Analyze_Jakarta_Routes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing everything\n",
    "And suppressing warnings :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "from geopandas import GeoSeries\n",
    "from shapely.geometry import Point, Polygon, LineString\n",
    "import folium\n",
    "from random import *\n",
    "from tqdm import tqdm\n",
    "import seaborn as sns\n",
    "from geopy import distance\n",
    "\n",
    "tqdm.pandas()\n",
    "\n",
    "import warnings\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "warnings.warn = warn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read all the GeoJSON files from Jakarta_POI.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_osrm = gpd.read_file('data/jkt_OSRM.geojson')\n",
    "df_gmaps = gpd.read_file('data/jkt_GMAPS.geojson')\n",
    "df_osmnx = gpd.read_file('data/jkt_OSMNX.geojson')\n",
    "df = pd.read_feather('data/matched_jkt.ftr')\n",
    "\n",
    "df = df[df.apply(lambda x: all(x.location != np.array([0, 0])), axis=1)]\n",
    "\n",
    "df_osrm.sort_values(by='trj_id', inplace=True)\n",
    "df_gmaps.sort_values(by='trj_id', inplace=True)\n",
    "df_osmnx.sort_values(by='trj_id', inplace=True)\n",
    "df.sort_values(by=['trj_id', 'pingtimestamp'], inplace=True)\n",
    "\n",
    "df_osrm.reset_index(drop=True, inplace=True)\n",
    "df_gmaps.reset_index(drop=True, inplace=True)\n",
    "df_osmnx.reset_index(drop=True, inplace=True)\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "    trj_id                                             routes  osrm_dur  \\\n",
       "0    10014  {'route': [[-6.19519, 106.7972], [-6.18229, 10...     363.6   \n",
       "1    10186  {'route': [[-6.17073, 106.78725], [-6.17372, 1...     603.6   \n",
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       "4    10286  {'route': [[-6.15126, 106.816], [-6.15128, 106...     370.9   \n",
       "..     ...                                                ...       ...   \n",
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       "893   9765  {'route': [[-6.14208, 106.80032], [-6.14191, 1...     148.9   \n",
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       "896   9990  {'route': [[-6.20376, 106.76937], [-6.20297, 1...     161.0   \n",
       "\n",
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       "4       4504.2  LINESTRING (106.81600 -6.15126, 106.81699 -6.1...  \n",
       "..         ...                                                ...  \n",
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       "893     2350.0  LINESTRING (106.80032 -6.14208, 106.80343 -6.1...  \n",
       "894     2482.0  LINESTRING (106.84037 -6.24203, 106.84100 -6.2...  \n",
       "895     4277.6  LINESTRING (106.83223 -6.22088, 106.83304 -6.2...  \n",
       "896     2458.2  LINESTRING (106.76937 -6.20376, 106.76934 -6.2...  \n",
       "\n",
       "[897 rows x 5 columns]"
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     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_osrm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "    trj_id  gdis  gdur  gdur_traffic  \\\n",
       "0    10014  7508   873          1217   \n",
       "1    10186  7849   979          1366   \n",
       "2    10255  4044   448           609   \n",
       "3    10282  9465  1252          1608   \n",
       "4    10286  4558   677           925   \n",
       "..     ...   ...   ...           ...   \n",
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       "\n",
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     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_gmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "    trj_id  osmnx_length                                           geometry\n",
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       "..     ...           ...                                                ...\n",
       "892   9636   8556.318186  LINESTRING (106.78755 -6.14307, 106.78756 -6.1...\n",
       "893   9765   1605.600651  LINESTRING (106.80044 -6.14215, 106.80062 -6.1...\n",
       "894   9844   4149.648125  LINESTRING (106.84040 -6.24204, 106.84046 -6.2...\n",
       "895    999   6669.499106  LINESTRING (106.83221 -6.22083, 106.83215 -6.2...\n",
       "896   9990   1313.927678  LINESTRING (106.76936 -6.20363, 106.76934 -6.2...\n",
       "\n",
       "[897 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_osmnx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>trj_id</th>\n",
       "      <th>driving_mode</th>\n",
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       "      <td>1554689984</td>\n",
       "      <td>-6.215685</td>\n",
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       "      <td>7.38</td>\n",
       "      <td>226</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-08 09:19:44+00:00</td>\n",
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       "      <td>7.83</td>\n",
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       "      <td>2019-04-08 09:19:45+00:00</td>\n",
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       "</table>\n",
       "<p>121514 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       trj_id driving_mode osname  pingtimestamp    rawlat      rawlng  speed  \\\n",
       "0       10186   motorcycle    ios     1555581210 -6.170755  106.787247   3.48   \n",
       "1       10186   motorcycle    ios     1555581211 -6.169763  106.787521   7.15   \n",
       "2       10186   motorcycle    ios     1555581212 -6.169143  106.787598   3.55   \n",
       "3       10186   motorcycle    ios     1555581213 -6.169040  106.787605   1.89   \n",
       "4       10186   motorcycle    ios     1555581214 -6.168501  106.787651   6.75   \n",
       "...       ...          ...    ...            ...       ...         ...    ...   \n",
       "121509    999          car    ios     1554689981 -6.215565  106.817566   7.16   \n",
       "121510    999          car    ios     1554689982 -6.215604  106.817520   6.91   \n",
       "121511    999          car    ios     1554689983 -6.215643  106.817474   7.00   \n",
       "121512    999          car    ios     1554689984 -6.215685  106.817421   7.38   \n",
       "121513    999          car    ios     1554689985 -6.215728  106.817368   7.83   \n",
       "\n",
       "        bearing  accuracy                      time  day  month  year  \\\n",
       "0             1       5.0 2019-04-18 16:53:30+00:00   18      4  2019   \n",
       "1             3       5.0 2019-04-18 16:53:31+00:00   18      4  2019   \n",
       "2           344       5.0 2019-04-18 16:53:32+00:00   18      4  2019   \n",
       "3           335       5.0 2019-04-18 16:53:33+00:00   18      4  2019   \n",
       "4           340       5.0 2019-04-18 16:53:34+00:00   18      4  2019   \n",
       "...         ...       ...                       ...  ...    ...   ...   \n",
       "121509      224      10.0 2019-04-08 09:19:41+00:00    8      4  2019   \n",
       "121510      225      10.0 2019-04-08 09:19:42+00:00    8      4  2019   \n",
       "121511      225      10.0 2019-04-08 09:19:43+00:00    8      4  2019   \n",
       "121512      226      10.0 2019-04-08 09:19:44+00:00    8      4  2019   \n",
       "121513      229      10.0 2019-04-08 09:19:45+00:00    8      4  2019   \n",
       "\n",
       "        day_of_week                 location  \n",
       "0                 3  [106.787248, -6.170731]  \n",
       "1                 3  [106.787423, -6.170158]  \n",
       "2                 3  [106.787683, -6.169139]  \n",
       "3                 3  [106.787678, -6.169037]  \n",
       "4                 3  [106.787662, -6.168501]  \n",
       "...             ...                      ...  \n",
       "121509            0  [106.817528, -6.215525]  \n",
       "121510            0  [106.817484, -6.215567]  \n",
       "121511            0  [106.817441, -6.215608]  \n",
       "121512            0  [106.817392, -6.215655]  \n",
       "121513            0  [106.817342, -6.215702]  \n",
       "\n",
       "[121514 rows x 15 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((121514, 15), (120257, 15))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape, df[df.speed != 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 55995/55995 [00:01<00:00, 46232.19it/s]\n"
     ]
    }
   ],
   "source": [
    "min_indices = []\n",
    "max_indices = []\n",
    "actual_routes = []\n",
    "grouped = df.groupby(\"trj_id\")\n",
    "for name, group in tqdm(grouped):\n",
    "    if not group.empty:\n",
    "        min_indices.append(group.iloc[0].name)\n",
    "        max_indices.append(group.iloc[-1].name)\n",
    "        actual_routes.append(list(zip(group.rawlng, group.rawlat)))\n",
    "\n",
    "df_start = df[df.index.isin(set(min_indices))].reset_index(drop=True)\n",
    "df_end = df[df.index.isin(set(max_indices))].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2019-04-09 21:54:04+00:00</td>\n",
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       "      <td>ios</td>\n",
       "      <td>1554979664</td>\n",
       "      <td>-6.151320</td>\n",
       "      <td>106.815994</td>\n",
       "      <td>6.780000</td>\n",
       "      <td>359</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019-04-11 17:47:44+00:00</td>\n",
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       "      <td>[106.815996, -6.15126]</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10620</td>\n",
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       "      <td>ios</td>\n",
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       "      <td>-6.173540</td>\n",
       "      <td>106.789513</td>\n",
       "      <td>7.680000</td>\n",
       "      <td>27</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-15 15:42:14+00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>[106.789338, -6.173456]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10995</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555169718</td>\n",
       "      <td>-6.143500</td>\n",
       "      <td>106.787987</td>\n",
       "      <td>4.550000</td>\n",
       "      <td>260</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019-04-13 22:35:18+00:00</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
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       "      <td>9173</td>\n",
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       "      <td>1555414149</td>\n",
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       "      <td>2.020000</td>\n",
       "      <td>153</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-16 18:29:09+00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>[106.817894, -6.210269]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>9245</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>ios</td>\n",
       "      <td>1554851548</td>\n",
       "      <td>-6.219659</td>\n",
       "      <td>106.813873</td>\n",
       "      <td>10.310000</td>\n",
       "      <td>170</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-10 06:12:28+00:00</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>[106.813898, -6.219666]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>9588</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1554789840</td>\n",
       "      <td>-6.168535</td>\n",
       "      <td>106.788383</td>\n",
       "      <td>5.990000</td>\n",
       "      <td>165</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-09 13:04:00+00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>[106.788467, -6.168548]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>9636</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555670638</td>\n",
       "      <td>-6.142925</td>\n",
       "      <td>106.787964</td>\n",
       "      <td>4.480000</td>\n",
       "      <td>183</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-19 17:43:58+00:00</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>[106.787964, -6.142925]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>999</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1554689223</td>\n",
       "      <td>-6.220886</td>\n",
       "      <td>106.832214</td>\n",
       "      <td>8.190000</td>\n",
       "      <td>149</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-08 09:07:03+00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>[106.832168, -6.220898]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    trj_id driving_mode osname  pingtimestamp    rawlat      rawlng  \\\n",
       "0    10186   motorcycle    ios     1555581210 -6.170755  106.787247   \n",
       "1    10255          car    ios     1554821644 -6.190298  106.762566   \n",
       "2    10286   motorcycle    ios     1554979664 -6.151320  106.815994   \n",
       "3    10620          car    ios     1555317734 -6.173540  106.789513   \n",
       "4    10995          car    ios     1555169718 -6.143500  106.787987   \n",
       "..     ...          ...    ...            ...       ...         ...   \n",
       "197   9173   motorcycle    ios     1555414149 -6.210269  106.817917   \n",
       "198   9245   motorcycle    ios     1554851548 -6.219659  106.813873   \n",
       "199   9588          car    ios     1554789840 -6.168535  106.788383   \n",
       "200   9636          car    ios     1555670638 -6.142925  106.787964   \n",
       "201    999          car    ios     1554689223 -6.220886  106.832214   \n",
       "\n",
       "         speed  bearing  accuracy                      time  day  month  year  \\\n",
       "0     3.480000        1       5.0 2019-04-18 16:53:30+00:00   18      4  2019   \n",
       "1     7.973843       87       4.0 2019-04-09 21:54:04+00:00    9      4  2019   \n",
       "2     6.780000      359       5.0 2019-04-11 17:47:44+00:00   11      4  2019   \n",
       "3     7.680000       27      10.0 2019-04-15 15:42:14+00:00   15      4  2019   \n",
       "4     4.550000      260       5.0 2019-04-13 22:35:18+00:00   13      4  2019   \n",
       "..         ...      ...       ...                       ...  ...    ...   ...   \n",
       "197   2.020000      153      10.0 2019-04-16 18:29:09+00:00   16      4  2019   \n",
       "198  10.310000      170      10.0 2019-04-10 06:12:28+00:00   10      4  2019   \n",
       "199   5.990000      165      10.0 2019-04-09 13:04:00+00:00    9      4  2019   \n",
       "200   4.480000      183      10.0 2019-04-19 17:43:58+00:00   19      4  2019   \n",
       "201   8.190000      149      10.0 2019-04-08 09:07:03+00:00    8      4  2019   \n",
       "\n",
       "     day_of_week                 location  \n",
       "0              3  [106.787248, -6.170731]  \n",
       "1              1  [106.762568, -6.190339]  \n",
       "2              3   [106.815996, -6.15126]  \n",
       "3              0  [106.789338, -6.173456]  \n",
       "4              5  [106.787983, -6.143472]  \n",
       "..           ...                      ...  \n",
       "197            1  [106.817894, -6.210269]  \n",
       "198            2  [106.813898, -6.219666]  \n",
       "199            1  [106.788467, -6.168548]  \n",
       "200            4  [106.787964, -6.142925]  \n",
       "201            0  [106.832168, -6.220898]  \n",
       "\n",
       "[202 rows x 15 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>trj_id</th>\n",
       "      <th>driving_mode</th>\n",
       "      <th>osname</th>\n",
       "      <th>pingtimestamp</th>\n",
       "      <th>rawlat</th>\n",
       "      <th>rawlng</th>\n",
       "      <th>speed</th>\n",
       "      <th>bearing</th>\n",
       "      <th>accuracy</th>\n",
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       "      <th>day</th>\n",
       "      <th>month</th>\n",
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       "      <th>day_of_week</th>\n",
       "      <th>location</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10186</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555582576</td>\n",
       "      <td>-6.203257</td>\n",
       "      <td>106.823425</td>\n",
       "      <td>10.490000</td>\n",
       "      <td>97</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-18 17:16:16+00:00</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>[106.823441, -6.203175]</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10255</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1554822109</td>\n",
       "      <td>-6.189587</td>\n",
       "      <td>106.795097</td>\n",
       "      <td>11.106246</td>\n",
       "      <td>88</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2019-04-09 22:01:49+00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>[106.795096, -6.189563]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10286</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>ios</td>\n",
       "      <td>1554980652</td>\n",
       "      <td>-6.125361</td>\n",
       "      <td>106.798851</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>189</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019-04-11 18:04:12+00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>[106.798851, -6.125346]</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10620</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555318226</td>\n",
