVED_181107_week.csv filter=lfs diff=lfs merge=lfs -text

code/EnergyConsumptionCalculation.ipynb ADDED Viewed

	@@ -0,0 +1,451 @@

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "ZS2siGtqyq-f"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import os\n",
+    "import datetime\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Paths for input and output\n",
+    "path_in = \"./../data/\"\n",
+    "path_out = \"./../data/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "6SyijUrB4Y8t"
+   },
+   "outputs": [],
+   "source": [
+    "# Create dictionary for mapping of VehId to Vehicle_Type\n",
+    "df_IceHev = pd.read_excel (path_in+'/VED_Static_Data_ICE&HEV.xlsx')\n",
+    "df_IceHev.rename(columns={'Vehicle Type': 'Vehicle_Type'}, inplace=True)\n",
+    "df_PhevEv = pd.read_excel (path_in+'/VED_Static_Data_PHEV&EV.xlsx')\n",
+    "df_PhevEv.rename(columns={'EngineType': 'Vehicle_Type'}, inplace=True)\n",
+    "df_VehId = df_IceHev.append(df_PhevEv, ignore_index = True)\n",
+    "df_VehId.sort_values(by='VehId', inplace=True)\n",
+    "df_VehId = df_VehId.set_index('VehId')\n",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "dictVehId = df_VehId.to_dict('index')\n",
+    "#dictVehId[12]['Vehicle_Type']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7KE0rghhp9YV"
+   },
+   "source": [
+    "# Energy Consumption Calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "LgwTfDyYarGs",
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "def calculateEnergyConsumption(df):\n",
+    "    df_org = df.copy()\n",
+    "    column_name = 'Energy_Consumption'\n",
+    "    if column_name not in df.columns:\n",
+    "        #print(\"Create column\", column_name)\n",
+    "        df[column_name] = \"\"\n",
+    "\n",
+    "    arr_VehId = df[[\"VehId\" , \"Vehicle Speed[km/h]\", \"MAF[g/sec]\", \"Short Term Fuel Trim Bank 1[%]\", \"Long Term Fuel Trim Bank 1[%]\", \"HV Battery Current[A]\", \"HV Battery Voltage[V]\", column_name]].to_numpy()\n",
+    "    list_Energy_Consumption = []\n",
+    "    for row in arr_VehId:\n",
+    "        vehicle_type = dictVehId[row[0]]['Vehicle_Type']\n",
+    "        if vehicle_type == \"ICE\" or vehicle_type == \"HEV\":\n",
+    "            '''\n",
+    "            The formula to calculate the energy consumption is as following:\n",
+    "\n",
+    "            Energy consumption=DrivingLength/100*MassAirFlow*(1+ShortTermFuelTrimBank1/100+LongTermFuelTrimBank1/100)/AirFuelRation\n",
+    "\n",
+    "            where the Mass Air Flow is measured by gallon per second, Driving Length is measured by km, and the Air Fuel \n",
+    "            Ration is provided with the value of 14.08 according to a literature:\n",
+    "\n",
+    "            G. Oh, D. J. Leblanc and H. Peng, \"Vehicle Energy Dataset (VED), A Large-Scale Dataset for Vehicle Energy \n",
+    "            Consumption Research,\" in IEEE Transactions on Intelligent Transportation Systems, \n",
+    "            doi: 10.1109/TITS.2020.3035596.\n",
+    "            '''\n",
+    "            #print(\"ICE or HEV found\")\n",
+    "            # calculate energy consumption\n",
+    "                # /3600 as sampling rate is 1 sec ; value from \"Vehicle Speed[km/h]\"\n",
+    "                # The reason is as following:\n",
+    "                # row[1] is measured by km/h, which is treated to be the constant value during the sampling time of one second.\n",
+    "                # So, if we need to calculate the length during each sampling time, we need to multiple the speed (measured by km/h) by the duration (measured by h), which lead to:\n",
+    "                # row[1]*1/60/60=row[1]/3600\n",
+    "            DrivingLength = row[1] / 3600        # row[1] = Vehicle Speed[km/h]\n",
+    "            MassAirFlow =   row[2] * 3.78541     # recalculation from gallons to liters; value from \"MAF[g/sec]\"\n",
+    "            ShortTermFuelTrimBank1 = row[3]      # value from \"Short Term Fuel Trim Bank 1[%]\"\n",
+    "            LongTermFuelTrimBank1 = row[4]       # value from \"Long Term Fuel Trim Bank 1[%]\"\n",
+    "            AirFuelRation = 14.08\n",
+    "            #print(\"Length=\", DrivingLength, \"MassAirFlow=\", MassAirFlow, \"ShortTermFuelTrimBank1=\", ShortTermFuelTrimBank1, \"LongTermFuelTrimBank1=\", LongTermFuelTrimBank1)\n",
+    "            in_liter_per_hour=DrivingLength/100*MassAirFlow*(1+ShortTermFuelTrimBank1/100+LongTermFuelTrimBank1/100)/AirFuelRation\n",
+    "            # recalcualte energy consuption into kWh ; 1 kw*h = 0.1123 liter\n",
+    "            in_kilo_watts_perHour = in_liter_per_hour / 0.1123\n",
+    "            #if not math.isnan(in_kilo_watts_perHour):\n",
+    "            #  print(\"EC =\", in_kilo_watts_perHour, \"kWh (\", in_liter_per_hour, \")\" )\n",
+    "            list_Energy_Consumption.append(in_kilo_watts_perHour)\n",
+    "\n",
+    "        elif vehicle_type == \"PHEV\" or vehicle_type == \"EV\":\n",
+    "            '''\n",
+    "            The energy consumption for EV can be calculated by integrating the voltage and current of the EV battery over \n",
+    "            time, and this energy consumption can be measured by kw*h.\n",
