Doc_1_Trajectory_Processing.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trajectory Processing\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dependent packages\n",
    "import pandas as pd\n",
    "from preprocessing import Preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Preprocessing\n",
    "\n",
    "The preprocessing precedure including five steps:\n",
    "1. Encoding.\n",
    "2. Trip segmentation.\n",
    "3. Densification.\n",
    "4. Calculation.\n",
    "5. Smoothing.\n",
    "6. Filtering.\n",
    "\n",
    "All methods are packed in class `Preprocess` in the module `preprocessing`.  \n",
    "> - Map-matching is not included in this section. Instead, we stored it in a seperate module `mapmatch` to avoid environment conflict.\n",
    "> - Due to the data privacy, we can only provide processing procedure with limited samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding is performed by the function `Preprocess.encode`. In the encoding function, the following operations are supported:\n",
    "- Set column names.\n",
    "- Encoding strings as ascending numbers, *e.g.* `vehID` and `orderID`.\n",
    "- CRS transferring, note that all calculation in this module is based on **WGS84**.\n",
    "- Sorting by some columns."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "raw = pd.read_csv(\"./data/traj/sample_raw.csv\")\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Encoding...\n"
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       "      <th></th>\n",
       "      <th>vehID</th>\n",
       "      <th>orderID</th>\n",
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       "   vehID  orderID     time[s]         lon        lat\n",
       "0      0        0  1477969147  104.072604  30.729620\n",
       "1      0        0  1477969150  104.072604  30.729401\n",
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   "source": [
    "# initialize a Preprocess object\n",
    "preprocessor = Preprocess()\n",
    "\n",
    "# set encode params\n",
    "preprocessor.setParam(\n",
    "    funcName='encode',\n",
    "    paramDict={\n",
    "        \"colName\": ['vehID', 'orderID', 'time[s]', 'lon', 'lat'],\n",
    "        \"encodingCol\": ['vehID', 'orderID'],\n",
    "        \"sortBy\": ['vehID', 'orderID', 'time[s]'],\n",
    "        \"originCRS\": 'GCJ02',\n",
    "    }\n",
    ")\n",
    "\n",
    "# perform encoding\n",
    "traj_encoded = preprocessor.encode(raw)\n",
    "traj_encoded.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There exist some missing points in a trajectory of a trip, which bring difficulty to the inference of the complete trajctory. Therefore, we divided sequences in the same order at intervals with missing duration longer than a certain length.  \n",
    "\n",
    "Segmentation is performed by the function `Preprocess.segment`. The default maximum missing interval is set to 6 sec cause it represents a missing of more than two samples (the sampling frequency is 2 to 4 sec)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmenting...\n",
      "- segmented trip amount: 12.\n"
     ]
    },
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       "   vehID  orderID     time[s]         lon        lat  tripID\n",
       "0      0        0  1477969147  104.072604  30.729620       0\n",
       "1      0        0  1477969150  104.072604  30.729401       0\n",
       "2      0        0  1477969154  104.072514  30.729101       0\n",
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       "4      0        0  1477969159  104.072444  30.728201       0"
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   "source": [
    "# set segmentation params\n",
    "preprocessor.setParam(\n",
    "    funcName='segment',\n",
    "    paramDict={\n",
    "        \"interval\": True,\n",
    "        \"segRefCol\": True,\n",
    "        \"segRefCol\": 'orderID',\n",
    "        \"timeCol\": 'time[s]',\n",
    "        \"tripIDColName\": 'tripID',\n",
    "        \"maxInterval\": 6,\n",
    "    }\n",
    ")\n",
    "\n",
    "# perform segmentation\n",
    "traj_segmented = preprocessor.segment(traj_encoded)\n",