       "      <td>-6.166225</td>\n",
       "      <td>106.799477</td>\n",
       "      <td>7.450000</td>\n",
       "      <td>350</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2019-04-15 15:50:26+00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>[106.799471, -6.166157]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10995</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555170303</td>\n",
       "      <td>-6.166469</td>\n",
       "      <td>106.786736</td>\n",
       "      <td>11.660000</td>\n",
       "      <td>141</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019-04-13 22:45:03+00:00</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>[106.786739, -6.166447]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>9173</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555415020</td>\n",
       "      <td>-6.166227</td>\n",
       "      <td>106.802322</td>\n",
       "      <td>3.420000</td>\n",
       "      <td>351</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019-04-16 18:43:40+00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>[106.802269, -6.166576]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>9245</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>ios</td>\n",
       "      <td>1554852374</td>\n",
       "      <td>-6.265384</td>\n",
       "      <td>106.783623</td>\n",
       "      <td>14.060000</td>\n",
       "      <td>151</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-10 06:26:14+00:00</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>[106.78369, -6.265383]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>9588</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1554790684</td>\n",
       "      <td>-6.242610</td>\n",
       "      <td>106.842880</td>\n",
       "      <td>2.620000</td>\n",
       "      <td>105</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-09 13:18:04+00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>[106.84288, -6.242602]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>9636</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555671587</td>\n",
       "      <td>-6.176974</td>\n",
       "      <td>106.793724</td>\n",
       "      <td>8.760000</td>\n",
       "      <td>308</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-19 17:59:47+00:00</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>[106.793697, -6.177003]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>999</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1554689985</td>\n",
       "      <td>-6.215728</td>\n",
       "      <td>106.817368</td>\n",
       "      <td>7.830000</td>\n",
       "      <td>229</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-04-08 09:19:45+00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>[106.817342, -6.215702]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    trj_id driving_mode osname  pingtimestamp    rawlat      rawlng  \\\n",
       "0    10186   motorcycle    ios     1555582576 -6.203257  106.823425   \n",
       "1    10255          car    ios     1554822109 -6.189587  106.795097   \n",
       "2    10286   motorcycle    ios     1554980652 -6.125361  106.798851   \n",
       "3    10620          car    ios     1555318226 -6.166225  106.799477   \n",
       "4    10995          car    ios     1555170303 -6.166469  106.786736   \n",
       "..     ...          ...    ...            ...       ...         ...   \n",
       "197   9173   motorcycle    ios     1555415020 -6.166227  106.802322   \n",
       "198   9245   motorcycle    ios     1554852374 -6.265384  106.783623   \n",
       "199   9588          car    ios     1554790684 -6.242610  106.842880   \n",
       "200   9636          car    ios     1555671587 -6.176974  106.793724   \n",
       "201    999          car    ios     1554689985 -6.215728  106.817368   \n",
       "\n",
       "         speed  bearing  accuracy                      time  day  month  year  \\\n",
       "0    10.490000       97      10.0 2019-04-18 17:16:16+00:00   18      4  2019   \n",
       "1    11.106246       88       8.0 2019-04-09 22:01:49+00:00    9      4  2019   \n",
       "2     5.700000      189       5.0 2019-04-11 18:04:12+00:00   11      4  2019   \n",
       "3     7.450000      350      17.0 2019-04-15 15:50:26+00:00   15      4  2019   \n",
       "4    11.660000      141       5.0 2019-04-13 22:45:03+00:00   13      4  2019   \n",
       "..         ...      ...       ...                       ...  ...    ...   ...   \n",
       "197   3.420000      351       5.0 2019-04-16 18:43:40+00:00   16      4  2019   \n",
       "198  14.060000      151      10.0 2019-04-10 06:26:14+00:00   10      4  2019   \n",
       "199   2.620000      105      10.0 2019-04-09 13:18:04+00:00    9      4  2019   \n",
       "200   8.760000      308      10.0 2019-04-19 17:59:47+00:00   19      4  2019   \n",
       "201   7.830000      229      10.0 2019-04-08 09:19:45+00:00    8      4  2019   \n",
       "\n",
       "     day_of_week                 location  \n",
       "0              3  [106.823441, -6.203175]  \n",
       "1              1  [106.795096, -6.189563]  \n",
       "2              3  [106.798851, -6.125346]  \n",
       "3              0  [106.799471, -6.166157]  \n",
       "4              5  [106.786739, -6.166447]  \n",
       "..           ...                      ...  \n",
       "197            1  [106.802269, -6.166576]  \n",
       "198            2   [106.78369, -6.265383]  \n",
       "199            1   [106.84288, -6.242602]  \n",
       "200            4  [106.793697, -6.177003]  \n",
       "201            0  [106.817342, -6.215702]  \n",
       "\n",
       "[202 rows x 15 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Folium Map\n",
    "We want to see how each routes API perform, including the actual trajectory and the map matching result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1aeeb18e3a0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding lines to the different feature groups\n",
    "for sample_trj_id in set(df.trj_id):\n",
    "    jkt = folium.Map(location=list(map(lambda x: x[::-1], df_osrm[df_osrm.trj_id == str(sample_trj_id)].geometry.iloc[0].coords))[0], zoom_start=17, tiles='openstreetmap')\n",
    "\n",
    "    # Creating feature groups\n",
    "    f1 = folium.FeatureGroup(\"OSRM\")\n",
    "    f2 = folium.FeatureGroup(\"GMAPS\")\n",
    "    f3 = folium.FeatureGroup(\"OSMNX\")\n",
    "    f4 = folium.FeatureGroup(\"ACTUAL\")\n",
    "    f5 = folium.FeatureGroup(\"MM\")\n",
    "\n",
    "    temp = df[df.trj_id == str(sample_trj_id)]\n",
    "    folium.vector_layers.PolyLine(list(map(lambda x: x[::-1], df_osrm[df_osrm.trj_id == str(sample_trj_id)].geometry.iloc[0].coords)), popup='<b>OSRM</b>', color='red', weight=3).add_to(f1)\n",
    "    folium.vector_layers.PolyLine(list(map(lambda x: x[::-1], df_gmaps[df_gmaps.trj_id == str(sample_trj_id)].geometry.iloc[0].coords)), popup='<b>GMAPS</b>', color='green', weight=3).add_to(f2)\n",
    "    folium.vector_layers.PolyLine(list(map(lambda x: x[::-1], df_osmnx[df_osmnx.trj_id == str(sample_trj_id)].geometry.iloc[0].coords)), popup='<b>OSMNX</b>', color='blue', weight=3).add_to(f3)\n",
    "    folium.vector_layers.PolyLine(list(zip(list(temp['rawlat']), list(temp['rawlng']))), popup='<b>ACTUAL</b>', color='black', weight=3).add_to(f4)\n",
    "    folium.vector_layers.PolyLine(list(map(lambda x: x[::-1], temp.location)), popup='<b>MM</b>', color='yellow', weight=3).add_to(f5)\n",
    "\n",
    "    f1.add_to(jkt)\n",
    "    f2.add_to(jkt)\n",
    "    f3.add_to(jkt)\n",
    "    f4.add_to(jkt)\n",
    "    f5.add_to(jkt)\n",
    "    folium.LayerControl().add_to(jkt)\n",
    "    jkt.save(f'data/routes/matched/{sample_trj_id}.html')\n",
    "jkt # sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_distance(df, trj_id):\n",
    "    temp = df[df.trj_id == trj_id]\n",
    "    temp['nextrawlat'] = temp.rawlat.shift()\n",
    "    temp['nextrawlng'] = temp.rawlng.shift()\n",
    "    x = temp.iloc[1:].apply(lambda row: get_dist(row), axis=1)\n",
    "    return sum(x)\n",
    "\n",
    "def get_dist(row):\n",
    "    return distance.distance((row.rawlat,row.rawlng), (row.nextrawlat, row.nextrawlng)).m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining all routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>trj_id</th>\n",
       "      <th>osmnxdis</th>\n",
       "      <th>osrmdis</th>\n",
       "      <th>gdis</th>\n",
       "      <th>actualdis</th>\n",
       "      <th>osrmdur</th>\n",
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       "      <th>osrmspeed</th>\n",
       "      <th>gspeed</th>\n",
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       "      <th>starth</th>\n",
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       "      <th>geometry_osmnx</th>\n",
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       "      <th>geometry_actual</th>\n",
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       "      <td>302.6</td>\n",
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       "      <td>465.0</td>\n",
       "      <td>55.928486</td>\n",
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       "      <td>370.9</td>\n",
       "      <td>677</td>\n",
       "      <td>925</td>\n",
       "      <td>988.0</td>\n",
       "      <td>43.718307</td>\n",
       "      <td>24.237518</td>\n",
       "      <td>16.734837</td>\n",
       "      <td>17</td>\n",
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       "      <td>5171.606425</td>\n",
       "      <td>1610.8</td>\n",
       "      <td>2132</td>\n",
       "      <td>2496.910897</td>\n",
       "      <td>233.2</td>\n",
       "      <td>415</td>\n",
       "      <td>554</td>\n",
       "      <td>492.0</td>\n",
       "      <td>24.866552</td>\n",
       "      <td>18.494458</td>\n",
       "      <td>18.270080</td>\n",
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       "      <td>LINESTRING (106.7895126342773 -6.1735401153564...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10995</td>\n",
       "      <td>4850.560287</td>\n",
       "      <td>5005.2</td>\n",
       "      <td>5420</td>\n",
       "      <td>5407.879717</td>\n",
       "      <td>263.9</td>\n",
       "      <td>641</td>\n",
       "      <td>790</td>\n",
       "      <td>585.0</td>\n",
       "      <td>68.278590</td>\n",
       "      <td>30.439938</td>\n",
       "      <td>33.279260</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>LINESTRING (106.81571 -6.15125, 106.81699 -6.1...</td>\n",
       "      <td>LINESTRING (106.81600 -6.15126, 106.81699 -6.1...</td>\n",
       "      <td>LINESTRING (106.78799 -6.14350, 106.78688 -6.1...</td>\n",
       "      <td>LINESTRING (106.7879867553711 -6.1435003280639...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trj_id      osmnxdis  osrmdis  gdis    actualdis  osrmdur  gdur  \\\n",
       "0   10186  16967.311474  10433.0  7849  7856.576317    603.6   979   \n",
       "1   10255   5392.254356   4701.1  4044  4032.848615    302.6   448   \n",
       "2   10286   7660.991620   4504.2  4558  4592.783040    370.9   677   \n",
       "3   10620   5171.606425   1610.8  2132  2496.910897    233.2   415   \n",
       "4   10995   4850.560287   5005.2  5420  5407.879717    263.9   641   \n",
       "\n",
       "   gdur_traffic  actualdur  osrmspeed     gspeed  actualspeed  starth  endh  \\\n",
       "0          1366     1366.0  62.224652  28.862513    20.705472      16    17   \n",
       "1           609      465.0  55.928486  32.496429    31.222054      21    22   \n",
       "2           925      988.0  43.718307  24.237518    16.734837      17    18   \n",
       "3           554      492.0  24.866552  18.494458    18.270080      15    15   \n",
       "4           790      585.0  68.278590  30.439938    33.279260      22    22   \n",
       "\n",
       "   day                                     geometry_osmnx  \\\n",
       "0   18  LINESTRING (106.79767 -6.19545, 106.79770 -6.1...   \n",
       "1    9  LINESTRING (106.78733 -6.17074, 106.78716 -6.1...   \n",
       "2   11  LINESTRING (106.76273 -6.19040, 106.76213 -6.1...   \n",
       "3   15  LINESTRING (106.79713 -6.25908, 106.79716 -6.2...   \n",
       "4   13  LINESTRING (106.81571 -6.15125, 106.81699 -6.1...   \n",
       "\n",
       "                                       geometry_osrm  \\\n",
       "0  LINESTRING (106.79720 -6.19519, 106.79655 -6.1...   \n",
       "1  LINESTRING (106.78725 -6.17073, 106.78683 -6.1...   \n",
       "2  LINESTRING (106.76257 -6.19034, 106.76667 -6.1...   \n",
       "3  LINESTRING (106.79689 -6.25918, 106.79666 -6.2...   \n",
       "4  LINESTRING (106.81600 -6.15126, 106.81699 -6.1...   \n",
       "\n",
       "                                      geometry_gmaps  \\\n",
       "0  LINESTRING (106.78732 -6.17077, 106.78751 -6.1...   \n",
       "1  LINESTRING (106.76257 -6.19032, 106.76537 -6.1...   \n",
       "2  LINESTRING (106.81600 -6.15129, 106.81701 -6.1...   \n",
       "3  LINESTRING (106.78765 -6.16782, 106.78764 -6.1...   \n",
       "4  LINESTRING (106.78799 -6.14350, 106.78688 -6.1...   \n",
       "\n",
       "                                     geometry_actual  \n",
       "0  LINESTRING (106.7872467041016 -6.1707553863525...  \n",
       "1  LINESTRING (106.762565612793 -6.19029760360717...  \n",
       "2  LINESTRING (106.8159942626953 -6.1513204574584...  \n",
       "3  LINESTRING (106.7895126342773 -6.1735401153564...  \n",
       "4  LINESTRING (106.7879867553711 -6.1435003280639...  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged = df_gmaps[df_gmaps.trj_id.isin(df.trj_id)].copy()\n",
    "df_merged.rename(columns={'geometry':'geometry_gmaps'}, inplace=True)\n",
    "df_merged['osrmdur'] = df_osrm['osrm_dur']\n",
    "df_merged['osrmdis'] = df_osrm['osrm_dist']\n",
    "df_merged['osmnxdis'] = df_osmnx['osmnx_length']\n",
    "df_merged = df_merged.sort_values(by='trj_id').reset_index(drop=True)\n",
    "df_merged['starth'] = df_start['time'].apply(lambda x: x.hour)\n",
    "df_merged['endh'] = df_end['time'].apply(lambda x: x.hour)\n",
    "df_merged['actualdur'] = (df_end['time'] - df_start['time']).apply(lambda x: x.total_seconds())\n",
    "df_merged['geometry_osrm'] = df_osrm['geometry']\n",
    "df_merged['geometry_osmnx'] = df_osmnx['geometry']\n",
    "df_merged['geometry_actual'] = list(map(LineString, actual_routes))\n",
    "\n",
    "df_merged['day'] = df_start['day']\n",
    "df_merged['osrmspeed'] = df_merged.apply(lambda x: 3.6*x['osrmdis']/x['osrmdur'], axis=1)\n",
    "df_merged['gspeed'] = df_merged.apply(lambda x: 3.6*x['gdis']/x['gdur'], axis=1)\n",
    "df_merged['actualdis'] = df_merged.apply(lambda x: get_distance(df, x.trj_id), axis=1)\n",
    "df_merged['actualspeed'] = df_merged.apply(lambda x: 3.6*x['actualdis']/x['actualdur'], axis=1)\n",
    "\n",
    "df_merged = df_merged[['trj_id', 'osmnxdis', 'osrmdis', 'gdis', 'actualdis', 'osrmdur', 'gdur', 'gdur_traffic', 'actualdur', 'osrmspeed', 'gspeed', 'actualspeed', 'starth', 'endh', 'day', 'geometry_osmnx', 'geometry_osrm', 'geometry_gmaps', 'geometry_actual']]\n",
    "df_merged['trj_id'] = df_merged['trj_id'].astype('int32')\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[-6.14524, 106.91498],\n",
       " [-6.1464, 106.91535],\n",
       " [-6.14811, 106.91568],\n",
       " [-6.14866, 106.91573],\n",
       " [-6.14868, 106.91474],\n",
       " [-6.14835, 106.9096],\n",
       " [-6.1483, 106.90941],\n",
       " [-6.1481, 106.90943],\n",
       " [-6.14815, 106.90962],\n",
       " [-6.14821, 106.90961]]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_osrm[df_osrm.trj_id == '8349'].routes.iloc[0]['route']\n",
    "# http://router.project-osrm.org/route/v1/driving/106.91498,-6.14524;106.90961,-6.14821"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is weather a significant factor?\n",
    "Doesn't seem like it, not many among the selected trajectories happened during the rain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\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>trj_id</th>\n",
       "      <th>osmnxdis</th>\n",
       "      <th>osrmdis</th>\n",
       "      <th>gdis</th>\n",
       "      <th>actualdis</th>\n",
       "      <th>osrmdur</th>\n",
       "      <th>gdur</th>\n",
       "      <th>gdur_traffic</th>\n",
       "      <th>actualdur</th>\n",
       "      <th>osrmspeed</th>\n",
       "      <th>gspeed</th>\n",
       "      <th>actualspeed</th>\n",
       "      <th>starth</th>\n",
       "      <th>endh</th>\n",
       "      <th>day</th>\n",
       "      <th>geometry_osmnx</th>\n",
       "      <th>geometry_osrm</th>\n",
       "      <th>geometry_gmaps</th>\n",
       "      <th>geometry_actual</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10186</td>\n",
       "      <td>16967.311474</td>\n",
       "      <td>10433.0</td>\n",
       "      <td>7849</td>\n",
       "      <td>7856.576317</td>\n",
       "      <td>603.6</td>\n",
       "      <td>979</td>\n",
       "      <td>1366</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>62.224652</td>\n",