+    "\n",
+    "            The convert between EV energy consumption (kw*h) and gas consumption (measured by liter) can be achieved as \n",
+    "            following:\n",
+    "\n",
+    "            1 kw*h = 0.1123 liter\n",
+    "\n",
+    "            The EV energy consumption calculation can be referred from: \n",
+    "            https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiY3Jzc3K70AhVpo4sKHTLUA9gQFnoECAYQAQ&url=https%3A%2F%2Fwww.mdpi.com%2F1996-1073%2F8%2F8%2F8573%2Fpdf&usg=AOvVaw3LyboWSueHI9UM0mJe5ypq\n",
+    "\n",
+    "            The convert from kw*h to liter can be referred from: \n",
+    "            https://en.wikipedia.org/wiki/Miles_per_gallon_gasoline_equivalent#Description\n",
+    "            '''\n",
+    "            #print(\"PHEV or EV found\")\n",
+    "            # calculate energy consumption\n",
+    "            in_watts = row[5] * row[6] * (-1) # = \"HV Battery Current[A]\" * \"HV Battery Voltage[V]\", *(-1) EV formula needs to interpreted in that way that the consumed energy is negative while the produced one is positive\n",
+    "            in_kilo_watts = in_watts / 1000\n",
+    "            # recalcualte energy consuption into kWh\n",
+    "            # measurement of A in 1 sec ; V in 5 sec => Therefore one line covers the smaller unit 1 second\n",
+    "            #print(\"HV Battery Current[A]\", row[5], \"HV Battery Voltage[V]\", row[6])\n",
+    "            in_kilo_watts_perHour = in_kilo_watts / 3600\n",
+    "            #print(\"EC =\", in_kilo_watts_perHour, \"kWh (\", in_kilo_watts, in_watts, \")\" )\n",
+    "            list_Energy_Consumption.append(in_kilo_watts_perHour)\n",
+    "\n",
+    "        else:\n",
+    "            print(\"Unknown Vehicle_Type\", row[0], \"found\")\n",
+    "\n",
+    "    #df['Energy_Consumption'] = list_Energy_Consumption\n",
+    "    df_org['Energy_Consumption'] = list_Energy_Consumption\n",
+    "    return df_org"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "j0syPBlrjmrd"
+   },
+   "source": [
+    "# Reworking of all original files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_files = []    \n",
+    "for file in os.listdir(path_in):\n",
+    "    if file.endswith(\"_week.csv\"):\n",
+    "        list_files.append(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Execution time =  VED_171101_week.csv : 9.888816118240356\n",
+      "Execution time =  VED_171108_week.csv : 9.541045904159546\n",
+      "Execution time =  VED_171115_week.csv : 10.846029043197632\n",
+      "Execution time =  VED_171122_week.csv : 8.577940940856934\n",
+      "Execution time =  VED_171129_week.csv : 9.552509069442749\n",
+      "Execution time =  VED_171206_week.csv : 10.801214933395386\n",
+      "Execution time =  VED_171213_week.csv : 12.398997068405151\n",
+      "Execution time =  VED_171220_week.csv : 9.741503953933716\n",
+      "Execution time =  VED_171227_week.csv : 5.659049987792969\n",
+      "Execution time =  VED_180103_week.csv : 7.128296136856079\n",
+      "Execution time =  VED_180110_week.csv : 10.233113050460815\n",
+      "Execution time =  VED_180117_week.csv : 9.690773963928223\n",
+      "Execution time =  VED_180124_week.csv : 8.43485403060913\n",
+      "Execution time =  VED_180131_week.csv : 8.949444055557251\n",
+      "Execution time =  VED_180207_week.csv : 7.7250587940216064\n",
+      "Execution time =  VED_180214_week.csv : 7.392001152038574\n",
+      "Execution time =  VED_180221_week.csv : 5.865344047546387\n",
+      "Execution time =  VED_180228_week.csv : 7.966830015182495\n",
+      "Execution time =  VED_180307_week.csv : 7.3129050731658936\n",
+      "Execution time =  VED_180314_week.csv : 7.454961061477661\n",
+      "Execution time =  VED_180321_week.csv : 7.334964990615845\n",
+      "Execution time =  VED_180328_week.csv : 6.493051052093506\n",
+      "Execution time =  VED_180404_week.csv : 8.315284013748169\n",
+      "Execution time =  VED_180411_week.csv : 7.657871961593628\n",
+      "Execution time =  VED_180418_week.csv : 8.63765001296997\n",
+      "Execution time =  VED_180425_week.csv : 7.897945880889893\n",
+      "Execution time =  VED_180502_week.csv : 7.535109996795654\n",
+      "Execution time =  VED_180509_week.csv : 7.370058059692383\n",
+      "Execution time =  VED_180516_week.csv : 8.234884977340698\n",
+      "Execution time =  VED_180523_week.csv : 7.743973970413208\n",
+      "Execution time =  VED_180530_week.csv : 7.505343914031982\n",
+      "Execution time =  VED_180606_week.csv : 7.749966144561768\n",
+      "Execution time =  VED_180613_week.csv : 6.840182065963745\n",
+      "Execution time =  VED_180620_week.csv : 9.27295184135437\n",
+      "Execution time =  VED_180627_week.csv : 10.812565088272095\n",
+      "Execution time =  VED_180704_week.csv : 6.055830001831055\n",
+      "Execution time =  VED_180711_week.csv : 7.213330984115601\n",
+      "Execution time =  VED_180718_week.csv : 7.271965980529785\n",
+      "Execution time =  VED_180725_week.csv : 5.97207498550415\n",
+      "Execution time =  VED_180801_week.csv : 6.417394161224365\n",