    "traj_segmented.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Densification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Densification is performed by the function `Preprocess.densify` for each trip. The default densification is the cubic spline interpotation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Densifying: 100%|██████████| 12/12 [00:00<00:00, 25.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- densified length: 3050;\n",
      "- densified ratio: 305.00%.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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   "source": [
    "# set densify params\n",
    "preprocessor.setParam(\n",
    "    funcName='densify',\n",
    "    paramDict={\n",
    "        \"timeCol\": 'time[s]',\n",
    "        \"tripIDCol\": 'tripID',\n",
    "        \"interpFunc\": 'cubic',\n",
    "    }\n",
    ")\n",
    "\n",
    "# perform densification\n",
    "traj_densified = preprocessor.densify(traj_segmented)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculation is performed by the function `Preprocess.calParam`. The following parameters will be calculated:\n",
    "\n",
    "- Speed [km/h].\n",
    "- Acceleration [m/s$^2$].\n",
    "- Road grade [Degree].\n",
    "- VSP [kW/t]\n",
    "\n",
    "> Notes:\n",
    "> 1. Currently this module only support road grade from DEM files. Automative online extraction will be developed in the future. If `demPath=None`, the road grade will be assumed 0.\n",
    "> 2. Calculation of VSP is only available for light-duty vehicles (LDVs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating...\n"
     ]
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       "</div>"
      ],
      "text/plain": [
       "   vehID  orderID  tripID       time[s]         lon        lat  dist[km]   \n",
       "0    0.0      0.0     0.0  1.477969e+09  104.072604  30.729620  0.012770  \\\n",
       "1    0.0      0.0     0.0  1.477969e+09  104.072609  30.729506  0.007384   \n",
       "2    0.0      0.0     0.0  1.477969e+09  104.072610  30.729439  0.004367   \n",
       "3    0.0      0.0     0.0  1.477969e+09  104.072604  30.729401  0.003783   \n",
       "4    0.0      0.0     0.0  1.477969e+09  104.072592  30.729368  0.005508   \n",
       "\n",
       "   interval[s]  speed[km/h]  acc[m/s2]  ele[m]  grade[D]  VSP[kW/t]  \n",
       "0          1.0    45.971886  -5.385921   468.0       0.0 -66.539696  \n",
       "1          1.0    26.582569  -3.017187   468.0       0.0 -21.301853  \n",
       "2          1.0    15.720697  -0.583657   468.0       0.0  -2.039436  \n",
       "3          1.0    13.619533   1.725009   468.0       0.0   6.958423  \n",
       "4          1.0    19.829565   3.963946   468.0       0.0  22.505531  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DEM path\n",
    "demPath = \"./data/geo/AP_27359_FBS_F0600_RT1.dem.tif\"\n",
    "\n",
    "# set calculate params\n",
    "preprocessor.setParam(\n",
    "    funcName='calculate',\n",
    "    paramDict={\n",
    "        \"demPath\": demPath,\n",
    "        \"tripIDCol\": 'tripID'\n",
    "    }\n",
    ")\n",
    "\n",
    "# perform calculation\n",
    "traj_calculated = preprocessor.calParam(traj_densified)\n",
    "traj_calculated.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 Smoothing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Smoothing is performed by function `Preprocess.smooth` for each trip. The default smoothing function is the exponential smoothing. Other smooth function includes:\n",
    "- `exp`: exponentail smoothing\n",
    "- `dexp`: double exponential smoothing\n",
    "- `kal`: Kalman filter smoothing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Smoothing: 100%|██████████| 11/11 [00:00<00:00, 533.56it/s]\n"
     ]
    },
    {
     "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>vehID</th>\n",
       "      <th>orderID</th>\n",
       "      <th>tripID</th>\n",
       "      <th>time[s]</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>dist[km]</th>\n",
       "      <th>interval[s]</th>\n",
       "      <th>speed[km/h]</th>\n",
       "      <th>acc[m/s2]</th>\n",
       "      <th>ele[m]</th>\n",
       "      <th>grade[D]</th>\n",
       "      <th>VSP[kW/t]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.477969e+09</td>\n",