       "      <td>28.862513</td>\n",
       "      <td>20.705472</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>LINESTRING (106.79767 -6.19545, 106.79770 -6.1...</td>\n",
       "      <td>LINESTRING (106.79720 -6.19519, 106.79655 -6.1...</td>\n",
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       "      <td>LINESTRING (106.7872467041016 -6.1707553863525...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10255</td>\n",
       "      <td>5392.254356</td>\n",
       "      <td>4701.1</td>\n",
       "      <td>4044</td>\n",
       "      <td>4032.848615</td>\n",
       "      <td>302.6</td>\n",
       "      <td>448</td>\n",
       "      <td>609</td>\n",
       "      <td>465.0</td>\n",
       "      <td>55.928486</td>\n",
       "      <td>32.496429</td>\n",
       "      <td>31.222054</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>LINESTRING (106.78733 -6.17074, 106.78716 -6.1...</td>\n",
       "      <td>LINESTRING (106.78725 -6.17073, 106.78683 -6.1...</td>\n",
       "      <td>LINESTRING (106.76257 -6.19032, 106.76537 -6.1...</td>\n",
       "      <td>LINESTRING (106.762565612793 -6.19029760360717...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10286</td>\n",
       "      <td>7660.991620</td>\n",
       "      <td>4504.2</td>\n",
       "      <td>4558</td>\n",
       "      <td>4592.783040</td>\n",
       "      <td>370.9</td>\n",
       "      <td>677</td>\n",
       "      <td>925</td>\n",
       "      <td>988.0</td>\n",
       "      <td>43.718307</td>\n",
       "      <td>24.237518</td>\n",
       "      <td>16.734837</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>LINESTRING (106.76273 -6.19040, 106.76213 -6.1...</td>\n",
       "      <td>LINESTRING (106.76257 -6.19034, 106.76667 -6.1...</td>\n",
       "      <td>LINESTRING (106.81600 -6.15129, 106.81701 -6.1...</td>\n",
       "      <td>LINESTRING (106.8159942626953 -6.1513204574584...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10620</td>\n",
       "      <td>5171.606425</td>\n",
       "      <td>1610.8</td>\n",
       "      <td>2132</td>\n",
       "      <td>2496.910897</td>\n",
       "      <td>233.2</td>\n",
       "      <td>415</td>\n",
       "      <td>554</td>\n",
       "      <td>492.0</td>\n",
       "      <td>24.866552</td>\n",
       "      <td>18.494458</td>\n",
       "      <td>18.270080</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>LINESTRING (106.79713 -6.25908, 106.79716 -6.2...</td>\n",
       "      <td>LINESTRING (106.79689 -6.25918, 106.79666 -6.2...</td>\n",
       "      <td>LINESTRING (106.78765 -6.16782, 106.78764 -6.1...</td>\n",
       "      <td>LINESTRING (106.7895126342773 -6.1735401153564...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10995</td>\n",
       "      <td>4850.560287</td>\n",
       "      <td>5005.2</td>\n",
       "      <td>5420</td>\n",
       "      <td>5407.879717</td>\n",
       "      <td>263.9</td>\n",
       "      <td>641</td>\n",
       "      <td>790</td>\n",
       "      <td>585.0</td>\n",
       "      <td>68.278590</td>\n",
       "      <td>30.439938</td>\n",
       "      <td>33.279260</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>LINESTRING (106.81571 -6.15125, 106.81699 -6.1...</td>\n",
       "      <td>LINESTRING (106.81600 -6.15126, 106.81699 -6.1...</td>\n",
       "      <td>LINESTRING (106.78799 -6.14350, 106.78688 -6.1...</td>\n",
       "      <td>LINESTRING (106.7879867553711 -6.1435003280639...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>9173</td>\n",
       "      <td>8257.200880</td>\n",
       "      <td>6900.9</td>\n",
       "      <td>6693</td>\n",
       "      <td>6565.544880</td>\n",
       "      <td>450.7</td>\n",
       "      <td>836</td>\n",
       "      <td>1116</td>\n",
       "      <td>871.0</td>\n",
       "      <td>55.121456</td>\n",
       "      <td>28.821531</td>\n",
       "      <td>27.136580</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>LINESTRING (106.84064 -6.16307, 106.84087 -6.1...</td>\n",
       "      <td>LINESTRING (106.84071 -6.16296, 106.84912 -6.1...</td>\n",
       "      <td>LINESTRING (106.81794 -6.21027, 106.81794 -6.2...</td>\n",
       "      <td>LINESTRING (106.8179168701172 -6.2102694511413...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>9245</td>\n",
       "      <td>12393.378887</td>\n",
       "      <td>8705.4</td>\n",
       "      <td>7910</td>\n",
       "      <td>7748.628068</td>\n",
       "      <td>494.0</td>\n",
       "      <td>1273</td>\n",
       "      <td>1844</td>\n",
       "      <td>826.0</td>\n",
       "      <td>63.440162</td>\n",
       "      <td>22.369207</td>\n",
       "      <td>33.771260</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>LINESTRING (106.89196 -6.11015, 106.89195 -6.1...</td>\n",
       "      <td>LINESTRING (106.89169 -6.11014, 106.89000 -6.1...</td>\n",
       "      <td>LINESTRING (106.81390 -6.21967, 106.81385 -6.2...</td>\n",
       "      <td>LINESTRING (106.8138732910156 -6.2196588516235...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>9588</td>\n",
       "      <td>12671.641743</td>\n",
       "      <td>10949.0</td>\n",
       "      <td>10945</td>\n",
       "      <td>10934.816902</td>\n",
       "      <td>461.0</td>\n",
       "      <td>704</td>\n",
       "      <td>2647</td>\n",
       "      <td>844.0</td>\n",
       "      <td>85.501952</td>\n",
       "      <td>55.968750</td>\n",
       "      <td>46.641399</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>LINESTRING (106.78835 -6.17170, 106.78824 -6.1...</td>\n",
       "      <td>LINESTRING (106.78849 -6.17197, 106.78772 -6.1...</td>\n",
       "      <td>LINESTRING (106.78834 -6.16853, 106.78835 -6.1...</td>\n",
       "      <td>LINESTRING (106.7883834838867 -6.1685347557067...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>9636</td>\n",
       "      <td>8556.318186</td>\n",
       "      <td>5685.5</td>\n",
       "      <td>5677</td>\n",
       "      <td>5870.274020</td>\n",
       "      <td>285.5</td>\n",
       "      <td>761</td>\n",
       "      <td>1197</td>\n",
       "      <td>949.0</td>\n",
       "      <td>71.691068</td>\n",
       "      <td>26.855716</td>\n",
       "      <td>22.268690</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>LINESTRING (106.78082 -6.24161, 106.78083 -6.2...</td>\n",
       "      <td>LINESTRING (106.78082 -6.24164, 106.78083 -6.2...</td>\n",
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       "      <td>LINESTRING (106.7879638671875 -6.1429247856140...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>999</td>\n",
       "      <td>6669.499106</td>\n",
       "      <td>4277.6</td>\n",
       "      <td>4915</td>\n",
       "      <td>4239.316655</td>\n",
       "      <td>271.5</td>\n",
       "      <td>597</td>\n",
       "      <td>973</td>\n",
       "      <td>762.0</td>\n",
       "      <td>56.719558</td>\n",
       "      <td>29.638191</td>\n",
       "      <td>20.028268</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
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       "      <td>LINESTRING (106.8322143554688 -6.2208857536315...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trj_id      osmnxdis  osrmdis   gdis     actualdis  osrmdur  gdur  \\\n",
       "0     10186  16967.311474  10433.0   7849   7856.576317    603.6   979   \n",
       "1     10255   5392.254356   4701.1   4044   4032.848615    302.6   448   \n",
       "2     10286   7660.991620   4504.2   4558   4592.783040    370.9   677   \n",
       "3     10620   5171.606425   1610.8   2132   2496.910897    233.2   415   \n",
       "4     10995   4850.560287   5005.2   5420   5407.879717    263.9   641   \n",
       "..      ...           ...      ...    ...           ...      ...   ...   \n",
       "197    9173   8257.200880   6900.9   6693   6565.544880    450.7   836   \n",
       "198    9245  12393.378887   8705.4   7910   7748.628068    494.0  1273   \n",
       "199    9588  12671.641743  10949.0  10945  10934.816902    461.0   704   \n",
       "200    9636   8556.318186   5685.5   5677   5870.274020    285.5   761   \n",
       "201     999   6669.499106   4277.6   4915   4239.316655    271.5   597   \n",
       "\n",
       "     gdur_traffic  actualdur  osrmspeed     gspeed  actualspeed  starth  endh  \\\n",
       "0            1366     1366.0  62.224652  28.862513    20.705472      16    17   \n",
       "1             609      465.0  55.928486  32.496429    31.222054      21    22   \n",
       "2             925      988.0  43.718307  24.237518    16.734837      17    18   \n",
       "3             554      492.0  24.866552  18.494458    18.270080      15    15   \n",
       "4             790      585.0  68.278590  30.439938    33.279260      22    22   \n",
       "..            ...        ...        ...        ...          ...     ...   ...   \n",
       "197          1116      871.0  55.121456  28.821531    27.136580      18    18   \n",
       "198          1844      826.0  63.440162  22.369207    33.771260       6     6   \n",
       "199          2647      844.0  85.501952  55.968750    46.641399      13    13   \n",
       "200          1197      949.0  71.691068  26.855716    22.268690      17    17   \n",
       "201           973      762.0  56.719558  29.638191    20.028268       9     9   \n",
       "\n",
       "     day                                     geometry_osmnx  \\\n",
       "0     18  LINESTRING (106.79767 -6.19545, 106.79770 -6.1...   \n",
       "1      9  LINESTRING (106.78733 -6.17074, 106.78716 -6.1...   \n",
       "2     11  LINESTRING (106.76273 -6.19040, 106.76213 -6.1...   \n",
       "3     15  LINESTRING (106.79713 -6.25908, 106.79716 -6.2...   \n",
       "4     13  LINESTRING (106.81571 -6.15125, 106.81699 -6.1...   \n",
       "..   ...                                                ...   \n",
       "197   16  LINESTRING (106.84064 -6.16307, 106.84087 -6.1...   \n",
       "198   10  LINESTRING (106.89196 -6.11015, 106.89195 -6.1...   \n",
       "199    9  LINESTRING (106.78835 -6.17170, 106.78824 -6.1...   \n",
       "200   19  LINESTRING (106.78082 -6.24161, 106.78083 -6.2...   \n",
       "201    8  LINESTRING (106.83897 -6.22526, 106.83850 -6.2...   \n",
       "\n",
       "                                         geometry_osrm  \\\n",
       "0    LINESTRING (106.79720 -6.19519, 106.79655 -6.1...   \n",
       "1    LINESTRING (106.78725 -6.17073, 106.78683 -6.1...   \n",
       "2    LINESTRING (106.76257 -6.19034, 106.76667 -6.1...   \n",
       "3    LINESTRING (106.79689 -6.25918, 106.79666 -6.2...   \n",
       "4    LINESTRING (106.81600 -6.15126, 106.81699 -6.1...   \n",
       "..                                                 ...   \n",
       "197  LINESTRING (106.84071 -6.16296, 106.84912 -6.1...   \n",
       "198  LINESTRING (106.89169 -6.11014, 106.89000 -6.1...   \n",
       "199  LINESTRING (106.78849 -6.17197, 106.78772 -6.1...   \n",
       "200  LINESTRING (106.78082 -6.24164, 106.78083 -6.2...   \n",
       "201  LINESTRING (106.83882 -6.22531, 106.83790 -6.2...   \n",
       "\n",
       "                                        geometry_gmaps  \\\n",
       "0    LINESTRING (106.78732 -6.17077, 106.78751 -6.1...   \n",
       "1    LINESTRING (106.76257 -6.19032, 106.76537 -6.1...   \n",
       "2    LINESTRING (106.81600 -6.15129, 106.81701 -6.1...   \n",
       "3    LINESTRING (106.78765 -6.16782, 106.78764 -6.1...   \n",
       "4    LINESTRING (106.78799 -6.14350, 106.78688 -6.1...   \n",
       "..                                                 ...   \n",
       "197  LINESTRING (106.81794 -6.21027, 106.81794 -6.2...   \n",
       "198  LINESTRING (106.81390 -6.21967, 106.81385 -6.2...   \n",
       "199  LINESTRING (106.78834 -6.16853, 106.78835 -6.1...   \n",
       "200  LINESTRING (106.78796 -6.14292, 106.78790 -6.1...   \n",
       "201  LINESTRING (106.83222 -6.22088, 106.83253 -6.2...   \n",
       "\n",
       "                                       geometry_actual  rain  \n",
       "0    LINESTRING (106.7872467041016 -6.1707553863525...     0  \n",
       "1    LINESTRING (106.762565612793 -6.19029760360717...     0  \n",
       "2    LINESTRING (106.8159942626953 -6.1513204574584...     0  \n",
       "3    LINESTRING (106.7895126342773 -6.1735401153564...     0  \n",
       "4    LINESTRING (106.7879867553711 -6.1435003280639...     0  \n",
       "..                                                 ...   ...  \n",
       "197  LINESTRING (106.8179168701172 -6.2102694511413...     0  \n",
       "198  LINESTRING (106.8138732910156 -6.2196588516235...     0  \n",
       "199  LINESTRING (106.7883834838867 -6.1685347557067...     0  \n",
       "200  LINESTRING (106.7879638671875 -6.1429247856140...     0  \n",
       "201  LINESTRING (106.8322143554688 -6.2208857536315...     0  \n",
       "\n",
       "[202 rows x 20 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather = pd.read_csv('data/weather_jkt.csv')\n",
    "df_merged['rain'] = df.apply(lambda x: int(any(weather[x.day == weather.day].rain)), axis=1)\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Peak vs Non-peak Hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_peak = df_merged[(13 <= df_merged.starth) & (df_merged.endh <= 18)]\n",
    "df_nonpeak = df_merged[~(13 <= df_merged.starth) | ~(df_merged.endh <= 18)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GMaps generally estimates a longer duration than OSRM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1aee2704520>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df_nonpeak, y='actualdur', x='actualdur', color='black', label='actual')\n",
    "sns.regplot(data=df_nonpeak, y='gdur', x='actualdur', color='green', label='gmaps', ci=None)\n",
    "fig = sns.regplot(data=df_nonpeak, y='osrmdur', x='actualdur', color='red', label='osrm', ci=None)\n",
    "fig.set_ylabel('api duration')\n",
    "fig.set_title('Duration gmaps vs osrm (in seconds)')\n",
    "fig.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is the gdur_traffic column useful?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1aeeb2e4280>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0mqOpVZ+FEMIshFgHHAJ+BHYAR6SUDmOVfUAr43MrYC+AsTwLaObeXsE27se6UwixRgix5vDhw7XQm6ZJdHQ0aWlp9S2GRqNp4NSqspBSOqWUvYHWqNFArc0Yk1LOlFL2lVL2bd68eW0dRqPRaBoua9fCG2/Uyq7rJBpKSnkE+BU4EwgRQhTnpGoN7Dc+7wfaABjLg4F09/YKtqk1EpISGDRrEDGvxTBo1iASkk68POGuXbvo0qULd9xxB926dWPw4MEUFBSwbt06+vfvT8+ePbnyyivJzMwEYMCAAYwfP57TTz+djh078ttvvwHw8ccfM2zYMAYMGEBsbCzPPvtsyTE++eQTTj/9dHr37s1dd92F0+kE4J577qFv375069aNp59++ijZCgoKiIuL47333jvhfmo0mjrmwAG45Rbo1w8efBA2barxQ9RmNFRzIUSI8dkXuAjYglIa1xirjQaKCzAsMr5jLP9Fqunli4AbjGipGCAW+Lu25AalKMYkjCElJ4UwnzBSclIYkzCmRhRGUlIS9913H5s3byYkJIQvv/ySUaNG8dJLL7FhwwZ69OhR5ubvcDj4+++/efXVV8u0//3333z55Zds2LCB+fPns2bNGrZs2cK8efP4448/WLduHWazmU8//RSA559/njVr1rBhwwaWL1/Ohg0bSvaVm5vL5Zdfzo033sgdd9xxwn3UaDR1REEBPP88dOwIH3+sall07araa5jazDobBcwyIpdMwBdSyu+EEInA50KIScC/QHGhhg+AOUKI7UAGKgIKKeVmIcQXQCLgAO6TUjprUW6mrJyC1WTF36ryL/lb/cGm2uNiTywFcExMDL179wbgtNNOY8eOHRw5coTzzz8fgNGjR3PttdeWrH/VVVeVrLvLrUTiRRddRLNmzUrW+f3337FYLKxdu5Z+/foBarQQEREBwBdffMHMmTNxOBykpKSQmJhIz549ARg2bBiPPPIII0aMOKG+aTSaOkJKmDcPxo+HPXtUW3g4TJoEt90Glpq/tdeaspBSbgBOraB9JxVEM0kpC4Fry7cby54Hnq9pGT2RfCSZMJ+ydSX8vPzYdWTXCe/b29u75LPZbObIkSPVWt9sNuNwOErayxc3EkIgpWT06NG8+OKLZZYlJyfzyiuvsHr1akJDQ7n55pvLpDE/++yzWbJkCcOHD9dFkzSahs7q1crUVFx22csLHngAnngCgoNr7bB6BncFxITEkG8vW882355PdEh0jR8rODiY0NDQEn/EnDlzSkYZlfHjjz+SkZFBQUEBX3/9NWeffTYXXHABCxYs4NChQ4BKd757926ys7Px9/cnODiY1NRUEhLKmtMmTpxIaGgo9913X433T6PR1BD798Po0XD66aWK4sorITFRVcirRUUBWllUSPxZ8dhcNvJseUgpybPlYXPZiD+r5urZujNr1izi4+Pp2bMn69at46mnnqpym9NPP52rr76anj17cvXVV9O3b1+6du3KpEmTGDx4MD179uSiiy4iJSWFXr16ceqpp9K5c2eGDx/O2WeffdT+XnvtNQoKCnjkkUdqo4sajeZ4yc+H555TfonZs1Vbr17wyy+wcCF06FAnYugU5R5ISEpgysop7Dqyi+iQaOLPij9hf0VN8fHHH7NmzRpmzJhRJ8fTKco1mnpASvj8c+WX2GtMNYuIUA7tW24Bc80nsqgsRflJWVa1OsTFxjUY5aBphCQkKNNAcjLExEB8fK3WR9Y0Mf76S/kl/vxTfbdaYexYmDABgoI8blZYWEhqairt2rWrcZG0GaoRcvPNN9fZqEJzHCQkwJgxkJICYWHqfcwY1a6pfxISYNAgpcQHDWpY12XvXhgxAvr3L1UUV18NW7bA5MkeFYWUkvnz59O1a1euvvpqasNipJWFRlPTTJmingT9/UEI9W61qnZN/dJQFXleHjz9NHTqBJ99ptpOPRWWL4cFC+CUUzxuunr1as477zyuu+46AgMDmTx5cq1ENWplodHUNMnJ4OdXts3PD9zmyWjqiYamyF0umDNHOa8nTlST6Vq0gA8+UCGy553ncdN9+/YxatQoTj/9dLZt28bMmTP5559/uPDCC2tFVO2z0GhqmpgY9cTq71/alp8P0dH1JpLGIDlZjSjcqS9FvnKl8kusXq2+e3vDQw/BY49BYKDHzfLy8nj55ZeZMmUKLpeLxx57jEcffZSgSnwZNYEeWWg0NU18PNhsyrQgpXq32VR7TdOQ7e8NkZgYpbjdqWtFvmcP3HgjnH12qaK47jr47z944QWPisLlcjFr1iw6duzIxIkTGTp0KP/99x8vvPBCrSsK0MqiUXLzzTezYMGCqlfU1A9xcTBjBkRFQWamep8xo+ajoRqq/b0hU5eKvDy5ufDkk8ov8fnnqu200+C331TqjkoU1m+//cbpp5/OzTffTOvWrfnjjz/4/PPPia5DJaeVhUZTG8TFqUlTO3eq99oIm21o9vfGQF0pcndcLpg1S/klJk2CwkJ13I8/hr//hnPO8bjpzp07ueaaazjvvPNITU3lk08+YdWqVZx11lm1J68HtLLwRC0M73ft2kXnzp0ZMWIEXbp04ZprriE/P5+1a9dy/vnnc9ppp3HxxReTkpICwHvvvUe/fv3o1asXV199Nfnlh8/Ak08+yc0331ySilxzEqEd6cdHXSjyYn7/XaXnuPlmNfLz8YHHH4dt21TqDlPFt+CsrCzi4+Pp0qULCQkJTJw4ka1btzJixAhMHrapbbSyqIhaHN5v3bqVe++9ly1bthAUFMSbb77J//73PxYsWMDatWu59dZbefzxxwGVTXb16tWsX7+eLl268MEHH5TZV3x8PIcPH+ajjz7CXAuzOTUNnIZgf9dUTHKy8kOce64qSARwww3KLzFpEgQEVLiZw+Hg7bffpkOHDkydOpXhw4eTlJTEk08+iV/5B4M6RiuLiqjF4X2bNm1KcjPddNNN/PDDD2zatImLLrqI3r17M2nSJPbt2wfApk2bOPfcc+nRoweffvopmzdvLtnPc889R1ZWFu+8807jyhSrHbI1R33a3zUVk5OjZll36QLz56u200+HP/6AuXOhkpnVP/zwA7179+bee++lW7durFmzho8++oiWLVvWkfCVo5VFRdTi8L78jT0wMJBu3bqxbt061q1bx8aNG1m6dClQOlN748aNPP3002XSivfr14+1a9eSkZFxwjLVGdohW7PUh/1dUzEuF3z0kfJLvPgiFBVBq1Yq8d+qVVCJj2HLli0MGTKESy65hMLCQhYuXMivv/5Knz596rADVaOVRUXU4vB+z549rFq1CoDPPvuM/v37c/jw4ZI2u91eMoLIyckhKioKu91eUvGumEsuuYRHH32USy+9lJycnBOWq07QDtmapy7t75qKWbEC+vaFW2+FgwfB1xeeeQa2boWRIz36JdLS0hgzZgw9evRg5cqVvPLKK2zevJkrr7yyQVoLtLKoiFoc3nfq1Ik333yTLl26kJmZWeKvGD9+PL169aJ3796sNHLVP/fcc5xxxhmcffbZdO7c+ah9XXvttdxxxx0MHTqUglooo1jjaIfsyUtTND/u3AnXXAPnnw///qvabrpJOa+ffrrspEw3bDYbU6dOpUOHDrzzzjvcfffdJCUlMW7cuDLF0RocUsom9zrttNNkeRITE49qq5TFi6UcOFDKmBj1vnjxsW1fAcnJybJbt24nvJ+65pjPnScGDpSyc2cpTzut9NW5s2rXNF0WL5bylFPUte7TR72fckqN/E/VC1lZUj7yiJRWq5TqcVLK/v2l/PPPSjdzuVxy4cKFsn379hKQcXFxcvPmzXUkdPUA1kgP91U9svCEHt7XPNohe3LSVMyPTie89x7ExsLLL6vfbps2KvHfypVwxhkeN/33338ZNGgQV111Fd7e3ixZsoTFixfTtWvXOuzAiaGVRR0SHR3Npk2b6luM+qMpO2SbopmlpmgK5sdff1Wzre+8Ew4dUvI/+6wKhb3xRqUEKyAlJYVbb72V0047jU2bNvHWW2+xfv16Lr744jruwIlzUiUSlFI2SMdRQ0bWdF78uLimoRzcKY7yslrLRnk1FUV4ojTmxIo7dsDDD8PXX5e2jRqlcji1auVxs4KCAqZOncrkyZOx2WyMGzeOxx9/nJCQkFoXubY4aUYWPj4+pKen10pRkKaKlJL09HR8fHzqW5SGTVMxs9QWVZkfG+KoLCsLHnlEzZcoVhRnnaXSc8ya5VFRuFwuPv30Uzp16sSTTz7JxRdfzJYtW5gyZUqjVhRQizW4hRBtgNlAJCCBmVLK14QQzwB3AIeNVSdIKRcb2zwG3AY4gfullD8Y7ZcArwFm4H0p5eTKjl1RDW673c6+ffvKzFXQVI2Pjw+tW7fGy8urvkVpuMTEqBGF+6hVSmVq27mz/uRqSBSXmd21S40oisvMuo/K/PzUiMNmq79RmcOhakk8+SQcNm5Rbdsq2a+91qO5CWDlypWMHTuWv//+mz59+jB9+nTOq6QeRUOkshrctaksooAoKeU/QohAYC1wBXAdkCulfKXc+l2BucDpQEvgJ6CjsXgbcBGwD1gN3CilTPR07IqUhaaJ0hBqXQ8adLSZJS9P+WR++aVuZWlsNKRz9/PPqs71xo3qu7+/mo09dqyaO+GB3bt3M378eObNm0fLli154YUXGDlyZL3lcDoRKlMWtdYbKWWKlPIf43MOsAXwbOSDYcDnUsoiKWUysB2lOE4Htkspd0opbcDnxrqak52GMiNcR3kdPw3B+b1tGwwdChdeqBSFEHDLLap9wgSPiiI7O5sJEybQqVMnFi1axFNPPcW2bdsYPXp0o1QUVVEnPRJCRAOnAn8ZTWOEEBuEEB8KIUKNtlbAXrfN9hltntrLH+NOIcQaIcSaw8XDR03TpqH4CppylFdtU5/JEDMzVWW6bt3g229V27nnqoJEH34IHnIyOZ1O3nvvPWJjY3nxxRe59tpr2bp1K88++yz+HibiNQVqXVkIIQKAL4EHpZTZwNtAe6A3kAJMrYnjSClnSin7Sin7Nm/evCZ2qWnoNISn0mL0vJzjoz5GZQ4HvPWWmi8xfbr6Hh2tEv8tX65CZD3w888/06dPH+68805iY2P566+/mDNnDm3atKk9eRsItaoshBBeKEXxqZRyIYCUMlVK6ZRSuoD3UGYmgP2A+xlvbbR5atec7OgU3Y2fuh6VLV0KvXvDffdBerpKFf7CC7Bli0rd4cGBvW3bNoYOHcqFF15IdnY28+bNK6led9LgaWr3ib4AgYqGerVce5Tb57EoPwVAN2A94A3EADtR0U8W43MMYDXW6VbZsStK96FpgjS1NBKa2uO//6S89NLS9BxCSHnbbVKmpFS6WXp6unzggQekxWKRgYGBcvLkybKgoKCOhK57qCTdR20qi3NQIbMbgHXGawgwB9hotC8qpzweB3YAW4E4t/YhqIioHcDjVR1bK4uTiFrI4aVpQqSnS/nAA1JaLKWKYsAAKf/5p9LNbDabfO2112RoaKg0mUzyzjvvlAcPHqwbmeuRypRFrYXO1ic6dFajOcmx2+Gdd1Sq8OKaL6ecAq+8Aldc4dHcJKXku+++4+GHH2bbtm1ceOGFTJs2jR49etSZ6PVJvYTOajQaTb2QkAA9e8L99ytFERioEv8lJsKVV3pUFBs2bOCiiy5i6NChCCH47rvvWLp06UmjKKpCKwuNRtM0SExUjvEhQ1SCP5MJ7rgDkpJUdJWHWhGpqanceeednHrqqfz777+8/vrrbNy4kUsvvVTnknPjpEokqNFomiDp6crc9PbbKo04qJnh06ZBr14eNyssLOTVV1/lhRdeoKCggPvvv58nn3ySsLCwupG7kaGVhUajaZzY7Wq+xDPPwJEjqq1DB+WXGDq0Ur/EF198wfjx49m9ezdDhw5lypQpdOzYscL1NQqtLDQaTeNCSli8GMaNU3WuAYKD4amnSpMSeuDvv/9m7NixrFy5kp49e/Lzzz8zaNCgOhK8caN9FhqN5phISEpg0KxBxLwWw6BZg0hIqsNcXJs3wyWXwGWXKUVhMsG99yq/xEMPeVQUe/fu5aabbuKMM85gx44dvP/++/zzzz9aURwDWlloNJpqk5CUwJiEMaTkpBDmE0ZKTgpjEsbUvsI4fFgphZ491SxsUIn/1q+HN98EDyl+cnNzeeqpp+jUqRMLFixgwoQJJCUlcdttt2E2m2tX5iaGVhYajabaTFk5BavJir/VHyEE/lZ/rCYrU1bWUvJGm005qmNjlQPb5YKOHVXiv6VLoXv3CjdzuVx8/PHHdOzYkeeee45hw4axdetWnn/+eQIDA2tH1iaO9lloNJpqk3wkmTCfstFCfl5+7Dqyq2YPJKVSCOPGwfbtqi0kRPkl7ruvUr/E8uXLGTt2LP/++y9nnHEGX375JWeeeWbNyncSokcWGo2m2sSExJBvL5u8Md+eT3RIdM0dZONGuOgiGDZMKQqzWTmut29XhYg8KIodO3Zw1VVXMWDAANLS0vjss89YtWqVVhQ1RJXKQghxlRAiSQiRJYTIFkLkCCGy60I4jUbTsIg/Kx6by0aeLQ8pJXm2PGwuG/Fn1UBK8UOH4K67VFbYn39WbZdcAhs2wBtvQLNmFW525MgRHn74Ybp06cLSpUuZNGkSW7du5cYbb9ST6mqQ6owsXgaGSimDpZRBUspAKWVQbQum0WgaHnGxccyIm0FUYBSZhZlEBUYxI24GcbEnkFK8qEgVrIqNhZkzlV+ic2cVHpuQAF27VriZw+HgrbfeIjY2lmnTpjFy5EiSkpJ4/PHH8a2kDKrm+KiOzyJVSrml1iXRaDSNgrjYuBNTDsVICV9/DQ8/rIpGgSqP++yzaoTh5eVx0yVLljBu3DgSExMZMGAA06ZN49RTTz1xmTQeqY6yWCOEmAd8DRQVN0qjmJFGo9EcM+vWKf/DsmXqu9msHNdPP60Uhgc2b97Mww8/zJIlS+jQoQNfffUVw4YN0+amOqA6yiIIyAcGu7VJQCsLjUZzbKSmwhNPwAcfqJEFqOR/U6dCly4eNzt8+DBPP/00M2fOJDAwkGnTpnHfffdhrSQqSlOzVKkspJS31IUgGo2mCVNYCK+9Bs8/Dzk5qq1rVzWH4uKLPW5WVFTEG2+8waRJk8jNzeWee+7h6aefJjw8vI4E1xRTnWio1kKIr4QQh4zXl0KI1nUhnEajQTl5Bw1SNccHDVLfGwtSwoIFSjE8+qhSFM2aqTrb69d7VBRSSr788ku6du1KfHw8Z599Nhs3buSNN97QiqKeqE401Eeo8qctjde3RptGo6ltEhLUHIOUFGXLT0lR3xuDwvjnHzj/fLj2WkhOBotF5W9KSlL+CUvFho21a9cyYMAArrnmGnx9ffnhhx/4/vvv6VKJmUpT+1RHWTSXUn4kpXQYr4+BihOxaDSammXKFDUJzd9fpdz291ffp9RSeo2aICUFbr0V+vaF335TbZdfrpIATp0KoaEVbnbgwAFuvvlm+vXrx5YtW3j77bdZt24dgwcPrnB9Td1SHQd3uhDiJmCu8f1GIL32RNJoNCUkJx8dHeTnB7t21Ys4lVJQANOnwwsvQF6eauveXfklLrrI42b5+fm88sorvPTSSzgcDuLj45kwYQLBwcF1JLimOlRHWdwKvAFMR0VBrQS001ujqQtiYtSTur9/aVt+PkRH15tIRyElzJ8PjzwCu3ertvBweO45uP12j+Yml8vFZ599xmOPPca+ffu45ppreOmllzjllFPqUHhNdanSDCWl3C2lHCqlbC6ljJBSXiGl3FPVdkKINkKIX4UQiUKIzUKIB4z2MCHEj0YKkR+FEKFGuxBCvC6E2C6E2CCE6OO2r9HG+klCiNEn0mGNplERH68yr+blqZtyXp76Hl8D6TVqgjVr4Lzz4PrrlaLw8lLJ/5KS4O67PSqKP/74g/79+zNy5EgiIyNZsWIF8+fP14qiISOlrPAFPGK8vwG8Xv7laTu37aOAPsbnQGAb0BWVPuRRo/1R4CXj8xAgARBAf+Avoz0M2Gm8hxqfQys79mmnnSY1mibD4sVSDhwoZUyMel+8uL4lknL/filHj5ZSqTD1uuIKKZOSKt1s586d8tprr5WAbNmypZw1a5Z0Op11I7OmSoA10sN9tTIzVHGKjzXHqYRSgBTjc44QYgvQChgGDDBWmwUsA8Yb7bMNgf8UQoQIIaKMdX+UUmYACCF+BC6h1Iei0TRt4uLUqyGQn6+c1JMnq8+gChJNn67Cej2QnZ3NCy+8wKuvvorZbOaZZ57h4Ycfxt/dvKZp0HhUFlLKb42P+VLK+e7LhBDXHstBhBDRwKnAX0CkoUgADgKRxudWwF63zfYZbZ7aNRpNXSElfP45jB8Pe41/x+bN1SS7W29V6ToqwOl08sEHH/Dkk09y6NAhRo0axQsvvECrVvpfuLFRndDZx6rZViFCiADgS+BBKWWZ1ObGKEJWd19VHOdOIcQaIcSaw4cP18QuNZoGQ73Wvf7rLzj7bBg+XCkKq1U5s5OS4I47PCqKn376iVNPPZW77rqLjh07snr1ambNmqUVRSPFo7IQQsQJId4AWhmO5+LXx4CjOjsXQnihFMWnsjTxYKphXsJ4P2S07wfauG3e2mjz1F4GKeVMKWVfKWXf5h7q8Wo0jZF6q3u9bx+MHAn9+8OqVartqqsgMRFeegk8hLZu3bqVyy+/nIsuuojc3Fzmz5/PihUr6Nu3b+3Kq6lVKhtZHED5KwqBtW6vRYDnZC4GQqWB/ADYIqWc5rZoEVAc0TQa+MatfZQRFdUfyDLMVT8Ag4UQoUbk1GCjTaM5Kajzutf5+SpNeMeO8Mknqq13b/j1V/jyS2jfvsLN0tPTuf/+++nevTvLly/npZdeIjExkWuuuUZnhW0CVOazWA+sF0J8JqW0H8e+zwZGAhuFEOuMtgnAZOALIcRtwG7gOmPZYlRE1HZUlttbDDkyhBDPAauN9SYWO7s1mpOBOqt77XLB3Lkqh9O+faotMlL5JW6+2aO5yWaz8dZbbzFx4kSysrK48847efbZZ4mIiKhZ+TT1SnUm5UULIV5Ehb36FDdKKSsNiJZS/o4Kg62ICypYXwL3edjXh8CH1ZBVo2lyxITEkJKTgr+1NHKoxuter1ql6kv89Zf67u2t8jg99hgEBla4iZSSb7/9locffpikpCQGDx7M1KlT6d69e83JpWkwVDeR4NsoP8VAYDbwSW0KpdFoSqnVutd79ijH9VlnlSqKa6+FLVtU2g4PimL9+vVceOGFDBs2DLPZzPfff8+SJUu0omjCVEdZ+EopfwaEVLO5nwEurV2xNBpNMbVS9zo3F556Cjp1UqYngD59YPly+OILlWakAg4ePMjtt9/Oqaeeyrp163jjjTfYsGEDQ4YM0X6JJk51zFBFQggTkCSEGIOKRAqoXbE0Go07NVb32uVSTuvHHoMDB1RbVJQaRYwaBaaKnx8LCgqYPn06L774IkVFRTz44IM8+eSThHrIIKtpelRHWTwA+AH3A8+hTFE6P5NG09j44w948EGVzwnAx0flcXr0UQio+PlPSsm8efMYP348e/bsYdiwYUyZMoXY2Ni6k1vTIKhUWQghzMD1UsqHgVx0tlmNpvGxe7eaeT1vXmnb9deruRLt2nnc7K+//mLs2LGsWrWK3r178/HHHzNw4MA6EFjTEKnUZyGldALn1JEsGo2mJsnJgccfV36JYkXRr58aYXz+uUdFsWfPHkaMGEH//v1JTk7mgw8+YM2aNVpRnORUxwz1rxBiETAfyCtudJuRrdFoGhIuF8yaBRMmwMGDqq1lS3jxRbjpJo9+idzcXCZPnszUqVMBePzxxxk/fjyBHiKiNCcX1VEWPqjKeO4pJSWglYWm8ZCQoEqRJierSJ/4+IaTybUmWbFC+SX+/Vd99/FReZweeaRsASU3nE4ns2bN4vHHH+fgwYMMHz6cF198kbZt29ad3JoGT5XKQkqp/RSaxk1CAowZoxLghYWpynNjxsCMGU1HYSQnK4WwYEFp2/DhajRRyU1/2bJljB07lnXr1tG/f3+++uor+vfvXwcCaxobVSoLIcRHVJAZVkp5a61IpNHUNFOmKEVR/GRd/D5lSuNXFtnZSiFMm6Yq6AGccQa8+qpKAOiB7du3Ex8fz9dff03btm2ZO3cu119/vZ4rofFIdSblfQd8b7x+BoJQkVEaTeMgORn8/Mq2+fnBrl31Ik6N4HTCBx+oZH+TJytF0bo1fPoprFzpUVEcOXKEcePG0bVrV3766Seef/55/vvvP2644YYyiqJeU6LXIlmFWbz/z/sMmjWItPy0+hanUVEdM9SX7t+FEHOB32tNIo2mpomJUaYnd5t9fj5ER9ebSCfEsmUqj9O6deq7n58yQcXHH60UDex2O++++y7PPPMMGRkZ3HrrrUyaNIkWLVoctW5xSnSryVomJfoMTnDWeD1hd9pZumMpszfM5pv/vqHIWQTAvE3zuO/0CtPRaSqgOiOL8sQCOp2kpvEQH6+evPPyVMW3vDz1Pb4GcivVJTt2qHoSAweWKopRo2DbNnj6aZWqY9AgpRwHDYKEBKSULF68mJ49e/K///2PXr168e+///L+++9XqCigHlKi1wJSSv5J+YcHlzxI6+mtuWzuZXyx+QuKnEX4WnwZ0WMEfaL61LeYjYrq+CxyUD4LYbwfRNXM1mgaB3Fxypk9ZYoyPUVHN65oqKwslSb8tddK/RJnnqn8Eqefrr5X4MTfdOedjIuIYOk//xAbG8s333zD5ZdfXqVfos5SotcC+7L38emGT5m9YTaJhxNL2gWCgTEDGdlzJFd3uZpAbx0OfKwIlRm8adG3b1+5pjilgUZTXRpaeG2xX+KJJ6C4VHCbNvDyy2oGtvtNf9CgElPbIbudpw8cYGZaGkEWC09PmcK9996L1Wqt1mEHzRp0VEr0PFseVrOVZn7NSD6STExIDPFnxTcIs1SuLZeFWxYye/1sfkn+BekWj9MlvAsje47kpp430Sa4TSV70QAIIdZKKSssaehRWQghKh2jSSn/qQHZagWtLDTHjPuTuZ+f8mnYbPUXXvvLL8ovsWGD+u7vr5L/PfQQ+PoevX5MDEUhIbx2+DDPp6SQ53Jxb/PmPO3rS7Pdu4/p0O4+Cz8vP/Lt+WQWZiIQhPiElLTZXLYTz357nDhdTn5J/oXZG2azcMtC8u35JcvC/cK5sfuNjOo1itOiTtMRXsfA8SqLX42PPkBfYD3KFNUTWCOlPLMWZK0RtLLQHDNuT+Yl5OWpjKy//FJ3ciQlqRHNN9+Utt18szJDtWxZ4SZSSr7s0YNHtm0j2W7n0uBgXmndms5O53HLn5CUwJSVU9h1ZBfRIdGkF6Rjc9iOGm1EBUbxy+i6Oz+bDm1izvo5fLLxEw7kHChp9zZ7M7TTUEb1GsXF7S/Gy+xVZzI1JSpTFpWVVR1obLwQ6COl3Gh87w48UwtyajT1R3KysvW7U5fhtUeOwKRJ8PrrYDeqGJ99tvJL9K3wfxeANWvW8NBDD/Hb5s109/JiaZs2XNS8eenI6Did+OVTose8FlNvfozU3FTmbprL7PWz+ffgv2WWndP2HEb1HMW13a4lxCek1mU5malOuo9OxYoCQEq5SQjRpRZl0mjqnnLhtVlFWWSmH+CAv4snZg2qPfu8wwHvvacKEaUZcf/t2infyTXXlPVLuLF//34mTJjA7NmziYiI4N133+XWli2xTJtWK078Oint6kaBvYBvtn7DnA1z+GH7Dzils2TZKaGnMKrnKG7qeRPtw9rXyvE1R1MdZbFBCPE+paVURwAbak8kjaYeiI9XPgsgy2zncNpurA7BZ1e3rb15BkuXqnoSmzap7wEBKvnf2LEqp1MF5OXl8corr/Dyyy/jcDgYP348EyZMICgoSK1w2WU1J58b8WfFMyZhDNgo47OokdKuBi7p4rfdvzFnwxzmJ84nuyi7ZFmITwjXd7uekT1Hclabs7Qfoh6oMhpKCOED3AOcZzStAN6WUhbWsmzHjfZZaI4LIxoqZeMqdoea+PSSlqzqHgzUsH1+61alJL7/Xn0XAm69VZmhPMx9cLlcfPLJJ0yYMIH9+/dz7bXX8tJLLxHjofxpbVDej1FTo62taVuZs2EOn2z4hN1Zpc54i8nCkNghjOo5iss6Xoa3xfuEj6WpnONycDdmtLLQnAgxr8VgFmYO5R2iyFmEt9mbCP8IXNLFzgd2Hv+OMzJg4kR4801lfgI47zyYPl3Vvy6Pobx+37KFsXl5rMnJoW/fvkyfPp1zzmncZWbS89OZt3kes9fP5q/9f5VZdnqr0xnZcyQ3dL+BcL/wepLw5KQyZXE8M7ire9APhRCHhBCb3NqeEULsF0KsM15D3JY9JoTYLoTYKoS42K39EqNtuxDi0dqSV1OPJCQcNfO4XvZhEOQdxO6s3diddizCgt1pZ3fW7upN5KpIDodDKYjYWDWxzuFQyxcsUKk7PCiKnXfdxbVr1nDuwYOkFBQwp3lz/nrmmUarKIocRXy15SuumncVUVOjuG/xfSWKom1wWyacM4Et923hr9v/YszpY7SiaGDU2shCCHEeKuHgbClld6PtGSBXSvlKuXW7AnOB04GWwE9AR2PxNuAiYB+wGrhRSplIJeiRRSOiJuY3nMg+KpiI12vXo2w+vBmXdCGRCAQmYaJb826sv2f9sclx5Ij6vmePWicwUFWve+ABj36JrKwsXujZk1f37sUiBOMjI3m4RQv8CgrqPpT3BJFS8tf+v5i9fjbzNs8joyCjZFmgNZBrul7DqF6jOK/deZhErT27aqrJcYXOetiRCQiQUmZXta6UcoUQIrqaux4GfC6lLAKShRDbUYoDYLuUcqdx/M+NdStVFppGRE2kDz/efXioc9F1QDqb2iolASpVhJSS1LzU6stRUAAHDqgU4qD8ErffDs89B5GRFW7ucDh4//33eeqppzh8+DCjw8J4vlUrWhXPvG5EmXKTM5P5ZMMnzNkwh6SMpJJ2kzBx0SkXMarXKK7ofAV+XhUnPtQ0PKqTG+oz4G7AiXqyDxJCvCalPN6sYmOEEKOANcA4KWUm0Ar4022dfUYbwN5y7Wd4kPNO4E5AV/gqR7FjsqGlaQBqZn5DdfdRfhSRnl6hkrlnWR4Lb7ZgMZf+ezicjpJspZXKERSkRhHF6TlAjSD+/BN69fK46dKlSxk3bhybNm3i3HPPJaFdO07LzVXyFdPAM+VmFWYxP3E+czbMYcXuFWWW9YrsxcieIxneYzhRgVH1JKHmRKjOuK+rMZK4AkgAYoCRx3m8t4H2QG8gBZh6nPs5CinlTCllXyll3+bNm9fUbhs9xakbUnJSyqSbbjD1CWJi1E3QnUpuihXWWajOPopHESkppaOIxMTSCXDF+PnRLtMFQqWUkFLidDlBgLepkmgcux28vFQYbLGi8PaGVq1UbQkPimLLli1ceumlXHzxxeTn57NgwQKWL1/OaRMnNopMuXanne+3fc/1C64n8pVI7vj2jhJF0SKgBePOHMe6u9ax7u51jDtrXL0riqZap6MuqI6y8BJCeKGUxSIppZ0KKudVByllqpTSKaV0Ae9RamraD7hn+WpttHlq11STBp9u+hjSh3tSfKuHD6h6H+4mIiHUu9WqlIY7+fmktwgiwjcCq9mKUzqxmq1E+EbQNaLr0fJLqUJge/RQqTpcLjCZlJJo104pjEceOWqz9PR0/ve//9GjRw9+//13pkyZQmJiIldffbWaQ1CcKTcqCjIz1XsDKQMrpeTflH8Zu2Rshem/h/cYTsKIBPaO3csrg1+hVwvPI6q6pME/ODVwquOzeBfYhcoNtUII0Q6o0mdREUKIKCll8X/nlUBxpNQi4DMhxDSUgzsW+BuViypWCBGDUhI3AMOP59gnKw0y3XR5c9Do0SoqqIqZx+6KD1DvNhjvtYxfqkpBXpGpKioKdu9WysXNMe58bCzW/Fm08m5V+QS0zZtVYr+lS9V3kwkuvljtc+dOZebqqOI0ik2BO9N24rXWi4PfHyQ/N5+77rqLZ599lgpHw3FxDUI5FLM/ez+fbvyUORvmsOlQSZBjo0n/7en3M2XllIZjlm3AVKdS3uvA625Nu4UQA6vazqioNwAIF0LsA54GBggheqNGJruAu4xjbBZCfIFyXDuA+6RU8/uFEGOAHwAz8KGUcnN1O6ep+zQNVVKRU3nWrGo9NVeq+EZXcWOtqFqelxd07QrNmpGflMh/gUW8dqGVvV7LGN1rNMt2Lat4Alpamio29O67Ko04wAUXqPkS+/ap/kVHlyigvLtuZV6ci20uL9K+SaPocBG+nX15c+qb3D3k7mM/h3VIri2Xr7Z8xZwNc/hp509l0n93Du9ckv67bXDD9xM2yAenRkR1ZnA3Q93oz0Hd5H8HJkop02tfvONDh86WUlG66fpMLX0i2V091Vmo1szqSsJrEzpQvXNks6n5EhMnqpBYUHMnpk2DSy+FJUvg2mtVf4RQju1WrVh8ZB9P5dlYW+DCJ8qH1te0xtLRUucZW6uL0+Xk112/Mnu9Sv+dZ88rWRbuF84N3W5gVK9R9G3Zt97TbhxL8MYJ/X5OEk50Ut7nwGHgauAa4/O8mhNPU5vExcYxI24GUYFRZBZmEhUYVX+KApQ5qHyd6GpGP8WfFY/NZSPPloeUkjxbXvXzE1XiA6jSryMlfPstdO+uzE5HjkBwsFISmzapfExLlqiUHXmlN9aUggJu27GDy9IK2Vno4vzzm/NLoIVln+1h9hv7aLuy6gjwunTIbj60mfE/jqfdq+24aM5FzNkwhzy7Knp0Tddr+OaGb9j/0H7eGPIG/Vr1axCK4lh8ECf0+9FUa2SxqXhSnVvbRillj1qV7ATQI4sGzAnWjaiN/ETF6bfdb35SSjILM9k56BulIH76SS0wm+Guu+CZZ8DdzzBoEPz1FxQWUuByMU0IXpQSG3CvCW6MsNLKATazoNBqwmpz4ucy0XLWQo/ms7oYFRan/56zYQ7/pJStZ3Z2m7MZ2XMk13W7jlDf0Bo5Xk1yPCOF2spv1VQ4odxQhtP5b+ALo+ka4HQp5cM1KmUNopVFA6ahVaSj4puONT2LR3/MZ+jygyrCCWDwYDWa6Nbt6J3ExCD37WOulDzqdLIXFcHxMnCK2cx/ESasLoHN22LMDHcRY40gwD8MmjWrsJRrbZlNCuwFfLvtW2avn82S7UuOSv89sudIRvYc2eDTf1eq5E8kh9dJzIkqixzAHzD+YzABxWNtKaUMqilBawqtLBo4xdFQRuTS6uEDGO+1rN4mDbo/wQcJH676aT/3LkkjsND43+jUCaZOhSFDPNaXWHXaaYxdt46/XC56A68AAwFMAlP3HuSnp7LXosweVrM3LQIiCS6QKnKqQ4cKFWdN3gxd0sUfe/5g9vrZfJH4RZNI/619EDXPCaX7kFI2zDg4TePFLSTU/Ubtbneu8doRlYkTG8cM+QYrZ4zntrmbiE4zMsKGhMCzz8I996jIqQrYvXs3jz76KJ//8w8tTIL3BNwowCxUyoMMX8Ge/11Nv8+W0am8+W3nlkrTlNREJFtSehJzNsxhzoY5ZaJ+LCYLl3S4hNG9RnNZx8vwsVScp6ohUxc1NjSlVCs3lBAiFDX3oeQXJaVc4XkLjaYsnqJWGkTs+/r1xD00lbhfjLkDZjPce68Kj23WrMJNcnJymDx5MtOmTQPgySefJCdzIWcv3I7lsA0pYWekN69cHs4Or2X84lZcqcwool27sjt2c/Yf780woyCDeZvmMXvDbP7c92eZZX1b9mVkz5Hc2P1Gmvs37kwHcbFxzGCG9kHUEdUxQ90OPICaPb0O6A+sklIOqnXpjhNthmpYVOaovXfxvfVnd05NhSefhPffVxFPoEY8U6dCl4orB3/333c8/PLDJC1IwpXjYuDQgcyaMYs2bdpUbTYqZ34jPV0pjEqc/dV1yNqcNhYnLWb2+tl8t+077K7SNCZtgtowoscIRvUaRZfmuiKyxjMnmnX2AaAf8KeUcqAQojPwQk0KqGnaVDZ6qJdJg0VFqq7EpEmQk6PaunSBadNIaC+ZsvI+kpce7T95cfaLPDPhGWz7bfif4k/4neHsbrubTYWbaEObqvtSfkZ2sbMfyo423NKUxMXGeXxSllLy9/6/mb1+Np9v/rxM+u8AawDXdL2GkT1HMiB6gE7/rTlhqvMLKiwuoSqE8JZS/gd0ql2xNI2KKgoPJR9JPioVdfHM2Upj32uwoBGgRg8LF6pZ2+PHK0URFqYcyhs2kNBeVhi3/97S9xg2bBgTRk/AVeAi5vYYOj3SifCO4WXmYxxzHP9x5n/afWQ3z694ns5vdqb/B/15a81bZBRkYBImLm5/MZ9c+QmpD6fy0bCPGBQzSCsKTY1QHTPUV8AtwIPAICAT8JJSDqlsu/pEm6HqkGqEwlYVtVKhqWU7NRti+++/MHYsLF+uvlsscN99cNZZ8M47kJzMP75HWBFj5uwdNlqmFfFfqJUHfWFzYh7+vv5YzrcQfUk0Zqu5ZLflTWa1FcefXZTNgsQFzF4/m+W7l5dZ1jOyJyN7jmREjxH1ntVV07ipsRrcQojzgWBgiZTSVkPy1ThaWdQh1Zhkd1yTywYNUllcs7KU2cjbW82ajo0l4d346tfnOHhQVab76KNSv8Rll8Err6hkf24Kaf+O9UTmODkQYOZdk4m3suxkS2jf2ZffliVz45IbayVU05Pz3+Fy8OOOH5m9YTZf//c1hY7Ckm1aBLRgRI8RjOw5ssFkddU0fk5YWRjRUG1w83FIKf/xvEX9opVFHRITo0w57rH5Uiqzys5SB/UxP3FHRqq0GkKobK4uF0hJYaAv3Z5qVrXiKSxUif1eeAFyc1Vbt25qUt3gwep7OUWXu+lffrK7eNQFW1HzJF60QFgLP2L35pUovUFbbNz5yxGi0orY28yM5ZHH6Hf7U8d1+sor0jxbHrn2XPq17MeqfavKVOfztfhyRecrGNlzJBe1vwiL6ZgKXWo0VXJCDm4hxHPAzcBOSifmSZRJSnOyU1E21wqKF1XmqK0QmzFwNZtL3x0ObAV5WE1RnkNtpYQFC1QNieJ8U+Hhqpzp7bcr81MxbmnLNxYUMNYm+Vmq4u8LTBAnwCTBmlIECQnExcXx+fLRtPryRfJNDvICfejhCiHgxVnQqt9xmceKnf9eZi9S81JJL0in0FHI/q2lZVsGRA9gVM9RXN31aoK8G9wcWM1JQnUeTa4D2jdks5OmHvE0f+BEK7p5eytzVnExoeKRhdnl0VnO2rXw4IPw++9qgZcX/O9/Kjw2JOToY8TEcGjvXp48coT309IIBqaj8uZLs8AkBF7CjMnsVTJRrt9ny6BZ9NFmt2OpGV68mS2PjYc2UuQoIseWU2aZl8mLZwY8w4geI2gX0s7DHjSauqM6ymITEAIcql1RNI2S4oieygoPHQ9dux7tswgPZ19gLvn2/DJ+A/+0LJ5PKIAH3UbPw4YpmWJjK9x9YWEhr0VH8/yyZRRIyf+aN+cpLy/CDhxQow8vq1JQLpfyvxSPUk6wZrjT5WTZrmXM2TCHBYkLyqT/NgszYb5h+Hn5ERMSw4RzJ1RrnxpNXVAdZfEi8K8QYhNQUrFeSjm01qTSNFgqdMYeb0W38hXz3JVM8YilVauyVeweGostfxbYIFT6cN3SfdyxNB1/m+F769FD+SpsNpUdtty+pZQsWLCARx55hF27dnH5GWcwBeh06JBSdFYrHDoEDodSUJGRSnlEGVFG1TS7lSfxcCJz1s/hk42fsC97X0m7xWTB2+xNiE8Izf2aU+goxOay8cjZR5di1Wjqk+qEzm5GlVbdSKnPAinlco8b1TPawV07VBnV5H7zDzJs69nZRysCqF722fIzno19JGxbzOpX47nt8620yjQypjZvXuqXWLq0wn2vvv9+xs6fzx9//EHPnj2ZOnUqF154YblOViHXMWTNPZR3iCd/eZK5m+YeZWY6s/WZjO41muu6Xcef+/7UKSs0DYITzTq7WkrZr1YkqyW0sqgdKp0vERFfehO122HPHuVsbtdO+Q7K31CPt67F33+r+RIrV6rvXl7KT/H44yq0toJ977PZeGz3bj7JziYiIoJJkyZx6623YjabKz6GByVVneWFjkIWbV3EnA1zWJy0GJcseb7Cy+SFv9WfVy56hdv63Fbl+dZo6pqaqGdRBCyirBlKh86eZFSa++ib6NIb9LZtSjkIoW7mHTserQiqGXJbwr59MGECzJlT2nbllfDyyyrFdxlB1b7zXC5eTk1lysGDuICxQUE8tmcPQUE1G1Hknv57fuJ8soqySpaZhIkwnzCa+TXD38uffHu+TqGtabCcaG6oU433/m5tOnT2JKTS3Efujt+iIhXqKoT6DEc7gath+09ISuD1Xydz0VcbuOeXbHxtxlN6r17w6qswYECFcrqio5mzdSsT0tI4YLdzfWgok0NDiW7XrsQ8diy1mz2xPWM7c9bPYc5fM0kuOljSbpLg7eWLw+WgdVBrwnxLHeIlkVsaTSOjOvUsBtaFIJqGT6Ups2OmlN78vb1LRxbe3mrj8k5gt5DbLLOdzIwUhM3GW0OsDEhKAJeLpc/fwoffZhJ1RNWXSAswk/LoffR4dFrp/ItyrFixgrH79vFPSgqn+/gwPyaGs0ymMuG8J1JDI6Mggy82f8GcDXNYuXdlmWWnZnhzaaKdK/8z8eEVLfmoRQp7s/diNpkJ9lYmslpPkqjR1BLVMUM9VEFzFrBWSrmuNoQ6UbQZqvZwn4kdaA0EofIWXbcniKfmH8LfP6R6PguAhATSnn2UgqRE9jez8snFUfzcyYuuO7N5+qsj9NyVD4DNIvj0wkjeHBRIUETrCk04O3bs4JFHHmHhwoW0bt2ayTfeyI2rV2PavbusXyEhgX/GDSc8NYfkEJhyNqzo4kewTzCxYbEV7tvmtJGQlMDLK19m1d5VSEr/Z1oXejNyZwA3pUZi3r0Xu9OOn02SHmJl+L2R7M7ajZfJi87hnWulhrZGU5OcqBmqr/H61vh+GbABuFsIMV9K+bKHg35orHtIStndaAsD5gHRwC7gOillplBG8NeAIUA+cHOxT0QIMRp4wtjtJCnlrGrIrKklimdil386XxSTz6EhkinrrIQfKiqtB5GTo3wVFc29iIvjukNTSMnpgL/Vn8gMG9Pm7Cfu79JU2z/1CeH1q1tzINwbKeVRJpysrCwmTZrE66+/jsViYeLEiYwbNw4/v7IT94CSSKaA3BwOezuJyIbXvxM8SCFLYguxO0trQEgpWX1gtUr/velz0gvSS5aZhIkAawD+Xv4kvu8kMKI1CMEGZxEWYaHQKmmZVkSwTzBtZVv25+4nszDzqEinmjCFaTR1RXWURWugj5QyF0AI8TTwPXAesBZVk74iPgZmALPd2h4FfpZSThZCPGp8Hw/EoSrxxQJnAG8DZxjK5WmUspLAWiHEIill5rF0UlPzVFSj4s/ucN2ZzYg/a3K1b4LJR5IJsAmu+Won9/9mw8+4X/8bBeOHeLM8Oh9vsZvIokgswlJiwnE4HLz33ns89dRTpKenM3r0aJ5//nlatmxZ9gDu4bxHjkBAAHlegBQUWAXCJnnoDyc/xFoochax+8huPtnwCbM3zGZb+rYyu/L38qe5X3NCfEIwm8zk2fJICjxIn/x88PfH2+xdMrI4EK7Mb15mL85sfeZRI5aGUE5WozkWqqMsInCLggLsQKSUskAIUeRhG6SUK4QQ0eWahwEDjM+zgGUoZTEMmC2VTexPIUSIECLKWPdHKWUGgBDiR+ASYG415NYUU9nkt+Mk+UgyYT5lZzL7efmReDiRL6bewnM/H6FVuo3kkN1MP+d3Vt/2BE+d/1RZeXbu5KeiFIKzbIQXqEUpAfDEBYI5vQWYXZgwYXPY2HVkF+G+4Uy7eBo//PAD48aNY/PmzZx//vlMmzaNPn36VNzv4nDesDDYvx/y8ggKgiPeIJHke0nCciVO6STPlkf0a9FlduFl8qJtcFuOFByhXUi7MpFgfl5+vH6uNx//pDLhtPCP4HDabiwOwayLIiqtZ9EgyslqNMdAdZTFp8BfQohvjO+XA58JIfyBxGM8XqSUMsX4fBCIND63Ava6rbfPaPPUfhRCiDuBOwHatm17jGI1YcrfMFNS1PfjrQthUFFkVJ91h/j4q8N0PuyiyAQpgRCVI5j+nZ3/uZ6mxeq3GJ0SyVPzDyFcTsThdNobEU42E7x6