+      "Execution time =  VED_180808_week.csv : 6.360198974609375\n",
+      "Execution time =  VED_180815_week.csv : 5.3010618686676025\n",
+      "Execution time =  VED_180822_week.csv : 5.262967109680176\n",
+      "Execution time =  VED_180829_week.csv : 6.512995958328247\n",
+      "Execution time =  VED_180905_week.csv : 9.022238969802856\n",
+      "Execution time =  VED_180912_week.csv : 7.027529001235962\n",
+      "Execution time =  VED_180919_week.csv : 6.726388931274414\n",
+      "Execution time =  VED_180926_week.csv : 6.687731027603149\n",
+      "Execution time =  VED_181003_week.csv : 6.175661087036133\n",
+      "Execution time =  VED_181010_week.csv : 5.615024089813232\n",
+      "Execution time =  VED_181017_week.csv : 7.1177568435668945\n",
+      "Execution time =  VED_181024_week.csv : 7.9306700229644775\n",
+      "Execution time =  VED_181031_week.csv : 6.453009128570557\n",
+      "Execution time =  VED_181107_week.csv : 2.064950942993164\n",
+      "Execution time overall =  415.75856614112854\n"
+     ]
+    }
+   ],
+   "source": [
+    "time_overall_start = datetime.datetime.now().timestamp()\n",
+    "for file in list_files:\n",
+    "    time_start = datetime.datetime.now().timestamp()\n",
+    "    df = pd.read_csv (path_in + file)\n",
+    "    #print(df.info())\n",
+    "    df = calculateEnergyConsumption(df)\n",
+    "    #print(df.Energy_Consumption.describe())\n",
+    "    #print(df.info())\n",
+    "    df.to_csv(path_out + file, index=False, na_rep='nan')\n",
+    "    time_end = datetime.datetime.now().timestamp()\n",
+    "    print(\"Execution time = \", file, \":\", (time_end-time_start))\n",
+    "    \n",
+    "    #break\n",
+    "    \n",
+    "time_overall_end = datetime.datetime.now().timestamp()\n",
+    "print(\"Execution time overall = \", (time_overall_end-time_overall_start))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "M9tmmfISv6tM"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluation of generated Energy Consumption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def boxplot_EC_perVehicleType(df, surpress=False):\n",
+    "    arr_VehId = df[[\"VehId\" , \"Energy_Consumption\"]].to_numpy()\n",
+    "    list_EC_ICE = []\n",
+    "    list_EC_EV = []\n",
+    "    for row in arr_VehId:\n",
+    "        vehicle_type = dictVehId[row[0]]['Vehicle_Type']\n",
+    "        if vehicle_type == \"ICE\" or vehicle_type == \"HEV\":\n",
+    "            if not math.isnan(row[1]):\n",
+    "                #print(vehicle_type, row[1])\n",
+    "                list_EC_ICE.append(row[1])\n",
+    "        elif vehicle_type == \"PHEV\" or vehicle_type == \"EV\":\n",
+    "            if not math.isnan(row[1]):\n",
+    "                #print(vehicle_type, row[1])\n",
+    "                list_EC_EV.append(row[1])\n",
+    "        else:\n",
+    "            print(\"Unknown Vehicle_Type\", row[0], \"found\")\n",
+    "            \n",
+    "    # Creating plot\n",
+    "    if not surpress:\n",
+    "        fig = plt.figure(figsize =(10, 7))\n",
+    "        plt.subplot(1, 2, 1)\n",
+    "        plt.title('EC for ICE/HEV vehicles')\n",
+    "        plt.boxplot(list_EC_ICE)\n",
+    "\n",
+    "        plt.subplot(1, 2, 2)\n",
+    "        plt.title('EC for PHEV/EV vehicles')\n",
+    "        plt.boxplot(list_EC_EV)\n",
+    "\n",
+    "        # show plot\n",
+    "        plt.show()\n",
+    "    \n",
+    "    avg_ICE = (sum(list_EC_ICE) / len(list_EC_ICE))\n",
+    "    avg_EC  = (sum(list_EC_EV) / len(list_EC_EV))\n",
+    "    if not surpress:\n",
+    "        print(\"Average EC ICE:\", avg_ICE)\n",
+    "        print(\"Average EC EV:\", avg_EC)\n",
+    "    \n",
+    "    return [avg_ICE, avg_EC]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average EC ICE: 0.0032574152263156668\n",
+      "Average EC EV: 0.0011408219467592066\n"
+     ]
+    }
+   ],
+   "source": [
+    "list_values = []\n",
+    "df_EC171101 = pd.read_csv (path_out+'VED_171101_week.csv')\n",
+    "list_values.append(boxplot_EC_perVehicleType(df_EC171101))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for file in os.listdir(path_out):\n",
+    "    if file.endswith(\"_week.csv\"):\n",
+    "        df_EC171115 = pd.read_csv (path_out+file)\n",
+    "        list_values.append(boxplot_EC_perVehicleType(df_EC171115, True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "# line 1 points\n",
+    "x1 = list(range(1, len(list_values)+1))\n",
+    "y1 = [row[0] for row in list_values]\n",
+    "# plotting the line 1 points \n",
+    "plt.plot(x1, y1, label = \"ICE\")\n",
+    "# line 2 points\n",
+    "x2 = list(range(1, len(list_values)+1))\n",
+    "y2 = [row[1] for row in list_values]\n",
+    "# plotting the line 2 points \n",
+    "plt.plot(x2, y2, label = \"EV\")\n",
+    "plt.xlabel('Number of trip file')\n",
+    "# Set the y axis label of the current axis.\n",
+    "plt.ylabel('Energy Consumption')\n",
+    "# Set a title of the current axes.\n",
+    "plt.title('Average EC over all trip files')\n",
+    "# show a legend on the plot\n",
+    "plt.legend()\n",