       "      <td>104.072604</td>\n",
       "      <td>30.729620</td>\n",
       "      <td>0.012770</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.971886</td>\n",
       "      <td>-5.385921</td>\n",
       "      <td>468.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-66.539696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.477969e+09</td>\n",
       "      <td>104.072609</td>\n",
       "      <td>30.729506</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.399364</td>\n",
       "      <td>-3.727807</td>\n",
       "      <td>468.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.873207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.477969e+09</td>\n",
       "      <td>104.072610</td>\n",
       "      <td>30.729439</td>\n",
       "      <td>0.004367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.724297</td>\n",
       "      <td>-1.526902</td>\n",
       "      <td>468.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-11.889567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.477969e+09</td>\n",
       "      <td>104.072604</td>\n",
       "      <td>30.729401</td>\n",
       "      <td>0.003783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.750962</td>\n",
       "      <td>0.749436</td>\n",
       "      <td>468.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.304026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.477969e+09</td>\n",
       "      <td>104.072592</td>\n",
       "      <td>30.729368</td>\n",
       "      <td>0.005508</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.605984</td>\n",
       "      <td>2.999593</td>\n",
       "      <td>468.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.145081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vehID  orderID  tripID       time[s]         lon        lat  dist[km]   \n",
       "0    0.0      0.0     0.0  1.477969e+09  104.072604  30.729620  0.012770  \\\n",
       "1    0.0      0.0     0.0  1.477969e+09  104.072609  30.729506  0.007384   \n",
       "2    0.0      0.0     0.0  1.477969e+09  104.072610  30.729439  0.004367   \n",
       "3    0.0      0.0     0.0  1.477969e+09  104.072604  30.729401  0.003783   \n",
       "4    0.0      0.0     0.0  1.477969e+09  104.072592  30.729368  0.005508   \n",
       "\n",
       "   interval[s]  speed[km/h]  acc[m/s2]  ele[m]  grade[D]  VSP[kW/t]  \n",
       "0          1.0    45.971886  -5.385921   468.0       0.0 -66.539696  \n",
       "1          1.0    32.399364  -3.727807   468.0       0.0 -34.873207  \n",
       "2          1.0    20.724297  -1.526902   468.0       0.0 -11.889567  \n",
       "3          1.0    15.750962   0.749436   468.0       0.0   1.304026  \n",
       "4          1.0    18.605984   2.999593   468.0       0.0  16.145081  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set smooth params\n",
    "preprocessor.setParam(\n",
    "    funcName='smooth',\n",
    "    paramDict={\n",
    "        \"smoothCol\": ['speed[km/h]', 'acc[m/s2]', 'VSP[kW/t]'],\n",
    "        \"tripIDCol\": \"tripID\",\n",
    "        \"smoothFunc\": 'exp',\n",
    "    }\n",
    ")\n",
    "\n",
    "# perform smoothing\n",
    "traj_smoothed = preprocessor.smooth(traj_calculated)\n",
    "traj_smoothed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(6,2))\n",
    "trip_calculated = traj_calculated[traj_calculated['tripID'] == 0].copy()\n",
    "trip_smoothed = traj_smoothed[traj_smoothed['tripID'] == 0].copy()\n",
    "plt.plot(trip_calculated['time[s]'], trip_calculated['speed[km/h]'], label='Old', alpha=0.5)\n",
    "plt.plot(trip_smoothed['time[s]'], trip_smoothed['speed[km/h]'], label='New', alpha=0.5)\n",
    "plt.xlabel(\"Speed [km/h]\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6 Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering is performed by the function `Preprocess.filter` which filters redundancy and samples exceeding a range of speed, acceleration and VSP.\n",
    "- The default speed range is set to `[0, 150]` km/h.\n",
    "- The default acc range is set to `[-4.4704, 6.25856]` m/s$^2$ ([-10, 10] mph/s).\n",
    "- The default VSP range is set to `[-47.5, 62.5]` kW/t."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtering...\n",
      "- filtered length: 2966;\n",