roWXzzWRYbFz6Q4TE/60EJVWSHIIvHqOF/9FB/HGA2+QkJBA+/btWbhwIVdccUWZG3gZpkxR/S6OuPLxgaIiWuRBlp+ZP6IcLGkPP7UHp3TilGqCX6hPKC7pItQnlFCfUAocBeQ58jiUe4jIwMiS3efb89lzVle4Ih6mTMErKZGMYG8mnWHjxxb76WjuyLSLp1V48/ekbHWklKahUp1oqOeEEAnA2UbT3VLKYu/xiOM9sJRSCiGqX0yj6v3NBGaCcnDX1H4bPeVvmMXvx5H4Dkrt7JsPbSbblk24XzgRfhH0WXeIJ+YfJDzbhUOAGWibDXuCJUUmGPcHXNE1j+sXbsN5qIigotJLlG2Fjc1h/EAHAsHF2ySvJjgQVjP5AT4E5joJme8gqWgbqUGpvPLKK4wZMwbv4kgrT5TL45QXFsAWew6f9IBPekF6ObeGj9mHCedO4Ofkn0nNTS3z1B/uF05afhoAR2xHKHIUYTaZubHHjXB+HAkdMMxKofh5+RFtzyfXnutRtHopJ6vRnADVSohvKIeaCC9KFUJESSlTDDNTcXLC/ah6GcW0Ntr2U2q2Km5fVgNynDycYOI7d9zt7K2DWnMo9xBp+WnYnXbeW15Es5AWeB3Zj11IMB72I3NhWxi0z4QJS/M5dZereBEFVhN7giQ5FklUaT49Hl4JRSYo9JJ8nuPgnSw7+RJGBFiY1r07zWfMgO+/r9qcZszlOBDmxYctDjD77AySyp2KsALID/ChZVBLrCYrC7YsYFvaNpzSiY/Fh8iASIK9g4nwiyDXlktaYRoOlwMfsw8hviHMWj+Lfi37HbNZqdIwZI2mAVLXxXkXAaONz6OBb9zaRwlFfyDLMFf9AAwWQoQaBZgGG22a6hITo+Y4uFONxHcV4X5DFEIQGRhJdHA0XZt3pU9BCE4fb2wWgckYNLgEeDsgKgeicuGx5UpR2E2QHALJkd4U+ljws6vvxURnwvdOGJZiY8oRO728TSwONjGrwEXz9PSy5jQPdbnzbHl8ektfLj5vN20u3MiTPdNJClOChTi9MAszVmHFHhxIt4huhPqEYnfZSTyciEmYMAkTdqedPVl7yCrKIt+uzmF0cDS9InvRKbwTkf6RJTW4y9cZzyrKYl/2PlbsXsGgWYOYuHwig2YNIua1GAbNUvNZZ8TNICowiszCTKICo3RIraZBU2ultoQQc1GjgnAhxD5UVNNk4AshxG3AblStDIDFqLDZ7ajQ2VsApJQZRvGl1cZ6E4ud3ZpqUoP1Jsrb2bOKsjiYc5CkjCT+tvoQcrgQa6CZqEwXuMDkAouEVsaoodBL8GvvEDonZVJkUeML7yIHXi6Ycpax04NwdRGsL4RTLPB2uIWBAb60P2jD5G2u1Jzmki6W7VrG7PWz+XLLl+TacsEIjgopgAv3eiEiI9nRKYKkjCRsThtFztIYjZScFKwmKy0DW7Inew8AQgoOZB+gRWCLkgSK7hT7GYrNSg7pYH/2fgocBQgE3hZvktKS+G3Pb7QIaEGEX0Rp5FPcDJ32Q9NoOKYa3I0FPSmvHFUlxqsm7okEs4qy2JO1ByRYLVYGbbEx7Tsb0uqFr00SecSBxe2n9XlPE29e2Zq0cD96/5vCfcsLiM6UJAU7ePlMyZIoMP1qwvWvC6sVHjfD8CBfOrTsohTc9u1qgp97ESMjl1TiX99VmP7barZyWcfLGNlzJK/++WoZP0RWYdZRE+a2Z26nXVA7QnxDyCrMIjUvlSJHESaTiYXXLWTKyikeEynGnxXPrd/cSlpBGk6Xs2TinlmYsZqtOF1OvC3edGzWscx2WlloGhInOilP09hxrzdRrDjuvfeYw2jd7ewHcw6qmS8CWvi3YEnHPXhfbOaVJQ5aHpElfom/W8GUa1uxODwTm+sAXc1dGfXoXHIehVErp7Byx0ocfzhggROX04XlbAvyXMGafXDvtlg4lFlaZ6K41Cpw2GpnbvhBZg8sYO1b3crIeWbrMxnZcyTXd7++JC+Tt9m7jI/AYrIQ5htGZEBkyYQ5q9lKRmEG29K3UeQswtvsTURABLFhsSXmIU9+hrjYOCL8I8guyibflY9JmLAICyZhotBRiI/Zp8woRkc+aRobWlmcTJxgGG1cbBwzmMGUlVNIykjC18uXFv4tCLUGMfhfwZNLnbQwAoD2BwkmXGTi61N9GZXqy7I52UQeluwKSeSDdTezp39X+mX0Y+N7G0k7mIZvD1/EYIE92I7ZZKbvbY8RXjwvw5C98P57+S40m9kd8kiIyMLh5nFr4d8CH4sPdpcdH4sP0SHRZRL4ucteXDeifFjrxOUTeW7Fc0gpcUkXNqeNHFsO57c73+M+3CccZtuy6RzeucTEZTaZKc4M4pRqZFGMjnzSNDa0GaqJUmEqibumHJ3pNTUVcnOVecfTSKOCSX2DDimTzLm7XIz7Yi+d9qpZdfleMGdwC8afmka+VXDT/nAmfplOoVmSZbLjZ4etdgt3egsOH7LToVsH7phwB0vsS0puwAOiB7Bs1zKSjyQTHRzNpbGXkpSRxLz1n5LlLA2bCjL7cX2vEcSGxfL2mrfxNntXXJSpmgyaNYgNKRvIKMpAIjEJE0IKvCxeLLxuYZX7KjbTOVwO9mTvQSBAqsl/LlwlPgudI0rTUDmhehaNkZNdWXiqaLfx5Tz8IluV1pDIyoLdu9XnHj08V6srVxkuL+8Ik88zc+q/B7gqsfT3s6CPD+9eHc0O/yIyCzMJsAbwxdvphB4pJNciOeCAGTnwYyE0twgsN7Sg06BO/HrLr0fJjlRP3+kF6dhdpTmbBAIfiw8xoTE8P/B5ruhyRZmbdGpeKkVGjqYOYR1Yf8/6ap+3mNdiSkKBzSYjq60Em8vGuW3PrdK/4H7e7U47Kbkp2Fw2ujbvytVdrmbZrmW6Gp6mQaN9FicZnmL+/wvKKMljBKhRRXEacSEqnrBXblJflrDhSk/l2c9kSYjsn61hxvAOjLj9dX50S5J36ze30uJwAXu84aNsmJunDnNXoIlxXmZuOCeK3dm7S+TOKMjgwSUPkpKTQoGjoEyfwu1WZFAgoT6hBHkHkW/PZ9yP4/C2eJN8JBkzZvbm7EUgMAszTukkMS2RhKSEat+UY0Ji2J+9H4up9N/CJV34mH1K/QuVpE4pb6Y6o/UZZZTCU+5mNY2mkaGVRRPEUyoJ9zxG+PlBQQGYTBAZ6bZiuQl7xZP6pIS0NPz37cXiUlriYKgXr1/dmq96WokKalnmphwXG0dz3+a8aU7l40OSdAlxvnBfELR3SY6E+ZBvz6dtcFsWbV3E7PWz+Xbbt9icpU7sFrlw6Q4L12wRdEy18eL1vvx7qqoL4T7pLSYkhr/2/aUUhTEiEFKUzIGoTFm4m+uCrEEIBE6Xk0uSYOzvDtplujgQ7s2ioYHV8vkUZ+XVaJoa2gzVBKmyVnZxGG1mJgQElFUW5cufDhqkwlbT0pRyAVzAgXAr1z/djSKrqbS06gOl5VB//vln4kbFYT9gp59FMD7IRLS3Ez87eDnh4RER/NqqEFD1MIoxCzODd8CNm0z0TrdglgKXdGEuKCQ3LIB7xnUqWbf4uG8OeZOhnw/FjBmzyYzdaccu7QgEEknPyJ5MvmDyUTfxisx1KbkpnJOYy2vfObGbBTZvM34OiLKG4x8aocx0x1o3XKNpJGgz1ElGpakkYuOO9kfk5R09YS8hgYzHxhKweRtWR+kDxRFvyPKB++Mka45sxtvsTbA1mNjwWAC2bdvGww8/zLfffotPuA/Nb2nO6f6+tP3xEPaCfKb2czGvuyTXeqgkl7FJmLgg5gJG9RqFv5c/fc+5jiw/EyaTUhQSFy5fH6LSikrnPxh+iVEHIoj7Zgq7NgiSguy8fJaNhFglb/Fch42pG7ls7mV0j+heRmlUZK6LCojiqb/2YvaR5FsceJu9aR4aib/DomqLd+1a9mQfZ+oUjaaxoUcWTZRi80rblYnc/1sRnbO98evY9ehop4om7OXmYr/tZiw5+SXzJZxApjcktrLwQn8HP3QEq8kKgAsX43qPo/DnQt588018fX15/PHH6XxpZx78+UEK7YXk2nPVjGo3ukd0Z1TPUbQIaMFH6z4qidz64LVdiIOpZJlsuJCYhCDAbma/v4MLb1apOJBwwTY7by4WhIdG4fTx5nDabkw2J/fFSZZ0PPqcFKfx6Nq8K5MvmMy9i+8lzCesTNZaKSVfT9hAm+iepYEAagEkJqpzpEcWmiaKjoY6WakgkonMTGV2ys4+OlTW4YD334f771elUQGbWbA/CApNEptFZWqNyVS5nKacBb93CcD1t4uCnwqQhZKW57dk8qTJNI9szuz1s1m4ZWGZyWjNiyyMaHsZo694ml6RvViyfQm3fnMr2UXZ2F12vExeDN1p5c0EQYYrh0IvM352sDhc3Bvn4qdOFiQSb7M3P3zkoHm2E6evN52adSSrKIsDB7dzIAAuutlNAbiVQfU2eWM2mblubyD3/JpLyzQbKc19mD04klXdg1VOqbcO0ke2OFopWK0qzNj9fFZULlajaaRoM1QT4LhKcJZPT+5wQEaGKnXauXNZB63FAg89BJs2AeAUkB7kRXqghXxXIUEF0OEwJIVCpq9KDnjVN/DH9/nkZ7ngFPAe6k12RDa3/HxLSW0IAB+HYNj+AEalRDB4twVL0QZolwJxvXn050dJK0jDIix4mbxwSRcLonMpuMjMo6ustMlwcCDcm9mDI1nafDfeJi86hSu/RduMjWT5mXEayijYO5iD/gG0z8rDaraWcZaDGlmYTWYGbi3i2cV27BYTmX4QeqSI8XP38Ow1EfzSxYrzobHwolG9110pTJtWcl7zkxL5L7CI1y60svfQFOKT0I5tTZNGK4tGwHGX4Cyfnjw1VUU/ORylobI2G4wcCelGjWkhwMsLl8OGX6GTPC9JnlUSlaPShudbYbsDpufA3zZoGeIi516gORSJIopspaOIHhE9eOA3G5f8m0NuYQ5Fzr3sMHvTyhxMgBGeuy1tG2bMmExqOrZJmDA7zXxzShF7z+qDEKLET+GwObC77GQVZRHsHcyBcG9CjxTh9C2dGd3KEsK6sCJcUhVVch9VFCuj+D/AYTGRb5W0DW7HwdxUCgsKuGd5Llc99Bn9YuOgVT+P+bTK167I1yVRNScB2gzVCKg0uqmyiWKDBpWdsb1xo1IGViuccopaduhQ6frduqk61ULgPJiCwxgdpPpBVB6sDoQpNvjGAT6dIbIP7GkHrgoS3VuEhWZ+zVj9YhqHvJ0IYcLL7KUilKST9oTht/cgvs/7IqUsnQQHOF1O7E47HZt1LDMb2ul04sSJ2WSmXXA7Bm4p4on5B2kW0oKA4IiSEcDqx0Zzu/1LtqRtwe5SUVEWkwWzMOPCxfbpLrL8zHhZrHQyEvsVJyVk586jO1PN62E1W2nm1+zYRn8aTQOiMjNUXdez0BwH5WslQDUT0cXHq5FDXp66GVosalTh46PMTYaiKPIyce/NzfnHuY9cHwGRkZijY3BYTAgJXjYY5wcXhME3g8EcDwVXw66YihUFgJfZi4yCDJKCVbisRGJz2pBIfO2CTX65DJo1CKfLic1pw+60I6XE6XLiki7aBrfF5rJxIOcAwvgzmU20CFCpwvdn72dH/1gOTX6SgLax6kYfFQUzZtDv9qdYf896vrnhG3pG9sRisuBwOXBJF22C2rC/mRWrzYHD5WDDoY1sTd9Gbtahatf4qOh62J12EtMSSclJKTP6S0iquN6GRtPY0MqiERATElNSfKeYaiWii4tT/oioqBLHtgsJhw+D04lLQIav4MHRUfzVvw0Rh/I5fCQF+3+JsGcPLuniuTYmOp0Lb9wBhaNBngoOK1iccPF26L2/4kNLqXIrvXwmeDvB3wZIiVehDavDxbOnF5CSk0LboLYIBA6XMjGZhZkw3zDeuvQtRvcaTYGjgCJnEXaXnXDfcFoGtqRzeOeSUVW/259SkUg7d6r3YlORYbqzOWz0iOhBq6BWmEwmcotymTU4AqsDvIucWDBjLigi/chBVg8fcNzXIyU3pUxhKH+rf8mkQI2mKaCVRX2TkKDMRTEx6r2Cym/xZ8Vjc9nIs+UhpSTPllf9EpxxcfDWW9ClCyQnY3K6kECet4k/WwtGXS34qYcvQgjyfC20ynSSaink4b4uokbDpFtc5JwDqInTdD0EE3+BeV9A333wyo+w41X46WO4eJtax8vkhQsXSPipk5kHLrNwMEjQrFCQEgD/G2JiWWcf/K3+hPiGcEroKfhafLEIC2e0PoOPhn3E6gOrefH3FwGVD8qEibSCtJKqdZUpyoSkBIYvHM6erD3sz9lPti2bSH+jql9EV7b2i+H566LIDPEmON9JZog3k65twXivZdW6ZJ6uR1RgVJn1dBpyTVNC+yzqk4pCWz2EYhZHQ1U7EV1CArzwAqxfr8I9jev8dwdfXr0hmm1t/Nh4aCMCgdVspUNYB0Z/solv29j5pQPIUhcCLbMhNUg5oYVQ6TAu3OrkzQTD6e0FfnbwdsF9cfBzJ0uJsogMiCS9IL0kA6vZZMbmspUUGSrGfRZ4QlICV31xFS6XclLbXCqqyYIFq8XKdXsDmbIukvDUo8N/i3NSpealqsyxmDCZTESHRBNkDSKzMBOJrHB+xf7s/XSN6Fotn0P565FekI7NYTt2v5JG04DQ8ywaKuUd0FAjk7zWvPM0sfEvEJDnwFx8eS0WXh/ox6yr2iOMyKOtaVspchThxKkcyG7hrt6FcMM2C8P3BtF+ewZdx1rxtngT6R9Jal4q37yXS2S2pMDbKNkgwc8Gh4IEg29RRX8cLgdR/lF4W7zLZGBFgs3p+cY6aNYgftvzm5r0JyjxawBctsPC3GXN8PcPqVDB9nqnF4mHE3G5XCWRUBKJr8WXdsHtSp7+yzuoU3NSSStMIzo4+rjSnHvK9KvTkGsaE9rB3VBJTlY3PHdOMH3E6vcn0vX+5wjOVYpCAkVmKGgWzDn7TOQ7CihyFHEg5wCFzkLs0o5LupSicEHz7TDl93Ayfz6Vj3f3YnBOBFktlKmoY7OOBPsEK5NOpqTQKvC1+OFj9kEIgcPHQmy2Fz0je9I9ojtRgVHkOnJx4eKM1mew6IZFrL97PZMvnFypWS35SDI+Zh/sTjuFjkJsThtCKCf3M2v8laLw9y8N/7VaVZgrlITiumeOBShwFJQcoyIzUlphGuF+4cftc4iLjWNG3AyiAqPILMwkKjBKKwpNk0LPs6hPYmKOHlnk51c7KqcMiYkwbhz9liwBlJJwCbCZwSwhPyudv9uAdUsWGyLKjSYPgHmvmWFhrZm9osh4aheQnwc2G87HxmLLn1WmJOn+Zl5E5ZrIlw6sZm+c0kGA3cyBcGvJbiP8IvAyeZVJMAhVV5yLCYkhpyiHXLtKDyIQJfMmYjKBlpUoWMOyVKwsHNJR4mwvvnknJCUQYA1gW9o2ENAxrCPB3sFE+EWU3e0x+hx0xllNU0aPLOqT8qGteermvHr4AAbNGkTMazEMmjWo8vDLtDTl9+jZEwxFYTeBzQS5VkiIheFXQcuH4Z7LKFEU3jnAHxD6RSifDfgM+/d2vvxkF/7vflgaPRUVxerHRjPeaxl5tjwO5h1kf/Z+ogKj8H70Cdr7t6KH/yl0Cosl2GnF4nAxe3BpBtvKHNFxsXHEnxVPdEg0yUeSmbJySkk/48+KJ8+eh1mYMQkTEolAEO4bTnIoSqG646ZgO4Z1VCMll5qP4WXywsvkRffm3UsURXGUVNfmXYkOjibXnkukf+TxRZxpNCcJ9eKzEELsAnJQ+ekcUsq+QogwYB4QDewCrpNSZgrlhXwNGALkAzdLKf+pbP+NxmcBZRP5BQaSXZRD1uG97G9m5ZOLo/i5k1fFtm+bTUU5PfusmkgH7Aq3ED/YxG2rbHzbBb7oBmlugxY/G7T7T7BtncQ7GSaEhPLQhx/ie8UVZUUynLebD20m25ZNuF94xeVA3WRPiwgkvncqf3YPrZbNviobf4tXWpSYp7zN3kQGRBJkDaLHP/v5+Cd/j0EBCUkJ3PLNLeQU5eBwObCYLAR6B/LRsI+Ii43zPKHOYiXXlqt9DpqTmgbn4DaURV8pZZpb28tAhpRyshDiUSBUSjleCDEE+B9KWZwBvCalPKOy/TcqZVGMERm1q+AguWY1kc3qlLx0Y1t+6mgpjaqREr7/HsaNUymzAYKDef3iEF7qncdhR1bZMqQSBu6EVuvh+/8g0wa3mM1MMpmIat/+KGe6+018X84+CuwFqp6EyYyvxZdgn2Biw2IrjPA5loitqmalV7smR7lUHFXJEfNaTIWRUMV1MY4p4kyjaWI0FmWxFRggpUwRQkQBy6SUnYQQ7xqf55Zfz9P+G6WyMCKjNuTtxCKUvd2nyEl6iJW7x8aqsNILFqlkfz/+CIA0mdh69flMOM/OV+m/l9ldxzS4bBt0Wg2vZsIWYIAQTPP25lQp1ZN5bGyZFBfF8xPybHn4WFQlO6dUkVJCqMpzTukkzDeMj4Z9dOyJDd3wdNPen7Ofrs27Vj2qOd7TfLypUzSak4CGGA0lgaVCiLVCiDuNtkg3BXAQKDZ+twL2um27z2hrWhiRUd5m7xJnbqHVRMu0Irwzspm+qAh69SpRFBt7taDffV506fZriaIwCzMRfhF0Ce9Cn4zW/PG7ibsywS4EX1ks/OLlpRSFlCpNuZutv3hEkWvLxSIs2Jy2MqG0Uko1zwJBri2XMQljqpXaIiEpoUL/S0xIDH3WHeLtadv4ZsJG3p62jS5rd5NVlEVKTgqtg1oT7hNOWn4a+3P211h00QlNcNQ0HKoxmVVTs9TXyKKVlHK/ECIC+BFlZlokpQxxWydTShkqhPgOmCyl/N1o/xkYL6VcU26fdwJ3ArRt2/a03bt311FvaghjZJFlcbAnaw8CE35FLkwuSViek6ACpUB2NDfzwIVOvu8ICPA2e3NF5yvoEt6Ft1e/TXZmNkU/FeFa60J4C25/8HZm9L8M65NPqogpq1WZnry8ytj6i5+49+fsx+60YzKZyjh8iyfvSSRSStqHtq/y6bwyv0T48tVEPPocdosJm9WM1ebEZLPx1FXN+O/06Er3e6Ic8wRHTcPiGCazao6NBlfPQkq533g/JIT4CjgdSBVCRLmZoYrToe4H2rht3tpoK7/PmcBMUGao2pS/VoiPhzFjCMZK28DWFKbsIzjHho9DLc70gWcGwFv9nDjMqsqc3Wkn357PobxDdAzuSN7yPAp+KgA7WE63EHpJKFcOvxJrbBwMHVrWmR4VVcbWn3wkmTCfMCIDItmTtQdclNSwFgjMJjNWs5VgazBpBWlVJjYsb9KK9I8k2CcYbKqc6S+fQW5IC/Y7s3A6VZpxp8vJA78Vcc/pnvdbE+gQ10ZO+Totxe9G2ntN7VDnykII4Q+YpJQ5xufBwERgETAamGy8f2NssggYI4T4HOXgzqrMX9FoMZL+FTzzBAVJO2iRqZzUDgHv9IWnB0Kz1rE83WsUrQJbMem3SVhNVsJ9w9n621aWLVyGzJQEdQ+i9TWt8Y3yJc+Wx5SVU4jbjvpHSk5Ww/Y33zzqnyomJIaUnBSCvYNpG9yW1NxU7C47EklL/5ZEBJT6DTr6dyTfnl9mZOEeZupu0vISXticNvZk76EtbQnyDlI3/2RJQFgEnURpqO1WuZWotKIycunwVc1RlK/TAroWeh1QHyOLSOArw7FpAT6TUi4RQqwGvhBC3AbsBq4z1l+MioTajgqdvaXuRa5dXNLFyjVf43j1Mc5dvY0WxrgooQNMvDyIUy8Ywfe9RnFGqzMQQjBo1iCVCiMFkuYnkZuUCxHgc4sPsf1jS/br5+VH25WJ8JMxZA8LK1sdz01hxJ8Vz5iEMWCDIGsQlkALNpeN0b1Gs2zXsjImG6BkXXfzUvGyKSunYDVZ8bX4YnfaMZvMOF1OUvNSsZgs6uYfw1ETEltZQtjYLI08W16F+9VogJqdzKqpNjo3VD2yNW0rn67+EJ+3Z3Lf0iMEGw/V/4XD/Nv60330IwyJHYK3xbvMdm0mtiE/IZ+MvzKwBFhoeXlL0junY8NGz8ieAGQVZnEg5wDff1BIdKEPoc1aEuxtpI71kH/qWGz51QlPzbZlsydrDyaUY9whHbQJbqMc1dup0O5cPAlQ+xM0HtE+i1qjwYXO1jYNUVkU31y3Z2zHx+KDALr+kcQrS6F9plony9/C+nuupNsTr9MsuMVR+8jPz+eVV17h2eefRbokEYMiiBoShdnXTGpeKmn5KhGe3WlnT/YeJJKdr0KWnxkpJG2D2yqFcQxV4Y4H9/DUrKIsUnNTKXAUEGAN4LOrPiu9+bv7UCqYL1EfHFetc03d0wB/O00BrSzqmUX/LeKO7+4g35ZPrj2X3ikwfQkMMAK2nGYT2bffROgL04+2xQIul4vPPvuMxx57jH379nHOJeewu99u/CP8y5hrik1Gq/atwoSJlkEt+ezNVJodsZFvFXiZvVQZ0RrIbAuU/sMW+0KMf9jGmoG1scqt0dQUDXGeRZNHSsnKvSu5+7u7uXr+1RzKO4R/Ri7vfQNrZ5YqCnnpEMybNhP6zqwKFcUff/xB//79GTlyJJGRkaxYsYLfEn7j3ZHvHpXh9Knzn+KX0b/QIqAFncM7E+wdzOzBkVidEj+bxOYoLMk/RfwJ+gGKTQEpKWV9IQkJjTYDa7GvRVe702iORo8sapidmTuZs34OczbMYUfmDgC87fDgn/D4bxCoyjKwI8qHiUODmfXOwQr3k5yczPjx45k/fz4tW7bkxRdf5KabbsJkqlq/93q7F9sztuOQDrzN3lyzO4A7f8miXaaLqB5n1syQvZZqcdQnlaUCKZ85V6NpijS4eRZNjcyCTOYnzmfOhjn8vsct7YaEhw9Ec/+X+2iToSZMHPE3887Qlnxyhh8RIS2P2ld2djYvvPAC06dPx2w28/TTTxMfH4+/+025EhKSEjiUdwiby4YZMzanjU9apbH4TpWiI6qmnu6rE77owUzVUCkOH/YUEuyRRtZPjeZ40Gao48TutPPdtu+4bv51RE2N4q7v7ipRFC0DW/Jq81HkLj2NKe/tok2GA7sJZg0I5YqJ3Zh1lj8Fwl4mJNTpdDJz5kxiY2N56aWXuOGGG0hKSuKMEWdw+YLLq5euHGVKCfEJITokGqtFzbj2MnkRGRBZs2agmJhKU4VXZqZqqBxXKpBG2E+N5njQZqhjQErJPyn/MHv9bOZumsvh/MMly/y8/Li6y9Xc1mII5878AdOsWSV1rxk6lOX3XcazKXOPDglNSODHxx5jXGIiG+12zunWjekff0zfvn2Py+FaZ6aUqsIXG6mZ6phTgTTSfmo0FaHNUCfI3qy9fLrxU+ZsmEPi4cSSdoHgglMuYGTPkVzV5SoCvvgaLrpd3SwAuneH6dPhwgs5H/iFO8rs97+ZM3l47Fi+z88nxmplfsuWXJ2fjzislJC7wxVQ70a6DI/K4nhNKceKMePcY/ji8c6yrWeTzjGnAtGziTUnCVpZeCCnKIeFWxYye8Nsfk3+FUnpCKxr866M6jmK4T2G0ybYLW1Vhw5KUYSHw6RJcNttYDn6FKenp/Pss8/y9owZ+ArBS61acX9EBD4mk9reyHFTnK/JnapyJbnPxK71WdBxcZ5v5Mczy9Z9tFLJbPMGhZ5NrDlJ0MrCDafLyc/JPzN7/WwWbllIgaOgZFlzv+YM7zGckT1H0ieqTxkzTwn9+8Onn8Kll0Jw8FGLbTYbb731FhMnTiQrK4s7AwJ4NjqaCGtp3Wr3p9LjGSVUVd+6zjASIwJlzVSVhew2xgRxx9NPjaYRon0Wbtyx6A7e//f9ku/eZm+GdR7GyJ4jubj9xXiZvY5LHiklixYtIj4+nqSkJAYPHszUK66g+4QJkJsLvr6qvkRwcBl7d6OfJHass2xjYtSIwl0R1/Js8xpBzybWNBH0DO5q8t2277h87uWc2/ZcRvUaxTVdryHEJ+SEZFm/fj0PPfQQv/zyC507d2bq1KnEAeJ//1NPoIeMTOzFBYms1jJml9qqvdAg01poZ7FGU69oZVFN7E47e7P3ckroKScsw8GDB3niiSf48MMPCQ0N5dlnn+Wuu+7Cy8ur7E0xKwtSU6GwUH3/7LNafyptsCMWnSBOo6lXtLKoQwoKCpg+fTovvvgiRUVFjBkzhieffJLQ0NDSlerZ3NKg61Brk45GU2/o0NlqciKmGSkl8+bNY/z48ezZs4dhw4YxZcoUYmNjj165niNojifKqs6oLMJKo9HUG3oGt0GxaSYlJ4UwnzBSclIYkzCmyhnTAH/++SdnnXUWN954I2FhYfzyyy98/fXXFSsKUE/LNpuyx0tZc8n9qklMSEyZ+tpQdZRVQlICg2YNqvZMco1G07TQysLgeDKO7tmzh+HDh3PmmWeya9cuPvjgA9asWcPAgQMrP1jxhLaoKGV6ioqqU7v8saa1OBFFqtFomgbaZ2FwLGkycnNzmTx5MlOnTgVg3LhxjB8/nsDAwBMXvo44liirBu3j0Gg0NYb2WVSD6kyAczqdzJo1i8cff5yDBw8yfPhwXnzxRdq2bVsPEp8Yx5LWokH7ODQaTZ2gzVAGVZlmli1bRt++fbntttuIjo5m1apVfPrpp41SURwrx+Pj0Gg0TQutLAw8VXfrQAeuvPJKBg4cSEZGBnPnzmXlypX079//6J0kJKg5FDEx6r2JpKk+rtTdGo2mSaF9Fh7IzMzkueeeY8aMGXh7e/PYY48xduxYfH19K96giU8oq62Z5BqNpuHQJCblCSEuAV4DzMD7UsrJntY9EWVht9t59913eeaZZ8jIyODWW29l0qRJtGjRovINdaoKjUbTyGn0Dm4hhBl4E7gI2AesFkIsklImVr7lsZGcnMyQIUP477//GDhwINOmTaN3797V3VjXNdBoNE2WxuKzOB3YLqXcKaW0AZ8Dw2r6IK1bt6Z9+/Z8/fXX/Pzzz9VXFFB1mVGNRqNpxDQWZdEK2Ov2fZ/RVoIQ4k4hxBohxJrDhw9zPHh5efHdd98xbNiwiutVVEY9z8rWaDSa2qSxKIsqkVLOlFL2lVL2bd68ed0LUM+zsjUajaY2aRQ+C2A/4Fa/lNZGW8NCJ8HTaDRNlMYyslgNxAohYoQQVuAGYFE9y6TRaDQnDY1iZCGldAghxgA/oEJnP5RSbq5nsTQajeakoVEoCwAp5WJgcX3LodFoNCcjjcUMpdFoNJp6RCsLjUaj0VSJVhYajUajqZJGkxvqWBBCHAZ2H+Nm4UBaLYhT3zTVfkHT7ZvuV+OiKfWrnZSywolqTVJZHA9CiDWeEmg1Zppqv6Dp9k33q3HRVPtVHm2G0mg0Gk2VaGWh0Wg0mirRyqKUmfUtQC3RVPsFTbdvul+Ni6barzJon4VGo9FoqkSPLDQajUZTJVpZaDQajaZKtLJA1fcWQmwVQmwXQjxa3/IcK0KIXUKIjUKIdUKINUZbmBDiRyFEkvEearQLIcTrRl83CCH61K/0pQghPhRCHBJCbHJrO+Z+CCFGG+snCSFG10df3PHQr2eEEPuNa7ZOCDHEbdljRr+2CiEudmtvUL9TIUQbIcSvQohEIcRmIcQDRntTuGae+tbor9txI6U8qV+oLLY7gFMAK7Ae6Frfch1jH3YB4eXaXgYeNT4/CrxkfB4CJAAC6A/8Vd/yu8l8HtAH2HS8/QDCgJ3Ge6jxObQB9usZ4OEK1u1q/Aa9gRjjt2luiL9TIAroY3wOBLYZ8jeFa+apb43+uh3vS48s6qi+dz0wDJhlfJ4FXOHWPlsq/gRChBBR9SDfUUgpVwAZ5ZqPtR8XAz9KKTOklJnAj8AltS58JXjolyeGAZ9LKYuklMnAdtRvtMH9TqWUKVLKf4zPOcAWVLnjpnDNPPXNE43muh0vWllUo753I0ACS4UQa4UQdxptkVLKFOPzQSDS+NzY+nus/WhM/RtjmGM+LDbV0Ej7JYSIBk4F/qKJXbNyfYMmdN2OBa0smgbnSCn7AHHAfUKI89wXSjVObvQx0k2lHwZvA+2B3kAKMLVepTkBhBABwJfAg1LKbPdljf2aVdC3JnPdjhWtLBpLfe9KkFLuN94PAV+hhr6pxeYl4/2QsXpj6++x9qNR9E9KmSqldEopXcB7qGsGjaxfQggv1M30UynlQqO5SVyzivrWVK7b8aCVRSOv7y2E8BdCBBZ/BgYDm1B9KI4qGQ18Y3xeBIwyIlP6A1luJoOGyLH24wdgsBAi1DARDDbaGhTl/ERXoq4ZqH7dIITwFkLEALHA3zTA36kQQgAfAFuklNPcFjX6a+apb03huh039e1hbwgvVJTGNlTUwuP1Lc8xyn4KKsJiPbC5WH6gGfAzkAT8BIQZ7QJ40+jrRqBvfffBrS9zUUN7O8q2e9vx9AO4FeVg3A7c0kD7NceQewPq5hHltv7jRr+2AnEN9XcKnIMyMW0A1hmvIU3kmnnqW6O/bsf70uk+NBqNRlMl2gyl0Wg0mirRykKj0Wg0VaKVhUaj0WiqRCsLjUaj0VSJVhYajUajqRKtLDSaE0QIMUAIcdYJ7iPXQ/vHQohrTmTfGk1NoJWFRnPiDABOSFnUFEIIS33LoGmaaGWh0XhACPG1kZxxc3GCRqM2wT9CiPVCiJ+NJHN3A2ON+gbnlh8NFI8ahBABxjb/CFV/5Kjso8bs5hlG/YOfgAi3ZbuEEOHG575CiGXG52eEEHOEEH+gJo1pNDWOfgrRaDxzq5QyQwjhC6wWQnyDygd0npQyWQgRZix/B8iVUr4CIIS4zcP+CoErpZTZxk3/TyHEIll2ZuyVQCdUfYRIIBH4sBqydkUllCw4rp5qNFWglYVG45n7hRBXGp/bAHcCK6SqV4CUsro1KooRwAtGVmAXKlV1JCqNdzHnAXOllE7ggBDil2rue5FWFJraRCsLjaYChBADgAuBM6WU+YbJZx3QuRqbOzBMvEIIE6pCGsAIoDlwmpTSLoTYBfgcg1gl+61gu7xj2I9Gc8xon4VGUzHBQKahKDqjyoD6AOcZWUURQoQZ6+agSm8Wsws4zfg8FPBy2+chQ1EMBNpVcNwVwPVCCLOR4XSgh/1efQJ902iOGa0sNJqKWQJYhBBbgMnAn8BhlClqoRBiPTDPWPdb4MpiBzfKr3G+sc6ZlD71fwr0FUJsBEYB/1Vw3K9Q2VoTgdnAKrdlzwKvCSHWAM4a66lGUw101lmNRqPRVIkeWWg0Go2mSrSy0Gg0Gk2VaGWh0Wg0mirRykKj0Wg0VaKVhUaj0WiqRCsLjUaj0VSJVhYajUajqZL/AwrUmuKYphgXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df_merged, y='actualdur', x='actualdur', color='black', label='actual')\n",
    "sns.regplot(data=df_nonpeak, y='gdur', x='actualdur', color='green', label='nonpeak', ci=None)\n",
    "fig = sns.regplot(data=df_peak, y='gdur_traffic', x='actualdur', color='red', label='peak', ci=None)\n",
    "fig.set_ylabel('gmaps duration')\n",
    "fig.set_title('Duration peak vs non-peak (in seconds)')\n",
    "fig.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GMaps tend to estimate the actual distance better than OSRM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1aeeb37a1f0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df_merged, y='actualdis', x='actualdis', color='black', label='actual')\n",
    "sns.scatterplot(data=df_merged, x='actualdis', y='osrmdis', color='red', label='osrm', alpha=0.5)\n",
    "#sns.regplot(data=df_merged, x='actualdis', y='osmnxdis', color='blue')\n",
    "fig = sns.scatterplot(data=df_merged, x='actualdis', y='gdis', color='green', label='gmaps', alpha=0.5)\n",
    "fig.set_xlabel('distance')\n",
    "fig.set_title('Distance gmaps vs osrm (in metres)')\n",
    "fig.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still hard to see the short distances, let's cut the long ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1aeeb28c160>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merged_cut = df_merged[df_merged.actualdis < 12000]\n",
    "sns.lineplot(data=df_merged_cut, y='actualdis', x='actualdis', color='black', label='actual')\n",
    "sns.scatterplot(data=df_merged_cut, x='actualdis', y='osrmdis', color='red', label='osrm', alpha=0.7)\n",
    "fig = sns.scatterplot(data=df_merged_cut, x='actualdis', y='gdis', color='green', label='gmaps', alpha=0.7)\n",
    "fig.set_xlabel('distance')\n",
    "fig.set_title('Distance gmaps vs osrm under 12km (in metres)')\n",