+    "# Display a figure.\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "EnergyConsumption.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "eb4d98e6",
+   "metadata": {},
+   "source": [
+    "### In this notebook we will use Valhalla to snap the latitude/longitude points from VED to roads using OpenStreetMap data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d99459b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import json\n",
+    "from tqdm.notebook import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b3e0b1f",
+   "metadata": {},
+   "source": [
+    "Create the headers and the meili request body."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "20178448",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_meili_request_body(df_trip):\n",
+    "    \"\"\"Returns the body for meili for its request.\n",
+    "    \"\"\"\n",
+    "    coordinates = df_trip.to_json(orient='records')\n",
+    "    head = '{\"shape\":'\n",
+    "    tail = \"\"\",\"search_radius\": 5, \"shape_match\":\"map_snap\", \"costing\":\"auto\", \"format\":\"osrm\"}\"\"\"\n",
+    "    body = head + coordinates + tail\n",
+    "    return body"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "357a90c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "headers = {'Content-type': 'application/json'}\n",
+    "trace_url = \"http://localhost:8002/trace_attributes\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "154c0f88",
+   "metadata": {},
+   "source": [
+    "Read all the file names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f1097900",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da818fc0e91c42fcb09ca4b0fadd0d45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/54 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_path = \"../data/ved/dynamic/\"\n",
+    "files = [os.path.join(data_path, file) for file in tqdm(os.listdir(data_path)) if file.endswith(\".csv\")]\n",
+    "files.sort()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f9b0acf",
+   "metadata": {},
+   "source": [
+    "The functions that send the requests to the meili\n",
+    "Match types: {'matched': 0, 'interpolated': 1, 'unmatched': 2}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "91bffff4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_matched_response(matched_points, group_df):\n",
+    "    \"\"\" Returns a list of dictionaries for the matched coordinates.\n",
+    "        Dictionary keys: matched_lat, matched_lon, and match_type. \n",
+    "        If a points was not matched, returns the original coordinates. \"\"\"\n",
+    "    \n",
+    "    processed_matches = []\n",
+    "    for (i, matched_point) in enumerate(matched_points):\n",
+    "        match_type = {'matched': 0, 'interpolated': 1, 'unmatched': 2}\n",
+    "        \n",
+    "        matched_dict = {'matched_lat': group_df.iloc[i, 1] \n",
+    "                        if matched_point['type'] == 'unmatched' \n",
+    "                        else matched_point['lat'], \n",
+    "                        'matched_lon': group_df.iloc[i, 0] \n",
+    "                        if matched_point['type'] == 'unmatched' \n",
+    "                        else matched_point['lon'], \n",
+    "                        'match_type': match_type[matched_point['type']]}\n",
+    "\n",
+    "        processed_matches.append(matched_dict)\n",
+    "    \n",
+    "    return processed_matches\n",
+    "      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fb36bca3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def map_match(file):\n",
+    "    \"\"\" Takes a csv file and sends requests to Valhalla Meili service to snap the coordinates\n",
+    "    in the file to roads. \"\"\"\n",
+    "    signal_df = pd.read_csv(file)\n",
+    "    dataframes = []\n",
+    "    \n",
+    "    # Group the dataframe by the columns VehId and Trip\n",
+    "    for _, group_df in signal_df.groupby(['VehId', 'Trip']):\n",
+    "        group = group_df.rename(columns={'Longitude[deg]':'lon','Latitude[deg]':'lat'})[['lon', 'lat']]\n",
+    "        meili_request_body = create_meili_request_body(group)\n",
+    "        r = requests.post(trace_url, data=str(meili_request_body), headers=headers)\n",
+    "        \n",
+    "        if r.status_code == 200:\n",
+    "            res = r.json()\n",
+    "            matched_dicts = process_matched_response(res['matched_points'], group)\n",
+    "            matched_df = pd.DataFrame.from_dict(matched_dicts)\n",
+    "            dataframes.append(matched_df.set_index(group_df.index.copy()))\n",
+    "            \n",
+    "        else:\n",
+    "            print('Error', r.status_code)\n",
+    "\n",
+    "    conc_dfs = pd.concat(dataframes)\n",
+    "    signal_df = pd.merge(signal_df, conc_dfs, left_index=True, right_index=True)\n",
+    "    return signal_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d6be11d",
+   "metadata": {},
+   "source": [
+    "Process all files and save the results to another directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d1466e8d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18f1e3506e9d40eeb9f8966359fd6065",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/54 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def get_save_path(filename):\n",