      "- filtered ratio: 2.02%.\n"
     ]
    }
   ],
   "source": [
    "# set filter params\n",
    "preprocessor.setParam(\n",
    "    funcName='filter',\n",
    "    paramDict={\n",
    "        \"minSpeed\": 0,\n",
    "        \"maxSpeed\": 150,\n",
    "        \"minAcc\": -4.4704,\n",
    "        \"maxAcc\": 6.25856,\n",
    "        \"minVSP\": -47.5,\n",
    "        \"maxVSP\": 62.5\n",
    "    }\n",
    ")\n",
    "\n",
    "# perform filtering\n",
    "traj_filtered = preprocessor.filter(traj_smoothed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Pipe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We provide pipeline function to combine several operations in one procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding...\n",
      "Segmenting...\n",
      "- segmented trip amount: 12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Densifying:   0%|          | 0/12 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Densifying: 100%|██████████| 12/12 [00:00<00:00, 51.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- densified length: 3050;\n",
      "- densified ratio: 305.00%.\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Smoothing: 100%|██████████| 11/11 [00:00<00:00, 687.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtering...\n",
      "- filtered length: 2963;\n",
      "- filtered ratio: 2.11%.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "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>vehID</th>\n",
       "      <th>orderID</th>\n",
       "      <th>tripID</th>\n",
       "      <th>time[s]</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>dist[km]</th>\n",
       "      <th>interval[s]</th>\n",
       "      <th>speed[km/h]</th>\n",
       "      <th>acc[m/s2]</th>\n",
       "      <th>ele[m]</th>\n",
       "      <th>grade[D]</th>\n",
       "      <th>VSP[kW/t]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.477969e+09</td>\n",
       "      <td>104.070087</td>\n",
       "      <td>30.731888</td>\n",
       "      <td>0.007380</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.384281</td>\n",
       "      <td>-3.726804</td>\n",
       "      <td>466.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-34.847050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.477969e+09</td>\n",
       "      <td>104.070087</td>\n",
       "      <td>30.731822</td>\n",
       "      <td>0.004364</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.712196</td>\n",
       "      <td>-1.527181</td>\n",
       "      <td>466.000</td>\n",
       "      <td>0.068639</td>\n",
       "      <td>-9.830153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.477969e+09</td>\n",
       "      <td>104.070082</td>\n",
       "      <td>30.731783</td>\n",
       "      <td>0.003779</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.737670</td>\n",
       "      <td>0.747944</td>\n",
       "      <td>465.700</td>\n",
       "      <td>0.055508</td>\n",
       "      <td>3.349933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.477969e+09</td>\n",
       "      <td>104.070070</td>\n",
       "      <td>30.731750</td>\n",
       "      <td>0.005502</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.587265</td>\n",
       "      <td>2.996978</td>\n",
       "      <td>465.490</td>\n",
       "      <td>0.026709</td>\n",
       "      <td>17.738155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.477969e+09</td>\n",
       "      <td>104.070051</td>\n",
       "      <td>30.731704</td>\n",
       "      <td>0.009463</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.423483</td>\n",
       "      <td>5.278275</td>\n",
       "      <td>465.343</td>\n",
       "      <td>0.042550</td>\n",
       "      <td>50.495445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vehID  orderID  tripID       time[s]         lon        lat  dist[km]   \n",
       "1    0.0      0.0     0.0  1.477969e+09  104.070087  30.731888  0.007380  \\\n",
       "2    0.0      0.0     0.0  1.477969e+09  104.070087  30.731822  0.004364   \n",
       "3    0.0      0.0     0.0  1.477969e+09  104.070082  30.731783  0.003779   \n",
       "4    0.0      0.0     0.0  1.477969e+09  104.070070  30.731750  0.005502   \n",