    "fig.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of the actual average speed during peak vs non-peak hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Speed (in kph)')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_peak['peak'] = 1\n",
    "df_nonpeak['peak'] = 0\n",
    "df_pnp = pd.concat([df_peak, df_nonpeak])\n",
    "fig = sns.boxplot(data=df_pnp, x='peak', y='actualspeed')\n",
    "fig.set_title('Speed (in kph)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which route estimates the speed better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_peak_melt = pd.melt(df_peak, id_vars=['trj_id'], value_vars=['gspeed', 'osrmspeed', 'actualspeed'])\n",
    "df_nonpeak_melt = pd.melt(df_nonpeak, id_vars=['trj_id'], value_vars=['gspeed', 'osrmspeed', 'actualspeed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'speed')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.boxplot(data=df_peak_melt, x='variable', y='value')\n",
    "fig.set_title('Speed for peak hours (in kph)')\n",
    "fig.set_ylabel('speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'speed')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.boxplot(data=df_nonpeak_melt, x='variable', y='value')\n",
    "fig.set_title('Speed for non-peak hours (in kph)')\n",
    "fig.set_ylabel('speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>trj_id</th>\n",
       "      <th>osmnxdis</th>\n",
       "      <th>osrmdis</th>\n",
       "      <th>gdis</th>\n",
       "      <th>actualdis</th>\n",
       "      <th>osrmdur</th>\n",
       "      <th>gdur</th>\n",
       "      <th>gdur_traffic</th>\n",
       "      <th>actualdur</th>\n",
       "      <th>osrmspeed</th>\n",
       "      <th>gspeed</th>\n",
       "      <th>actualspeed</th>\n",
       "      <th>starth</th>\n",
       "      <th>endh</th>\n",
       "      <th>day</th>\n",
       "      <th>rain</th>\n",
       "      <th>peak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40138.202532</td>\n",
       "      <td>8927.813505</td>\n",
       "      <td>6343.859494</td>\n",
       "      <td>6212.354430</td>\n",
       "      <td>6403.189966</td>\n",
       "      <td>380.741772</td>\n",
       "      <td>753.936709</td>\n",
       "      <td>1137.329114</td>\n",
       "      <td>1022.075949</td>\n",
       "      <td>58.487952</td>\n",
       "      <td>27.877792</td>\n",
       "      <td>22.376762</td>\n",
       "      <td>15.405063</td>\n",
       "      <td>15.645570</td>\n",
       "      <td>14.341772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25079.424123</td>\n",
       "      <td>7181.513771</td>\n",
       "      <td>4136.438542</td>\n",
       "      <td>4494.683543</td>\n",
       "      <td>4116.765113</td>\n",
       "      <td>225.306052</td>\n",
       "      <td>406.260695</td>\n",
       "      <td>696.529303</td>\n",
       "      <td>482.521443</td>\n",
       "      <td>10.905229</td>\n",
       "      <td>7.621620</td>\n",
       "      <td>7.404887</td>\n",
       "      <td>1.581293</td>\n",
       "      <td>1.609571</td>\n",
       "      <td>4.153732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2644.000000</td>\n",
       "      <td>162.013394</td>\n",
       "      <td>129.700000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>892.897540</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>23.902460</td>\n",
       "      <td>8.943498</td>\n",
       "      <td>2.411426</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18092.000000</td>\n",
       "      <td>3704.826201</td>\n",
       "      <td>3597.200000</td>\n",
       "      <td>3322.500000</td>\n",
       "      <td>3350.811525</td>\n",
       "      <td>250.650000</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>684.500000</td>\n",
       "      <td>708.000000</td>\n",
       "      <td>54.362972</td>\n",
       "      <td>23.197602</td>\n",
       "      <td>18.348578</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38573.000000</td>\n",
       "      <td>7371.409399</td>\n",
       "      <td>5361.900000</td>\n",
       "      <td>5248.000000</td>\n",
       "      <td>5666.114671</td>\n",
       "      <td>331.200000</td>\n",
       "      <td>703.000000</td>\n",
       "      <td>974.000000</td>\n",
       "      <td>944.000000</td>\n",
       "      <td>59.840816</td>\n",
       "      <td>26.874538</td>\n",
       "      <td>21.858544</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59762.000000</td>\n",
       "      <td>11947.367028</td>\n",
       "      <td>7682.350000</td>\n",
       "      <td>7878.000000</td>\n",
       "      <td>8074.656992</td>\n",
       "      <td>448.800000</td>\n",
       "      <td>934.000000</td>\n",
       "      <td>1402.000000</td>\n",
       "      <td>1293.000000</td>\n",
       "      <td>64.142846</td>\n",
       "      <td>30.718686</td>\n",
       "      <td>26.395157</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>83984.000000</td>\n",
       "      <td>40270.557137</td>\n",
       "      <td>22064.600000</td>\n",
       "      <td>27578.000000</td>\n",
       "      <td>23736.085825</td>\n",
       "      <td>1324.500000</td>\n",
       "      <td>2355.000000</td>\n",
       "      <td>3242.000000</td>\n",
       "      <td>2828.000000</td>\n",
       "      <td>85.501952</td>\n",
       "      <td>55.968750</td>\n",
       "      <td>46.641399</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             trj_id      osmnxdis       osrmdis          gdis     actualdis  \\\n",
       "count     79.000000     79.000000     79.000000     79.000000     79.000000   \n",
       "mean   40138.202532   8927.813505   6343.859494   6212.354430   6403.189966   \n",
       "std    25079.424123   7181.513771   4136.438542   4494.683543   4116.765113   \n",
       "min     2644.000000    162.013394    129.700000    130.000000    892.897540   \n",
       "25%    18092.000000   3704.826201   3597.200000   3322.500000   3350.811525   \n",
       "50%    38573.000000   7371.409399   5361.900000   5248.000000   5666.114671   \n",
       "75%    59762.000000  11947.367028   7682.350000   7878.000000   8074.656992   \n",
       "max    83984.000000  40270.557137  22064.600000  27578.000000  23736.085825   \n",
       "\n",
       "           osrmdur         gdur  gdur_traffic    actualdur  osrmspeed  \\\n",
       "count    79.000000    79.000000     79.000000    79.000000  79.000000   \n",
       "mean    380.741772   753.936709   1137.329114  1022.075949  58.487952   \n",
       "std     225.306052   406.260695    696.529303   482.521443  10.905229   \n",
       "min       5.700000    39.000000     42.000000   172.000000  23.902460   \n",
       "25%     250.650000   496.000000    684.500000   708.000000  54.362972   \n",
       "50%     331.200000   703.000000    974.000000   944.000000  59.840816   \n",
       "75%     448.800000   934.000000   1402.000000  1293.000000  64.142846   \n",
       "max    1324.500000  2355.000000   3242.000000  2828.000000  85.501952   \n",
       "\n",
       "          gspeed  actualspeed     starth       endh        day  rain  peak  \n",
       "count  79.000000    79.000000  79.000000  79.000000  79.000000  79.0  79.0  \n",
       "mean   27.877792    22.376762  15.405063  15.645570  14.341772   0.0   1.0  \n",
       "std     7.621620     7.404887   1.581293   1.609571   4.153732   0.0   0.0  \n",
       "min     8.943498     2.411426  13.000000  13.000000   8.000000   0.0   1.0  \n",
       "25%    23.197602    18.348578  14.000000  14.000000  10.000000   0.0   1.0  \n",
       "50%    26.874538    21.858544  15.000000  16.000000  15.000000   0.0   1.0  \n",
       "75%    30.718686    26.395157  17.000000  17.000000  18.000000   0.0   1.0  \n",
       "max    55.968750    46.641399  18.000000  18.000000  21.000000   0.0   1.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_peak.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trj_id</th>\n",
       "      <th>osmnxdis</th>\n",
       "      <th>osrmdis</th>\n",
       "      <th>gdis</th>\n",
       "      <th>actualdis</th>\n",
       "      <th>osrmdur</th>\n",
       "      <th>gdur</th>\n",
       "      <th>gdur_traffic</th>\n",
       "      <th>actualdur</th>\n",
       "      <th>osrmspeed</th>\n",
       "      <th>gspeed</th>\n",
       "      <th>actualspeed</th>\n",
       "      <th>starth</th>\n",
       "      <th>endh</th>\n",
       "      <th>day</th>\n",
       "      <th>rain</th>\n",
       "      <th>peak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.0</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44344.398374</td>\n",
       "      <td>9577.673769</td>\n",
       "      <td>6715.439024</td>\n",
       "      <td>6674.959350</td>\n",
       "      <td>6630.280003</td>\n",
       "      <td>401.366667</td>\n",
       "      <td>845.934959</td>\n",
       "      <td>1270.089431</td>\n",
       "      <td>967.024390</td>\n",
       "      <td>59.016042</td>\n",
       "      <td>28.078970</td>\n",
       "      <td>25.298081</td>\n",
       "      <td>14.032520</td>\n",
       "      <td>14.284553</td>\n",
       "      <td>13.991870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24808.124234</td>\n",
       "      <td>6276.693458</td>\n",
       "      <td>3219.050354</td>\n",
       "      <td>3292.321214</td>\n",
       "      <td>3100.600707</td>\n",
       "      <td>172.002203</td>\n",
       "      <td>360.100868</td>\n",
       "      <td>632.520459</td>\n",
       "      <td>373.355464</td>\n",
       "      <td>9.648409</td>\n",
       "      <td>5.851626</td>\n",
       "      <td>7.637819</td>\n",
       "      <td>5.910445</td>\n",
       "      <td>5.955464</td>\n",
       "      <td>4.230056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>999.000000</td>\n",
       "      <td>85.565615</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>862.000000</td>\n",
       "      <td>178.224623</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>16.243432</td>\n",
       "      <td>10.021505</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21200.500000</td>\n",
       "      <td>5460.823305</td>\n",
       "      <td>4670.050000</td>\n",
       "      <td>4567.500000</td>\n",
       "      <td>4626.638330</td>\n",
       "      <td>286.250000</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>717.500000</td>\n",
       "      <td>54.607976</td>\n",
       "      <td>24.670563</td>\n",
       "      <td>19.791640</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44366.000000</td>\n",
       "      <td>7971.422313</td>\n",
       "      <td>6131.300000</td>\n",
       "      <td>6147.000000</td>\n",
       "      <td>6066.755827</td>\n",
       "      <td>366.800000</td>\n",
       "      <td>799.000000</td>\n",
       "      <td>1167.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>59.270179</td>\n",
       "      <td>27.476680</td>\n",
       "      <td>24.789562</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>65065.000000</td>\n",
       "      <td>11925.159072</td>\n",
       "      <td>8024.550000</td>\n",
       "      <td>7941.000000</td>\n",
       "      <td>7819.825917</td>\n",
       "      <td>484.100000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>1516.000000</td>\n",
       "      <td>1210.000000</td>\n",
       "      <td>63.686521</td>\n",
       "      <td>29.654287</td>\n",
       "      <td>30.089747</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>83824.000000</td>\n",
       "      <td>31855.325662</td>\n",
       "      <td>21127.100000</td>\n",
       "      <td>21373.000000</td>\n",
       "      <td>16964.288031</td>\n",
       "      <td>1039.100000</td>\n",
       "      <td>2264.000000</td>\n",
       "      <td>4019.000000</td>\n",
       "      <td>2323.000000</td>\n",
       "      <td>83.914172</td>\n",
       "      <td>55.207509</td>\n",
       "      <td>43.523720</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             trj_id      osmnxdis       osrmdis          gdis     actualdis  \\\n",
       "count    123.000000    123.000000    123.000000    123.000000    123.000000   \n",
       "mean   44344.398374   9577.673769   6715.439024   6674.959350   6630.280003   \n",
       "std    24808.124234   6276.693458   3219.050354   3292.321214   3100.600707   \n",
       "min      999.000000     85.565615     27.000000    862.000000    178.224623   \n",
       "25%    21200.500000   5460.823305   4670.050000   4567.500000   4626.638330   \n",
       "50%    44366.000000   7971.422313   6131.300000   6147.000000   6066.755827   \n",
       "75%    65065.000000  11925.159072   8024.550000   7941.000000   7819.825917   \n",
       "max    83824.000000  31855.325662  21127.100000  21373.000000  16964.288031   \n",
       "\n",
       "           osrmdur         gdur  gdur_traffic    actualdur   osrmspeed  \\\n",
       "count   123.000000   123.000000    123.000000   123.000000  123.000000   \n",
       "mean    401.366667   845.934959   1270.089431   967.024390   59.016042   \n",
       "std     172.002203   360.100868    632.520459   373.355464    9.648409   \n",
       "min       6.000000   121.000000    157.000000    23.000000   16.200000   \n",
       "25%     286.250000   630.000000    846.000000   717.500000   54.607976   \n",
       "50%     366.800000   799.000000   1167.000000   920.000000   59.270179   \n",
       "75%     484.100000   991.000000   1516.000000  1210.000000   63.686521   \n",
       "max    1039.100000  2264.000000   4019.000000  2323.000000   83.914172   \n",
       "\n",
       "           gspeed  actualspeed      starth        endh         day   rain  \\\n",
       "count  123.000000   123.000000  123.000000  123.000000  123.000000  123.0   \n",
       "mean    28.078970    25.298081   14.032520   14.284553   13.991870    0.0   \n",
       "std      5.851626     7.637819    5.910445    5.955464    4.230056    0.0   \n",
       "min     16.243432    10.021505    0.000000    0.000000    8.000000    0.0   \n",
       "25%     24.670563    19.791640   10.000000   10.000000   10.000000    0.0   \n",
       "50%     27.476680    24.789562   12.000000   12.000000   14.000000    0.0   \n",
       "75%     29.654287    30.089747   20.000000   20.000000   18.000000    0.0   \n",
       "max     55.207509    43.523720   23.000000   23.000000   22.000000    0.0   \n",
       "\n",
       "        peak  \n",
       "count  123.0  \n",
       "mean     0.0  \n",
       "std      0.0  \n",
       "min      0.0  \n",
       "25%      0.0  \n",
       "50%      0.0  \n",
       "75%      0.0  \n",
       "max      0.0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nonpeak.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Terry's route classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_distance_approx(trj_id):\n",
    "    temp = df[df.trj_id == str(trj_id)]\n",
    "    temp2 = (df_merged[df_merged.trj_id == trj_id]).iloc[0]\n",
    "    \n",
    "    osrm_dist_list = temp.apply(lambda x: temp2.geometry_osrm.distance(Point(x.rawlng, x.rawlat)), axis=1)\n",
    "    osrm_count = len(list(filter(lambda x: x <= 10**(-4), osrm_dist_list)))\n",
    "\n",
    "    gmaps_dist_list = temp.apply(lambda x: temp2.geometry_gmaps.distance(Point(x.rawlng, x.rawlat)), axis=1)\n",
    "    gmaps_count = len(list(filter(lambda x: x <= 10**(-4), gmaps_dist_list)))\n",
    "\n",
    "    return ( trj_id, (osrm_count/len(temp))*100 , (gmaps_count/len(temp))*100 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(999, 0.0, 51.515151515151516)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_distance_approx(999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 202/202 [00:07<00:00, 26.07it/s]\n"
     ]
    }
   ],
   "source": [
    "results = list(map(lambda x: find_distance_approx(x), tqdm(df_merged.trj_id)))\n",
    "temp = []\n",
    "for i in results:\n",
    "    if i[1] <= 80 and i[2] <= 80:\n",
    "        temp.append((i[0], \"Others\"))\n",
    "    else:\n",
    "        if i[1] >= i[2]:\n",
    "            temp.append((i[0], \"OSRM\"))\n",
    "        else:\n",
    "            temp.append((i[0], \"GMAPS\"))\n",
    "\n",
    "results = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame(results, columns = [\"trj_id\", \"same_route\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trj_id</th>\n",
       "      <th>osmnxdis</th>\n",
       "      <th>osrmdis</th>\n",
       "      <th>gdis</th>\n",
       "      <th>actualdis</th>\n",
       "      <th>osrmdur</th>\n",
       "      <th>gdur</th>\n",
       "      <th>gdur_traffic</th>\n",
       "      <th>actualdur</th>\n",
       "      <th>osrmspeed</th>\n",
       "      <th>...</th>\n",
       "      <th>actualspeed</th>\n",
       "      <th>starth</th>\n",
       "      <th>endh</th>\n",
       "      <th>day</th>\n",
       "      <th>geometry_osmnx</th>\n",
       "      <th>geometry_osrm</th>\n",
       "      <th>geometry_gmaps</th>\n",
       "      <th>geometry_actual</th>\n",
       "      <th>rain</th>\n",
       "      <th>same_route</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10186</td>\n",
       "      <td>16967.311474</td>\n",
       "      <td>10433.0</td>\n",
       "      <td>7849</td>\n",
       "      <td>7856.576317</td>\n",