+    "    return f\"../data/ved-valhalla/{filename[len(data_path)-1:]}\"\n",
+    "\n",
+    "for file in tqdm(files):\n",
+    "    matched_df = map_match(file)\n",
+    "    matched_df.to_csv(get_save_path(file), na_rep=np.nan, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7865645b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

code/spatial-join.ipynb ADDED Viewed

	@@ -0,0 +1,674 @@

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4e14ec4d",
+   "metadata": {},
+   "source": [
+    "### This notebook performs spatial joins of the roads and VED coordinates to assign each point a speed limit (if possible) ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8a1d5196",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/fahmed/anaconda3/lib/python3.8/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from shapely.geometry import Point, LineString\n",
+    "from shapely.wkt import loads\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm.notebook import tqdm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import geopandas as gpd\n",
+    "from shapely import wkt\n",
+    "import math\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efd0fe22",
+   "metadata": {},
+   "source": [
+    "Read the lines layer from OSM into a Pandas DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "545d2995",
+   "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>WKT</th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>highway</th>\n",
+       "      <th>maxspeed:forward</th>\n",
+       "      <th>maxspeed:backward</th>\n",
+       "      <th>source:maxspeed</th>\n",
+       "      <th>maxspeed:advisory</th>\n",
+       "      <th>maxspeed</th>\n",
+       "      <th>maxspeed:practical</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LINESTRING (-83.6889144 42.3061218,-83.6890468...</td>\n",
+       "      <td>4411729</td>\n",
+       "      <td>motorway_link</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25 mph</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LINESTRING (-83.7392397 42.2366692,-83.7392406...</td>\n",
+       "      <td>4439869</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35 mph</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LINESTRING (-83.6855813 42.3070306,-83.6852592...</td>\n",
+       "      <td>4569607</td>\n",
+       "      <td>motorway_link</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25 mph</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LINESTRING (-83.8373697 42.2896413,-83.8366383...</td>\n",
+       "      <td>4569609</td>\n",
+       "      <td>motorway_link</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70 mph</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>LINESTRING (-83.7733019 42.2692107,-83.7730861...</td>\n",
+       "      <td>4574780</td>\n",
+       "      <td>service</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 WKT   osm_id        highway  \\\n",
+       "0  LINESTRING (-83.6889144 42.3061218,-83.6890468...  4411729  motorway_link   \n",
+       "1  LINESTRING (-83.7392397 42.2366692,-83.7392406...  4439869      secondary   \n",
+       "2  LINESTRING (-83.6855813 42.3070306,-83.6852592...  4569607  motorway_link   \n",
+       "3  LINESTRING (-83.8373697 42.2896413,-83.8366383...  4569609  motorway_link   \n",
+       "4  LINESTRING (-83.7733019 42.2692107,-83.7730861...  4574780        service   \n",
+       "\n",
+       "  maxspeed:forward maxspeed:backward source:maxspeed maxspeed:advisory  \\\n",
+       "0                0                 0               0                 0   \n",
+       "1                0                 0               0                 0   \n",
+       "2                0                 0               0                 0   \n",
+       "3                0                 0               0                 0   \n",
+       "4                0                 0               0                 0   \n",
+       "\n",
+       "  maxspeed maxspeed:practical  \n",
+       "0        0             25 mph  \n",
+       "1   35 mph                  0  \n",
+       "2        0             25 mph  \n",
+       "3   70 mph                  0  \n",
+       "4        0                  0  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fields = ['WKT', 'osm_id',  'highway', 'maxspeed:forward', 'maxspeed:backward', 'source:maxspeed', \n",
+    "          'maxspeed:advisory', 'maxspeed', 'maxspeed:practical']\n",
+    "\n",
+    "\n",
+    "dtype = {'maxspeed:forward': 'str', 'maxspeed:backward': 'str', 'source:maxspeed': 'str',\n",
+    "         'maxspeed:advisory': 'str', 'maxspeed': 'str', 'maxspeed:practical': 'str'}\n",
+    "\n",
+    "roads_df = pd.read_csv('../data/osm/highways-lines-layer.csv', usecols=fields, dtype=dtype).fillna('0')\n",
+    "roads_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33674615",
+   "metadata": {},
+   "source": [
+    "Build a dictionary of roads, where keys are the OSM IDs and the values are the coordinates of the nodes in each road."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ff3a1d34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "roads_arr = roads_df.to_numpy()\n",