       "5    0.0      0.0     0.0  1.477969e+09  104.070051  30.731704  0.009463   \n",
       "\n",
       "   interval[s]  speed[km/h]  acc[m/s2]   ele[m]  grade[D]  VSP[kW/t]  \n",
       "1          1.0    32.384281  -3.726804  466.000  0.000000 -34.847050  \n",
       "2          1.0    20.712196  -1.527181  466.000  0.068639  -9.830153  \n",
       "3          1.0    15.737670   0.747944  465.700  0.055508   3.349933  \n",
       "4          1.0    18.587265   2.996978  465.490  0.026709  17.738155  \n",
       "5          1.0    29.423483   5.278275  465.343  0.042550  50.495445  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from preprocessing import Preprocess\n",
    "\n",
    "# set parameters\n",
    "encodeParam = {\n",
    "    \"colName\": ['vehID', 'orderID', 'time[s]', 'lon', 'lat'],\n",
    "    \"encodingCol\": ['vehID', 'orderID'],\n",
    "    \"sortBy\": ['vehID', 'orderID', 'time[s]'],\n",
    "    \"originCRS\": 'GCJ02'\n",
    "}\n",
    "filterParam = {\"maxSpeed\": 150}\n",
    "segmentParam = {\"segRefCol\": 'orderID', \"maxInterval\": 6}\n",
    "densifyParam = {\"interpFunc\": 'cubic'}\n",
    "demPath = \"./data/geo/AP_27359_FBS_F0600_RT1.dem.tif\"\n",
    "calculateParam = {\"demPath\": demPath}\n",
    "smoothParam = {\"smoothCol\": ['speed[km/h]', 'acc[m/s2]', 'VSP[kW/t]'], \"smoothFunc\": 'exp'}\n",
    "\n",
    "pre = Preprocess(\n",
    "    encodeParam=encodeParam,\n",
    "    filterParam=filterParam,\n",
    "    segmentParam=segmentParam,\n",
    "    densifyParam=densifyParam,\n",
    "    calculateParam=calculateParam,\n",
    "    smoothParam=smoothParam\n",
    ")\n",
    "\n",
    "# perform pipeline\n",
    "traj = pre.pipe(raw, steps=['encode', 'segment', 'densify', 'calculate', 'smooth', 'filter'])\n",
    "traj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# traj.to_csv(\"./data/traj/sample_preprocessed.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `Preprocess.printDefautParam` helps you check defaut parameters in `Preprocess`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "colName: ['vehID', 'orderID', 'time[s]', 'lon', 'lat']\n",
      "encodingCol: ['vehID', 'orderID']\n",
      "sortBy: ['vehID', 'orderID', 'time[s]']\n",
      "originCRS: GCJ02\n"
     ]
    }
   ],
   "source": [
    "preprocessor.printDefaultParam('encode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intervalCol: interval[s]\n",
      "speedCol: speed[km/h]\n",
      "accCol: acc[m/s2]\n",
      "VSPCol: VSP[kW/t]\n",
      "minSpeed: 0\n",
      "maxSpeed: 150\n",
      "minAcc: -4.4704\n",
      "maxAcc: 6.25856\n",
      "minVSP: -47.5\n",
      "maxVSP: 62.5\n"
     ]
    }
   ],
   "source": [
    "preprocessor.printDefaultParam('filter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval: True\n",
      "segRefCol: orderID\n",
      "timeCol: time[s]\n",
      "tripIDColName: tripID\n",
      "maxInterval: 6\n"
     ]
    }
   ],
   "source": [
    "preprocessor.printDefaultParam('segment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timeCol: time[s]\n",
      "tripIDCol: tripID\n",
      "sortBy: ['vehID', 'tripID', 'time[s]']\n",
      "lonCol: lon\n",
      "latCol: lat\n",
      "interpFunc: cubic\n"
     ]
    }
   ],
   "source": [
    "preprocessor.printDefaultParam('densify')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "demPath: None\n",
      "tripIDCol: tripID\n",
      "sortBy: ['vehID', 'tripID', 'time[s]']\n",
      "dem: None\n",
      "speed: True\n",
      "acc: True\n",
      "VSP: True\n",
      "weight: 1.497\n",
      "lonCol: lon\n",
      "latCol: lat\n",
      "timeCol: time[s]\n",
      "distColName: dist[km]\n",
      "intervalColName: interval[s]\n",
      "speedColName: speed[km/h]\n",
      "accColName: acc[m/s2]\n",
      "gradeColName: grade[D]\n",
      "VSPColName: VSP[kW/t]\n",
      "calDirect: forward\n"
     ]
    }
   ],
   "source": [
    "preprocessor.printDefaultParam('calculate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smoothCol: ['speed[km/h]', 'acc[m/s2]', 'VSP[kW/t]']\n",
      "tripIDCol: tripID\n",
      "smoothFunc: exp\n"
     ]
    }
   ],
   "source": [
    "preprocessor.printDefaultParam('smooth')"
   ]
  }
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