       "      <td>603.6</td>\n",
       "      <td>979</td>\n",
       "      <td>1366</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>62.224652</td>\n",
       "      <td>...</td>\n",
       "      <td>20.705472</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>LINESTRING (106.79767 -6.19545, 106.79770 -6.1...</td>\n",
       "      <td>LINESTRING (106.79720 -6.19519, 106.79655 -6.1...</td>\n",
       "      <td>LINESTRING (106.78732 -6.17077, 106.78751 -6.1...</td>\n",
       "      <td>LINESTRING (106.7872467041016 -6.1707553863525...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10255</td>\n",
       "      <td>5392.254356</td>\n",
       "      <td>4701.1</td>\n",
       "      <td>4044</td>\n",
       "      <td>4032.848615</td>\n",
       "      <td>302.6</td>\n",
       "      <td>448</td>\n",
       "      <td>609</td>\n",
       "      <td>465.0</td>\n",
       "      <td>55.928486</td>\n",
       "      <td>...</td>\n",
       "      <td>31.222054</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>LINESTRING (106.78733 -6.17074, 106.78716 -6.1...</td>\n",
       "      <td>LINESTRING (106.78725 -6.17073, 106.78683 -6.1...</td>\n",
       "      <td>LINESTRING (106.76257 -6.19032, 106.76537 -6.1...</td>\n",
       "      <td>LINESTRING (106.762565612793 -6.19029760360717...</td>\n",
       "      <td>0</td>\n",
       "      <td>GMAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10286</td>\n",
       "      <td>7660.991620</td>\n",
       "      <td>4504.2</td>\n",
       "      <td>4558</td>\n",
       "      <td>4592.783040</td>\n",
       "      <td>370.9</td>\n",
       "      <td>677</td>\n",
       "      <td>925</td>\n",
       "      <td>988.0</td>\n",
       "      <td>43.718307</td>\n",
       "      <td>...</td>\n",
       "      <td>16.734837</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>LINESTRING (106.76273 -6.19040, 106.76213 -6.1...</td>\n",
       "      <td>LINESTRING (106.76257 -6.19034, 106.76667 -6.1...</td>\n",
       "      <td>LINESTRING (106.81600 -6.15129, 106.81701 -6.1...</td>\n",
       "      <td>LINESTRING (106.8159942626953 -6.1513204574584...</td>\n",
       "      <td>0</td>\n",
       "      <td>GMAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10620</td>\n",
       "      <td>5171.606425</td>\n",
       "      <td>1610.8</td>\n",
       "      <td>2132</td>\n",
       "      <td>2496.910897</td>\n",
       "      <td>233.2</td>\n",
       "      <td>415</td>\n",
       "      <td>554</td>\n",
       "      <td>492.0</td>\n",
       "      <td>24.866552</td>\n",
       "      <td>...</td>\n",
       "      <td>18.270080</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>LINESTRING (106.79713 -6.25908, 106.79716 -6.2...</td>\n",
       "      <td>LINESTRING (106.79689 -6.25918, 106.79666 -6.2...</td>\n",
       "      <td>LINESTRING (106.78765 -6.16782, 106.78764 -6.1...</td>\n",
       "      <td>LINESTRING (106.7895126342773 -6.1735401153564...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10995</td>\n",
       "      <td>4850.560287</td>\n",
       "      <td>5005.2</td>\n",
       "      <td>5420</td>\n",
       "      <td>5407.879717</td>\n",
       "      <td>263.9</td>\n",
       "      <td>641</td>\n",
       "      <td>790</td>\n",
       "      <td>585.0</td>\n",
       "      <td>68.278590</td>\n",
       "      <td>...</td>\n",
       "      <td>33.279260</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>LINESTRING (106.81571 -6.15125, 106.81699 -6.1...</td>\n",
       "      <td>LINESTRING (106.81600 -6.15126, 106.81699 -6.1...</td>\n",
       "      <td>LINESTRING (106.78799 -6.14350, 106.78688 -6.1...</td>\n",
       "      <td>LINESTRING (106.7879867553711 -6.1435003280639...</td>\n",
       "      <td>0</td>\n",
       "      <td>GMAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>9173</td>\n",
       "      <td>8257.200880</td>\n",
       "      <td>6900.9</td>\n",
       "      <td>6693</td>\n",
       "      <td>6565.544880</td>\n",
       "      <td>450.7</td>\n",
       "      <td>836</td>\n",
       "      <td>1116</td>\n",
       "      <td>871.0</td>\n",
       "      <td>55.121456</td>\n",
       "      <td>...</td>\n",
       "      <td>27.136580</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>LINESTRING (106.84064 -6.16307, 106.84087 -6.1...</td>\n",
       "      <td>LINESTRING (106.84071 -6.16296, 106.84912 -6.1...</td>\n",
       "      <td>LINESTRING (106.81794 -6.21027, 106.81794 -6.2...</td>\n",
       "      <td>LINESTRING (106.8179168701172 -6.2102694511413...</td>\n",
       "      <td>0</td>\n",
       "      <td>GMAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>9245</td>\n",
       "      <td>12393.378887</td>\n",
       "      <td>8705.4</td>\n",
       "      <td>7910</td>\n",
       "      <td>7748.628068</td>\n",
       "      <td>494.0</td>\n",
       "      <td>1273</td>\n",
       "      <td>1844</td>\n",
       "      <td>826.0</td>\n",
       "      <td>63.440162</td>\n",
       "      <td>...</td>\n",
       "      <td>33.771260</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>LINESTRING (106.89196 -6.11015, 106.89195 -6.1...</td>\n",
       "      <td>LINESTRING (106.89169 -6.11014, 106.89000 -6.1...</td>\n",
       "      <td>LINESTRING (106.81390 -6.21967, 106.81385 -6.2...</td>\n",
       "      <td>LINESTRING (106.8138732910156 -6.2196588516235...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>9588</td>\n",
       "      <td>12671.641743</td>\n",
       "      <td>10949.0</td>\n",
       "      <td>10945</td>\n",
       "      <td>10934.816902</td>\n",
       "      <td>461.0</td>\n",
       "      <td>704</td>\n",
       "      <td>2647</td>\n",
       "      <td>844.0</td>\n",
       "      <td>85.501952</td>\n",
       "      <td>...</td>\n",
       "      <td>46.641399</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>LINESTRING (106.78835 -6.17170, 106.78824 -6.1...</td>\n",
       "      <td>LINESTRING (106.78849 -6.17197, 106.78772 -6.1...</td>\n",
       "      <td>LINESTRING (106.78834 -6.16853, 106.78835 -6.1...</td>\n",
       "      <td>LINESTRING (106.7883834838867 -6.1685347557067...</td>\n",
       "      <td>0</td>\n",
       "      <td>GMAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>9636</td>\n",
       "      <td>8556.318186</td>\n",
       "      <td>5685.5</td>\n",
       "      <td>5677</td>\n",
       "      <td>5870.274020</td>\n",
       "      <td>285.5</td>\n",
       "      <td>761</td>\n",
       "      <td>1197</td>\n",
       "      <td>949.0</td>\n",
       "      <td>71.691068</td>\n",
       "      <td>...</td>\n",
       "      <td>22.268690</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>LINESTRING (106.78082 -6.24161, 106.78083 -6.2...</td>\n",
       "      <td>LINESTRING (106.78082 -6.24164, 106.78083 -6.2...</td>\n",
       "      <td>LINESTRING (106.78796 -6.14292, 106.78790 -6.1...</td>\n",
       "      <td>LINESTRING (106.7879638671875 -6.1429247856140...</td>\n",
       "      <td>0</td>\n",
       "      <td>GMAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>999</td>\n",
       "      <td>6669.499106</td>\n",
       "      <td>4277.6</td>\n",
       "      <td>4915</td>\n",
       "      <td>4239.316655</td>\n",
       "      <td>271.5</td>\n",
       "      <td>597</td>\n",
       "      <td>973</td>\n",
       "      <td>762.0</td>\n",
       "      <td>56.719558</td>\n",
       "      <td>...</td>\n",
       "      <td>20.028268</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
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       "      <td>LINESTRING (106.83882 -6.22531, 106.83790 -6.2...</td>\n",
       "      <td>LINESTRING (106.83222 -6.22088, 106.83253 -6.2...</td>\n",
       "      <td>LINESTRING (106.8322143554688 -6.2208857536315...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trj_id      osmnxdis  osrmdis   gdis     actualdis  osrmdur  gdur  \\\n",
       "0     10186  16967.311474  10433.0   7849   7856.576317    603.6   979   \n",
       "1     10255   5392.254356   4701.1   4044   4032.848615    302.6   448   \n",
       "2     10286   7660.991620   4504.2   4558   4592.783040    370.9   677   \n",
       "3     10620   5171.606425   1610.8   2132   2496.910897    233.2   415   \n",
       "4     10995   4850.560287   5005.2   5420   5407.879717    263.9   641   \n",
       "..      ...           ...      ...    ...           ...      ...   ...   \n",
       "197    9173   8257.200880   6900.9   6693   6565.544880    450.7   836   \n",
       "198    9245  12393.378887   8705.4   7910   7748.628068    494.0  1273   \n",
       "199    9588  12671.641743  10949.0  10945  10934.816902    461.0   704   \n",
       "200    9636   8556.318186   5685.5   5677   5870.274020    285.5   761   \n",
       "201     999   6669.499106   4277.6   4915   4239.316655    271.5   597   \n",
       "\n",
       "     gdur_traffic  actualdur  osrmspeed  ...  actualspeed  starth  endh  day  \\\n",
       "0            1366     1366.0  62.224652  ...    20.705472      16    17   18   \n",
       "1             609      465.0  55.928486  ...    31.222054      21    22    9   \n",
       "2             925      988.0  43.718307  ...    16.734837      17    18   11   \n",
       "3             554      492.0  24.866552  ...    18.270080      15    15   15   \n",
       "4             790      585.0  68.278590  ...    33.279260      22    22   13   \n",
       "..            ...        ...        ...  ...          ...     ...   ...  ...   \n",
       "197          1116      871.0  55.121456  ...    27.136580      18    18   16   \n",
       "198          1844      826.0  63.440162  ...    33.771260       6     6   10   \n",
       "199          2647      844.0  85.501952  ...    46.641399      13    13    9   \n",
       "200          1197      949.0  71.691068  ...    22.268690      17    17   19   \n",
       "201           973      762.0  56.719558  ...    20.028268       9     9    8   \n",
       "\n",
       "                                        geometry_osmnx  \\\n",
       "0    LINESTRING (106.79767 -6.19545, 106.79770 -6.1...   \n",
       "1    LINESTRING (106.78733 -6.17074, 106.78716 -6.1...   \n",
       "2    LINESTRING (106.76273 -6.19040, 106.76213 -6.1...   \n",
       "3    LINESTRING (106.79713 -6.25908, 106.79716 -6.2...   \n",
       "4    LINESTRING (106.81571 -6.15125, 106.81699 -6.1...   \n",
       "..                                                 ...   \n",
       "197  LINESTRING (106.84064 -6.16307, 106.84087 -6.1...   \n",
       "198  LINESTRING (106.89196 -6.11015, 106.89195 -6.1...   \n",
       "199  LINESTRING (106.78835 -6.17170, 106.78824 -6.1...   \n",
       "200  LINESTRING (106.78082 -6.24161, 106.78083 -6.2...   \n",
       "201  LINESTRING (106.83897 -6.22526, 106.83850 -6.2...   \n",
       "\n",
       "                                         geometry_osrm  \\\n",
       "0    LINESTRING (106.79720 -6.19519, 106.79655 -6.1...   \n",
       "1    LINESTRING (106.78725 -6.17073, 106.78683 -6.1...   \n",
       "2    LINESTRING (106.76257 -6.19034, 106.76667 -6.1...   \n",
       "3    LINESTRING (106.79689 -6.25918, 106.79666 -6.2...   \n",
       "4    LINESTRING (106.81600 -6.15126, 106.81699 -6.1...   \n",
       "..                                                 ...   \n",
       "197  LINESTRING (106.84071 -6.16296, 106.84912 -6.1...   \n",
       "198  LINESTRING (106.89169 -6.11014, 106.89000 -6.1...   \n",
       "199  LINESTRING (106.78849 -6.17197, 106.78772 -6.1...   \n",
       "200  LINESTRING (106.78082 -6.24164, 106.78083 -6.2...   \n",
       "201  LINESTRING (106.83882 -6.22531, 106.83790 -6.2...   \n",
       "\n",
       "                                        geometry_gmaps  \\\n",
       "0    LINESTRING (106.78732 -6.17077, 106.78751 -6.1...   \n",
       "1    LINESTRING (106.76257 -6.19032, 106.76537 -6.1...   \n",
       "2    LINESTRING (106.81600 -6.15129, 106.81701 -6.1...   \n",
       "3    LINESTRING (106.78765 -6.16782, 106.78764 -6.1...   \n",
       "4    LINESTRING (106.78799 -6.14350, 106.78688 -6.1...   \n",
       "..                                                 ...   \n",
       "197  LINESTRING (106.81794 -6.21027, 106.81794 -6.2...   \n",
       "198  LINESTRING (106.81390 -6.21967, 106.81385 -6.2...   \n",
       "199  LINESTRING (106.78834 -6.16853, 106.78835 -6.1...   \n",
       "200  LINESTRING (106.78796 -6.14292, 106.78790 -6.1...   \n",
       "201  LINESTRING (106.83222 -6.22088, 106.83253 -6.2...   \n",
       "\n",
       "                                       geometry_actual rain  same_route  \n",
       "0    LINESTRING (106.7872467041016 -6.1707553863525...    0      Others  \n",
       "1    LINESTRING (106.762565612793 -6.19029760360717...    0       GMAPS  \n",
       "2    LINESTRING (106.8159942626953 -6.1513204574584...    0       GMAPS  \n",
       "3    LINESTRING (106.7895126342773 -6.1735401153564...    0      Others  \n",
       "4    LINESTRING (106.7879867553711 -6.1435003280639...    0       GMAPS  \n",
       "..                                                 ...  ...         ...  \n",
       "197  LINESTRING (106.8179168701172 -6.2102694511413...    0       GMAPS  \n",
       "198  LINESTRING (106.8138732910156 -6.2196588516235...    0      Others  \n",
       "199  LINESTRING (106.7883834838867 -6.1685347557067...    0       GMAPS  \n",
       "200  LINESTRING (106.7879638671875 -6.1429247856140...    0       GMAPS  \n",
       "201  LINESTRING (106.8322143554688 -6.2208857536315...    0      Others  \n",
       "\n",
       "[202 rows x 21 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_2 = df_merged.copy()\n",
    "merged_df_2 = pd.merge(df_merged, results, on=\"trj_id\")\n",
    "merged_df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trj_id</th>\n",
       "      <th>osmnxdis</th>\n",
       "      <th>osrmdis</th>\n",
       "      <th>gdis</th>\n",
       "      <th>actualdis</th>\n",
       "      <th>osrmdur</th>\n",
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       "      <td>LINESTRING (106.79720 -6.19519, 106.79655 -6.1...</td>\n",
       "      <td>LINESTRING (106.78732 -6.17077, 106.78751 -6.1...</td>\n",
       "      <td>LINESTRING (106.7872467041016 -6.1707553863525...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10620</td>\n",
       "      <td>5171.606425</td>\n",
       "      <td>1610.8</td>\n",
       "      <td>2132</td>\n",
       "      <td>2496.910897</td>\n",
       "      <td>233.2</td>\n",
       "      <td>415</td>\n",
       "      <td>554</td>\n",
       "      <td>492.0</td>\n",
       "      <td>24.866552</td>\n",
       "      <td>...</td>\n",
       "      <td>18.270080</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>LINESTRING (106.79713 -6.25908, 106.79716 -6.2...</td>\n",
       "      <td>LINESTRING (106.79689 -6.25918, 106.79666 -6.2...</td>\n",
       "      <td>LINESTRING (106.78765 -6.16782, 106.78764 -6.1...</td>\n",
       "      <td>LINESTRING (106.7895126342773 -6.1735401153564...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11293</td>\n",
       "      <td>14356.017863</td>\n",
       "      <td>7469.5</td>\n",
       "      <td>6737</td>\n",
       "      <td>7696.080962</td>\n",
       "      <td>446.2</td>\n",
       "      <td>919</td>\n",
       "      <td>1366</td>\n",
       "      <td>896.0</td>\n",
       "      <td>60.264904</td>\n",
       "      <td>...</td>\n",
       "      <td>30.921754</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>LINESTRING (106.78770 -6.16796, 106.78844 -6.1...</td>\n",
       "      <td>LINESTRING (106.78766 -6.16782, 106.78766 -6.1...</td>\n",
       "      <td>LINESTRING (106.81915 -6.15712, 106.81867 -6.1...</td>\n",
       "      <td>LINESTRING (106.8191528320312 -6.1571216583251...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12433</td>\n",
       "      <td>9629.337914</td>\n",
       "      <td>6914.1</td>\n",
       "      <td>6916</td>\n",
       "      <td>7106.740004</td>\n",
       "      <td>489.2</td>\n",
       "      <td>983</td>\n",
       "      <td>1166</td>\n",
       "      <td>890.0</td>\n",
       "      <td>50.880540</td>\n",
       "      <td>...</td>\n",
       "      <td>28.746364</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>LINESTRING (106.82253 -6.17335, 106.82176 -6.1...</td>\n",
       "      <td>LINESTRING (106.82253 -6.17301, 106.82253 -6.1...</td>\n",
       "      <td>LINESTRING (106.86677 -6.13845, 106.86410 -6.1...</td>\n",
       "      <td>LINESTRING (106.8667755126953 -6.1384863853454...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13434</td>\n",
       "      <td>13289.172600</td>\n",
       "      <td>8593.7</td>\n",
       "      <td>8608</td>\n",
       "      <td>8493.836200</td>\n",
       "      <td>479.7</td>\n",
       "      <td>1025</td>\n",
       "      <td>1855</td>\n",
       "      <td>893.0</td>\n",
       "      <td>64.493058</td>\n",
       "      <td>...</td>\n",
       "      <td>34.241669</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>LINESTRING (106.78806 -6.14346, 106.79036 -6.1...</td>\n",
       "      <td>LINESTRING (106.78798 -6.14347, 106.78629 -6.1...</td>\n",