+    "roads_dict = {roads_arr[i,1]: loads(roads_arr[i,0]) for i in range(len(roads_arr))}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4725b1fc",
+   "metadata": {},
+   "source": [
+    "Convert the roads DataFrame to GeoDataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7ffb3615",
+   "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>osm_id</th>\n",
+       "      <th>highway</th>\n",
+       "      <th>maxspeed:forward</th>\n",
+       "      <th>maxspeed:backward</th>\n",
+       "      <th>source:maxspeed</th>\n",
+       "      <th>maxspeed:advisory</th>\n",
+       "      <th>maxspeed</th>\n",
+       "      <th>maxspeed:practical</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4411729</td>\n",
+       "      <td>motorway_link</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25 mph</td>\n",
+       "      <td>LINESTRING (-83.68891 42.30612, -83.68905 42.3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4439869</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35 mph</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LINESTRING (-83.73924 42.23667, -83.73924 42.2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4569607</td>\n",
+       "      <td>motorway_link</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25 mph</td>\n",
+       "      <td>LINESTRING (-83.68558 42.30703, -83.68526 42.3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4569609</td>\n",
+       "      <td>motorway_link</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70 mph</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LINESTRING (-83.83737 42.28964, -83.83664 42.2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4574780</td>\n",
+       "      <td>service</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LINESTRING (-83.77330 42.26921, -83.77309 42.2...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    osm_id        highway maxspeed:forward maxspeed:backward source:maxspeed  \\\n",
+       "0  4411729  motorway_link                0                 0               0   \n",
+       "1  4439869      secondary                0                 0               0   \n",
+       "2  4569607  motorway_link                0                 0               0   \n",
+       "3  4569609  motorway_link                0                 0               0   \n",
+       "4  4574780        service                0                 0               0   \n",
+       "\n",
+       "  maxspeed:advisory maxspeed maxspeed:practical  \\\n",
+       "0                 0        0             25 mph   \n",
+       "1                 0   35 mph                  0   \n",
+       "2                 0        0             25 mph   \n",
+       "3                 0   70 mph                  0   \n",
+       "4                 0        0                  0   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  LINESTRING (-83.68891 42.30612, -83.68905 42.3...  \n",
+       "1  LINESTRING (-83.73924 42.23667, -83.73924 42.2...  \n",
+       "2  LINESTRING (-83.68558 42.30703, -83.68526 42.3...  \n",
+       "3  LINESTRING (-83.83737 42.28964, -83.83664 42.2...  \n",
+       "4  LINESTRING (-83.77330 42.26921, -83.77309 42.2...  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roads_df['geometry'] = roads_df.WKT.apply(wkt.loads)\n",
+    "roads_df.drop('WKT', axis=1, inplace=True) #Drop WKT column\n",
+    "\n",
+    "roads_gdf = gpd.GeoDataFrame(roads_df, geometry='geometry')\n",
+    "roads_gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00f520db",
+   "metadata": {},
+   "source": [
+    "Function that calculates the bearing of the two points whose coordinates are given in the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d378fee8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_bearing(lat1, lat2, lon1, lon2):\n",
+    "    \"\"\" Calculate the initial bearing for start point (lat1, lon1) and end point (lat2, lon2) \"\"\"\n",
+    "    lat1, lat2 = math.radians(lat1), math.radians(lat2), \n",
+    "    lon1, lon2 = math.radians(lon1), math.radians(lon2)\n",
+    "    dLon = lon2 - lon1\n",
+    "    y = math.sin(dLon) * math.cos(lat2)\n",
+    "    x = math.cos(lat1) * math.sin(lat2) - math.sin(lat1) * math.cos(lat2) * math.cos(dLon)\n",
+    "    brng = math.atan2(y, x)\n",
+    "    return brng"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "27a18498",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mph2kph(speed):\n",
+    "    return round(speed * 1.609344)\n",
+    "\n",
+    "def speed2numeric(speed):\n",
+    "    return mph2kph(int(speed.split()[0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fa9cda87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def_maxspeed = {'residential': (1, speed2numeric('25 mph')), \n",
+    "                'motorway': (1, speed2numeric('70 mph')), \n",
+    "                'trunk': (1, speed2numeric('55 mph')),\n",
+    "                'primary': (1, speed2numeric('55 mph')),\n",
+    "                'secondary': (1, speed2numeric('45 mph')),\n",
+    "                'tertiary': (1, speed2numeric('35 mph')),\n",