       "      <td>LINESTRING (106.82034 -6.19467, 106.82022 -6.1...</td>\n",
       "      <td>LINESTRING (106.8203125 -6.194704055786133, 10...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   trj_id      osmnxdis  osrmdis  gdis    actualdis  osrmdur  gdur  \\\n",
       "0   10186  16967.311474  10433.0  7849  7856.576317    603.6   979   \n",
       "3   10620   5171.606425   1610.8  2132  2496.910897    233.2   415   \n",
       "5   11293  14356.017863   7469.5  6737  7696.080962    446.2   919   \n",
       "7   12433   9629.337914   6914.1  6916  7106.740004    489.2   983   \n",
       "9   13434  13289.172600   8593.7  8608  8493.836200    479.7  1025   \n",
       "\n",
       "   gdur_traffic  actualdur  osrmspeed  ...  actualspeed  starth  endh  day  \\\n",
       "0          1366     1366.0  62.224652  ...    20.705472      16    17   18   \n",
       "3           554      492.0  24.866552  ...    18.270080      15    15   15   \n",
       "5          1366      896.0  60.264904  ...    30.921754      12    12   14   \n",
       "7          1166      890.0  50.880540  ...    28.746364      18    19   18   \n",
       "9          1855      893.0  64.493058  ...    34.241669       1     1   11   \n",
       "\n",
       "                                      geometry_osmnx  \\\n",
       "0  LINESTRING (106.79767 -6.19545, 106.79770 -6.1...   \n",
       "3  LINESTRING (106.79713 -6.25908, 106.79716 -6.2...   \n",
       "5  LINESTRING (106.78770 -6.16796, 106.78844 -6.1...   \n",
       "7  LINESTRING (106.82253 -6.17335, 106.82176 -6.1...   \n",
       "9  LINESTRING (106.78806 -6.14346, 106.79036 -6.1...   \n",
       "\n",
       "                                       geometry_osrm  \\\n",
       "0  LINESTRING (106.79720 -6.19519, 106.79655 -6.1...   \n",
       "3  LINESTRING (106.79689 -6.25918, 106.79666 -6.2...   \n",
       "5  LINESTRING (106.78766 -6.16782, 106.78766 -6.1...   \n",
       "7  LINESTRING (106.82253 -6.17301, 106.82253 -6.1...   \n",
       "9  LINESTRING (106.78798 -6.14347, 106.78629 -6.1...   \n",
       "\n",
       "                                      geometry_gmaps  \\\n",
       "0  LINESTRING (106.78732 -6.17077, 106.78751 -6.1...   \n",
       "3  LINESTRING (106.78765 -6.16782, 106.78764 -6.1...   \n",
       "5  LINESTRING (106.81915 -6.15712, 106.81867 -6.1...   \n",
       "7  LINESTRING (106.86677 -6.13845, 106.86410 -6.1...   \n",
       "9  LINESTRING (106.82034 -6.19467, 106.82022 -6.1...   \n",
       "\n",
       "                                     geometry_actual rain  same_route  \n",
       "0  LINESTRING (106.7872467041016 -6.1707553863525...    0      Others  \n",
       "3  LINESTRING (106.7895126342773 -6.1735401153564...    0      Others  \n",
       "5  LINESTRING (106.8191528320312 -6.1571216583251...    0      Others  \n",
       "7  LINESTRING (106.8667755126953 -6.1384863853454...    0      Others  \n",
       "9  LINESTRING (106.8203125 -6.194704055786133, 10...    0      Others  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_2[merged_df_2.same_route == 'Others'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trj_id</th>\n",
       "      <th>osmnxdis</th>\n",
       "      <th>osrmdis</th>\n",
       "      <th>gdis</th>\n",
       "      <th>actualdis</th>\n",
       "      <th>osrmdur</th>\n",
       "      <th>gdur</th>\n",
       "      <th>gdur_traffic</th>\n",
       "      <th>actualdur</th>\n",
       "      <th>osrmspeed</th>\n",
       "      <th>...</th>\n",
       "      <th>starth</th>\n",
       "      <th>endh</th>\n",
       "      <th>day</th>\n",
       "      <th>geometry_osmnx</th>\n",
       "      <th>geometry_osrm</th>\n",
       "      <th>geometry_gmaps</th>\n",
       "      <th>geometry_actual</th>\n",
       "      <th>rain</th>\n",
       "      <th>same_route</th>\n",
       "      <th>peak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10186</td>\n",
       "      <td>16967.311474</td>\n",
       "      <td>10433.0</td>\n",
       "      <td>7849</td>\n",
       "      <td>7856.576317</td>\n",
       "      <td>603.6</td>\n",
       "      <td>979</td>\n",
       "      <td>1366</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>62.224652</td>\n",
       "      <td>...</td>\n",
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       "      <td>17</td>\n",
       "      <td>18</td>\n",
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       "      <td>LINESTRING (106.7872467041016 -6.1707553863525...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10255</td>\n",
       "      <td>5392.254356</td>\n",
       "      <td>4701.1</td>\n",
       "      <td>4044</td>\n",
       "      <td>4032.848615</td>\n",
       "      <td>302.6</td>\n",
       "      <td>448</td>\n",
       "      <td>609</td>\n",
       "      <td>465.0</td>\n",
       "      <td>55.928486</td>\n",
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       "      <td>LINESTRING (106.76257 -6.19032, 106.76537 -6.1...</td>\n",
       "      <td>LINESTRING (106.762565612793 -6.19029760360717...</td>\n",
       "      <td>0</td>\n",
       "      <td>GMAPS</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10286</td>\n",
       "      <td>7660.991620</td>\n",
       "      <td>4504.2</td>\n",
       "      <td>4558</td>\n",
       "      <td>4592.783040</td>\n",
       "      <td>370.9</td>\n",
       "      <td>677</td>\n",
       "      <td>925</td>\n",
       "      <td>988.0</td>\n",
       "      <td>43.718307</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>LINESTRING (106.76273 -6.19040, 106.76213 -6.1...</td>\n",
       "      <td>LINESTRING (106.76257 -6.19034, 106.76667 -6.1...</td>\n",
       "      <td>LINESTRING (106.81600 -6.15129, 106.81701 -6.1...</td>\n",
       "      <td>LINESTRING (106.8159942626953 -6.1513204574584...</td>\n",
       "      <td>0</td>\n",
       "      <td>GMAPS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10620</td>\n",
       "      <td>5171.606425</td>\n",
       "      <td>1610.8</td>\n",
       "      <td>2132</td>\n",
       "      <td>2496.910897</td>\n",
       "      <td>233.2</td>\n",
       "      <td>415</td>\n",
       "      <td>554</td>\n",
       "      <td>492.0</td>\n",
       "      <td>24.866552</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>LINESTRING (106.79713 -6.25908, 106.79716 -6.2...</td>\n",
       "      <td>LINESTRING (106.79689 -6.25918, 106.79666 -6.2...</td>\n",
       "      <td>LINESTRING (106.78765 -6.16782, 106.78764 -6.1...</td>\n",
       "      <td>LINESTRING (106.7895126342773 -6.1735401153564...</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>4850.560287</td>\n",
       "      <td>5005.2</td>\n",
       "      <td>5420</td>\n",
       "      <td>5407.879717</td>\n",
       "      <td>263.9</td>\n",
       "      <td>641</td>\n",
       "      <td>790</td>\n",
       "      <td>585.0</td>\n",
       "      <td>68.278590</td>\n",
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       "      <td>LINESTRING (106.81600 -6.15126, 106.81699 -6.1...</td>\n",
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       "      <td>LINESTRING (106.7879867553711 -6.1435003280639...</td>\n",
       "      <td>0</td>\n",
       "      <td>GMAPS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   trj_id      osmnxdis  osrmdis  gdis    actualdis  osrmdur  gdur  \\\n",
       "0   10186  16967.311474  10433.0  7849  7856.576317    603.6   979   \n",
       "1   10255   5392.254356   4701.1  4044  4032.848615    302.6   448   \n",
       "2   10286   7660.991620   4504.2  4558  4592.783040    370.9   677   \n",
       "3   10620   5171.606425   1610.8  2132  2496.910897    233.2   415   \n",
       "4   10995   4850.560287   5005.2  5420  5407.879717    263.9   641   \n",
       "\n",
       "   gdur_traffic  actualdur  osrmspeed  ...  starth  endh  day  \\\n",
       "0          1366     1366.0  62.224652  ...      16    17   18   \n",
       "1           609      465.0  55.928486  ...      21    22    9   \n",
       "2           925      988.0  43.718307  ...      17    18   11   \n",
       "3           554      492.0  24.866552  ...      15    15   15   \n",
       "4           790      585.0  68.278590  ...      22    22   13   \n",
       "\n",
       "                                      geometry_osmnx  \\\n",
       "0  LINESTRING (106.79767 -6.19545, 106.79770 -6.1...   \n",
       "1  LINESTRING (106.78733 -6.17074, 106.78716 -6.1...   \n",
       "2  LINESTRING (106.76273 -6.19040, 106.76213 -6.1...   \n",
       "3  LINESTRING (106.79713 -6.25908, 106.79716 -6.2...   \n",
       "4  LINESTRING (106.81571 -6.15125, 106.81699 -6.1...   \n",
       "\n",
       "                                       geometry_osrm  \\\n",
       "0  LINESTRING (106.79720 -6.19519, 106.79655 -6.1...   \n",
       "1  LINESTRING (106.78725 -6.17073, 106.78683 -6.1...   \n",
       "2  LINESTRING (106.76257 -6.19034, 106.76667 -6.1...   \n",
       "3  LINESTRING (106.79689 -6.25918, 106.79666 -6.2...   \n",
       "4  LINESTRING (106.81600 -6.15126, 106.81699 -6.1...   \n",
       "\n",
       "                                      geometry_gmaps  \\\n",
       "0  LINESTRING (106.78732 -6.17077, 106.78751 -6.1...   \n",
       "1  LINESTRING (106.76257 -6.19032, 106.76537 -6.1...   \n",
       "2  LINESTRING (106.81600 -6.15129, 106.81701 -6.1...   \n",
       "3  LINESTRING (106.78765 -6.16782, 106.78764 -6.1...   \n",
       "4  LINESTRING (106.78799 -6.14350, 106.78688 -6.1...   \n",
       "\n",
       "                                     geometry_actual rain same_route  peak  \n",
       "0  LINESTRING (106.7872467041016 -6.1707553863525...    0     Others     1  \n",
       "1  LINESTRING (106.762565612793 -6.19029760360717...    0      GMAPS     0  \n",
       "2  LINESTRING (106.8159942626953 -6.1513204574584...    0      GMAPS     1  \n",
       "3  LINESTRING (106.7895126342773 -6.1735401153564...    0     Others     1  \n",
       "4  LINESTRING (106.7879867553711 -6.1435003280639...    0      GMAPS     0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_2['peak'] = merged_df_2.apply(lambda x: int((13 <= x.starth) & (x.endh <= 18)), axis=1)\n",
    "merged_df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No one obeys OSRM :)\n",
    "df_obey_gmaps = merged_df_2[merged_df_2.same_route == 'GMAPS']\n",
    "df_obey_others = merged_df_2[merged_df_2.same_route == 'Others']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((75, 22), (127, 22))"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obey_gmaps.shape, df_obey_others.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_2.to_csv('data/merged_routes.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Speed distribution between routes that obey GMaps VS their own way\n",
    "\n",
    "For peak hours, there is actually an outlier which might be caused by the usage of the highway/expressway, or due to the fact that the peak hours affect the traffic so the speed becomes extremely slow.\n",
    "\n",
    "The reason why there isn't any high outlier for non-peak hours could be that not many trajectories go through the expressway due to safety (and/or it's still the night)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Non-peak hours')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.boxplot(data=merged_df_2[merged_df_2.peak == 0], x='same_route', y='actualspeed')\n",
    "fig.set_title('Non-peak hours')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Peak hours (1-6 PM)')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.boxplot(data=merged_df_2[merged_df_2.peak == 1], x='same_route', y='actualspeed')\n",
    "fig.set_yticks(list(range(0, 55, 5)))\n",
    "fig.set_title('Peak hours (1-6 PM)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a driver uses another route, does it achieve its destination faster than what Google Maps predicts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Other routes comparison')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=list(range(60)), y=list(range(60)), color='black')\n",
    "fig = sns.scatterplot(data=df_obey_others, x='gspeed', y='actualspeed', hue='peak', palette=\"deep\")\n",
    "fig.set_title('Other routes comparison')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like most of the points lie under the black line, which suggests the driver could've been faster by obeying GMaps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For now this part is unused, but might come in handy ;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#f = pd.read_feather('data/processed_jkt.ftr')\n",
    "#g = pd.read_feather('data/matched_jkt.ftr')[['trj_id', 'pingtimestamp', 'location']]\n",
    "#g = f.merge(g, on=['trj_id', 'pingtimestamp'], how='inner')\n",
    "#g.sort_values(by=['trj_id', 'pingtimestamp'], inplace=True)\n",
    "#g.reset_index(drop=True).to_feather('data/matched_jkt.ftr')\n",
    "#g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>trj_id</th>\n",
       "      <th>osrmdis</th>\n",
       "      <th>gdis</th>\n",
       "      <th>actualdis</th>\n",
       "      <th>osrmdelta</th>\n",
       "      <th>gdelta</th>\n",
       "      <th>gwins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11293</td>\n",
       "      <td>7469.5</td>\n",
       "      <td>6737</td>\n",
       "      <td>7696.080962</td>\n",
       "      <td>226.580962</td>\n",
       "      <td>959.080962</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1241</td>\n",
       "      <td>6294.5</td>\n",
       "      <td>14437</td>\n",
       "      <td>6359.299043</td>\n",
       "      <td>64.799043</td>\n",
       "      <td>8077.700957</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13434</td>\n",
       "      <td>8593.7</td>\n",
       "      <td>8608</td>\n",
       "      <td>8493.836200</td>\n",
       "      <td>99.863800</td>\n",
       "      <td>114.163800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1406</td>\n",
       "      <td>5826.9</td>\n",
       "      <td>5938</td>\n",
       "      <td>5590.929213</td>\n",
       "      <td>235.970787</td>\n",
       "      <td>347.070787</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14594</td>\n",
       "      <td>6163.9</td>\n",
       "      <td>6170</td>\n",
       "      <td>5827.865906</td>\n",
       "      <td>336.034094</td>\n",
       "      <td>342.134094</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>83984</td>\n",
       "      <td>22064.6</td>\n",
       "      <td>27578</td>\n",
       "      <td>19409.594637</td>\n",
       "      <td>2655.005363</td>\n",
       "      <td>8168.405363</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>8647</td>\n",
       "      <td>4842.8</td>\n",
       "      <td>4838</td>\n",
       "      <td>4901.960086</td>\n",
       "      <td>59.160086</td>\n",
       "      <td>63.960086</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>9147</td>\n",
       "      <td>5796.3</td>\n",
       "      <td>5802</td>\n",
       "      <td>5798.554172</td>\n",
       "      <td>2.254172</td>\n",
       "      <td>3.445828</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>9636</td>\n",
       "      <td>5685.5</td>\n",
       "      <td>5677</td>\n",
       "      <td>5870.274020</td>\n",
       "      <td>184.774020</td>\n",
       "      <td>193.274020</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>999</td>\n",
       "      <td>4277.6</td>\n",
       "      <td>4915</td>\n",
       "      <td>4239.316655</td>\n",
       "      <td>38.283345</td>\n",
       "      <td>675.683345</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trj_id  osrmdis   gdis     actualdis    osrmdelta       gdelta  gwins\n",
       "5     11293   7469.5   6737   7696.080962   226.580962   959.080962  False\n",
       "6      1241   6294.5  14437   6359.299043    64.799043  8077.700957  False\n",
       "9     13434   8593.7   8608   8493.836200    99.863800   114.163800  False\n",
       "11     1406   5826.9   5938   5590.929213   235.970787   347.070787  False\n",
       "16    14594   6163.9   6170   5827.865906   336.034094   342.134094  False\n",
       "..      ...      ...    ...           ...          ...          ...    ...\n",
       "193   83984  22064.6  27578  19409.594637  2655.005363  8168.405363  False\n",
       "194    8647   4842.8   4838   4901.960086    59.160086    63.960086  False\n",
       "196    9147   5796.3   5802   5798.554172     2.254172     3.445828  False\n",
       "200    9636   5685.5   5677   5870.274020   184.774020   193.274020  False\n",
       "201     999   4277.6   4915   4239.316655    38.283345   675.683345  False\n",
       "\n",
       "[104 rows x 7 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_delta = df_merged[['trj_id', 'osrmdis', 'gdis', 'actualdis']]\n",
    "df_delta['osrmdelta'] = abs(df_delta.osrmdis - df_delta.actualdis)\n",
    "df_delta['gdelta'] = abs(df_delta.gdis - df_delta.actualdis)\n",
    "df_delta['gwins'] = df_delta['gdelta'] < df_delta['osrmdelta']\n",
    "#print(sum(df_delta.gwins))\n",
    "df_delta[~df_delta.gwins]"
   ]
  }
 ],
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