+    "                'unclassified': (1, speed2numeric('55 mph')),\n",
+    "                'service': (1, speed2numeric('25 mph'))}\n",
+    "\n",
+    "\n",
+    "def get_speed(highway, maxspeed):\n",
+    "    \"\"\" Returns two values, the class of the speed limit and the speed limit.\"\"\"\n",
+    "    if maxspeed['maxspeed'] != '0':\n",
+    "        return 0, speed2numeric(maxspeed['maxspeed'])\n",
+    "    elif maxspeed['maxspeed:forward'] != '0':\n",
+    "        sp = '{}-{}'.format(speed2numeric(maxspeed['maxspeed:forward']), \n",
+    "                            speed2numeric(maxspeed['maxspeed:backward']))\n",
+    "        return -1, sp\n",
+    "    elif maxspeed['maxspeed:advisory'] != '0':\n",
+    "        return 2, speed2numeric(maxspeed['maxspeed:advisory'])\n",
+    "    elif maxspeed['maxspeed:practical'] != '0':\n",
+    "        return 3, str(speed2numeric(maxspeed['maxspeed:practical']))\n",
+    "    else:\n",
+    "        return def_maxspeed.get(highway, (np.nan, np.nan))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1ac5816",
+   "metadata": {},
+   "source": [
+    "The function below looks a bit messy and could probably be written in a clearer and better way, but it does the job. It first checks if the current point has two different speed limits depending on the direction (is of class -1). If so, it saves the coordinates of this point and goes forward until it finds a point in the same trip that has different coordinates. It then uses these two points to calculate the bearing and decides on the direction of travel. The second case is when the road the point lies has the same speed limit regardless of the direction. In such case, it checks if the point that follows along the same trip is of class -1. If this is the case, it uses these two points to decide on the direction and updates all duplicate points (if any) that follow. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8b5872c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1: VehId, 2: Trip, 4: Latitude, 5: Longitude, 22: matched_lat, 23: matched_lon, 24: match_type\n",
+    "\n",
+    "def set_direction_sp(records):\n",
+    "    np_arr = np.zeros(len(records)) # set the size to the correct one\n",
+    "    for i in range(len(records)):\n",
+    "        p_lat1, p_lon1 = records[i, 22], records[i, 23]\n",
+    "\n",
+    "        if records[i, 25] == -1 and np_arr[i] == 0:\n",
+    "            j = i\n",
+    "            osm_id = records[i, 27]\n",
+    "            road_lon1, road_lat1 = roads_dict[osm_id].coords[0]\n",
+    "            road_lon2, road_lat2 = roads_dict[osm_id].coords[-1]\n",
+    "\n",
+    "            while (records[j, 4] == records[i, 4]) and \\\n",
+    "                (records[j, 5] == records[i, 5]) and \\\n",
+    "                (records[j, 1] == records[i, 1]) and \\\n",
+    "                (records[j, 2] == records[i, 2]) and \\\n",
+    "                (j < len(records)-1):\n",
+    "                j += 1\n",
+    "\n",
+    "            if i != j:\n",
+    "                p_lat2, p_lon2 = records[j, 22], records[j, 23]\n",
+    "                # perform the computations and update all relevant rows\n",
+    "                br1 = get_bearing(p_lat1, p_lat2, p_lon1, p_lon2)\n",
+    "                br2 = get_bearing(road_lat1, road_lat2, road_lon1, road_lon2)\n",
+    "                direction = math.cos(br1-br2)\n",
+    "                speed = None\n",
+    "\n",
+    "                while i <= j:\n",
+    "                    if records[i, 25] == -1:\n",
+    "                        if direction > 0: # same direction\n",
+    "                            # extract the correct speed limit and set it\n",
+    "                            speed = records[i, 26].split('-')[0]\n",
+    "                        else:\n",
+    "                            speed = records[i, 26].split('-')[1]\n",
+    "                    else:\n",
+    "                        speed = records[i, 26]\n",
+    "                    np_arr[i] = speed\n",
+    "                    i += 1\n",
+    "            else:\n",
+    "                np_arr[i] = records[i, 26]\n",
+    "        else:\n",
+    "            if np_arr[i] == 0:\n",
+    "                np_arr[i] = records[i, 26]\n",
+    "                j = i + 1\n",
+    "                if j < len(records):\n",
+    "                    if records[j, 25] == -1:\n",
+    "                        i = j\n",
+    "                        p_lat2, p_lon2 = records[i, 22], records[i, 23]\n",
+    "                        osm_id = records[i, 27]\n",
+    "                        road_lon1, road_lat1 = roads_dict[osm_id].coords[0]\n",
+    "                        road_lon2, road_lat2 = roads_dict[osm_id].coords[-1]\n",
+    "                        \n",
+    "                        while (records[j, 4] == records[i, 4]) and \\\n",
+    "                        (records[j, 5] == records[i, 5]) and \\\n",
+    "                        (records[j, 1] == records[i, 1]) and \\\n",
+    "                        (records[j, 2] == records[i, 2]) and \\\n",
+    "                        (j < len(records)-1):\n",
+    "                            j += 1\n",
+    "        \n",
+    "                        # perform the computations and update all relevant rows\n",
+    "                        br1 = get_bearing(p_lat1, p_lat2, p_lon1, p_lon2)\n",
+    "                        br2 = get_bearing(road_lat1, road_lat2, road_lon1, road_lon2)\n",
+    "                        direction = math.cos(br1-br2)\n",
+    "                        speed = None\n",
+    "                        while i <= j:\n",
+    "                            if records[i, 25] == -1:\n",
+    "                                if direction > 0: # same direction\n",
+    "                                    # extract the correct speed limit and set it\n",
+    "                                    speed = records[i, 26].split('-')[0]\n",
+    "                                else:\n",
+    "                                    speed = records[i, 26].split('-')[1]\n",
+    "                            else:\n",
+    "                                speed = records[i, 26]\n",
+    "                            np_arr[i] = speed\n",
+    "                            i += 1\n",
+    "    return np_arr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "401d6fd8",
+   "metadata": {},
+   "source": [
+    "Read all files that contain the matched coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a4bd3a80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bfd0cb4118a4746b51862d20096674a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/54 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_path = \"../data/ved-valhalla/\"\n",
+    "files = [os.path.join(data_path, file) for file in tqdm(os.listdir(data_path)) if file.endswith(\".csv\")]\n",
+    "files.sort()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6bab521",
+   "metadata": {},
+   "source": [
+    "Converting each into a Pandas GeoDataFrame, spatially joining of the two GeoDataFrames and removing the duplicates which are as a result of intersections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "469930dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def spatial_join(file):\n",
+    "    data_df = pd.read_csv(file)\n",
+    "    data_gdf = gpd.GeoDataFrame(data_df, geometry=gpd.points_from_xy(data_df.matched_lon, data_df.matched_lat))\n",
+    "    sjoined_gdf = gpd.sjoin_nearest(data_gdf, roads_gdf, how=\"left\", max_distance=1e-05, distance_col='distance')\n",
+    "    sjoined_gdf_grouped = sjoined_gdf.groupby(sjoined_gdf.index).first() # remove duplicates\n",
+    "    gdf_cols = ['osm_id', 'highway', 'maxspeed:forward', 'maxspeed:backward', \n",
+    "                'maxspeed:advisory', 'maxspeed', 'maxspeed:practical']\n",
+    "    data_df = data_df.drop('geometry', 1)\n",
+    "    data_df = data_df.merge(sjoined_gdf_grouped[gdf_cols], left_index=True, right_index=True)\n",
+    "    return data_df\n",
+    "\n",
+    "def add_speed_limits(file):\n",
+    "    data_df = spatial_join(file)\n",
+    "    columns = [col for col in data_df.columns]\n",
+    "    maxspeed_cols = list(range(columns.index('highway'), len(columns)))\n",
+    "\n",
+    "    data_arr = data_df.to_numpy()\n",
+    "    N = len(data_arr)\n",
+    "    ved_out_df = np.c_[data_arr, np.zeros(N), np.empty(N, dtype='<U10')]\n",
+    "    for row in ved_out_df:\n",
+    "        # ms_cols dictionary: keys: maxspeed tag, values: speed limit\n",
+    "        ms_cols = {columns[j]: row[j] for j in range(27, len(columns))}\n",
+    "        if row[26] is None:\n",
+    "            sp_cls, speed_limit = np.nan, np.nan\n",
+    "        else:\n",
+    "            sp_cls, speed_limit = get_speed(row[26], ms_cols)\n",
+    "        row[32], row[33] = sp_cls, speed_limit\n",
+    "\n",
+    "    columns.extend(['Class of Speed Limit', 'Speed Limit[km/h]'])\n",
+    "    ved_out_df = pd.DataFrame(ved_out_df, columns=columns) \n",
+    "    ved_out_df = ved_out_df.drop([columns[i] for i in maxspeed_cols], axis=1)\n",
+    "    osm_col = ved_out_df['osm_id']\n",
+    "    ved_out_df = ved_out_df.drop(['osm_id'], 1)\n",
+    "    ved_out_df = ved_out_df.merge(osm_col, left_index=True, right_index=True)\n",
+    "    speed_column = set_direction_sp(ved_out_df.to_numpy())\n",
+    "    speed_column = pd.DataFrame(speed_column, columns={'Speed Limit with Direction'})\n",
+    "    ved_out_df = ved_out_df.merge(speed_column, left_index=True, right_index=True)\n",
+    "    return ved_out_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6d7aff86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26d17b74c9f14a68b0c4b8edb1d4e556",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/54 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def get_save_path(filename):\n",
+    "    return f\"../data/ved-speed-limits/{filename[len(data_path)-1:]}\"\n",
+    "\n",
+    "for file in tqdm(files):\n",
+    "    ved_file = add_speed_limits(file)\n",
+    "    ved_file = ved_file.drop('osm_id', 1)\n",
+    "    ved_file.to_csv(get_save_path(file), na_rep=np.nan, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63caeded",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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