diff --git a/model_base.ipynb b/model_base.ipynb
index 189305a..4a1f83f 100644
--- a/model_base.ipynb
+++ b/model_base.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,353 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5py.File('./data/TrainingData.h5', 'r') as f:\n",
+    "    # Access the trip dataset and their corresponding timestamps\n",
+    "    traffic_data = f['trip'][()]\n",
+    "    dates = f['timeslot'][()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "formatted_dates = []\n",
+    "\n",
+    "for date_string in dates:\n",
+    "    formatted_date = datetime.strptime(date_string.decode(), '%Y%m%d%H%M')\n",
+    "\n",
+    "    year = formatted_date.year\n",
+    "    month = formatted_date.month\n",
+    "    day = formatted_date.day\n",
+    "    hour = formatted_date.hour\n",
+    "    minute = formatted_date.minute\n",
+    "\n",
+    "    formatted_dates.append(np.array([year, month, day, hour, minute]))\n",
+    "\n",
+    "formatted_dates = np.array(formatted_dates).reshape(1488, 5, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_size = 240\n",
+    "\n",
+    "train_traffic_data = traffic_data[:-test_size]\n",
+    "test_traffic_data = traffic_data[-test_size:]\n",
+    "\n",
+    "train_formatted_dates = formatted_dates[:-test_size]\n",
+    "test_formatted_dates = formatted_dates[-test_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test = train_formatted_dates, test_formatted_dates\n",
+    "y_train, y_test = train_traffic_data, test_traffic_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>Join_Count</th>\n",
+       "      <th>TARGET_FID</th>\n",
+       "      <th>BlockID</th>\n",
+       "      <th>BikeLane_miles</th>\n",
+       "      <th>AADT</th>\n",
+       "      <th>SpeedLimit</th>\n",
+       "      <th>AvgSpeed</th>\n",
+       "      <th>Pop2019</th>\n",
+       "      <th>Pop_Density</th>\n",
+       "      <th>Shape_Length</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>StationNum</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>32638.666667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7840.490288</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7840.686448</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.807055</td>\n",
+       "      <td>21234.741935</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1049.0</td>\n",
+       "      <td>0.029594</td>\n",
+       "      <td>7840.882970</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.096221</td>\n",
+       "      <td>5915.125000</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1816.0</td>\n",
+       "      <td>0.030252</td>\n",
+       "      <td>7841.080477</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.984320</td>\n",
+       "      <td>23940.078947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1892.0</td>\n",
+       "      <td>0.022647</td>\n",
+       "      <td>7841.277327</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>3</td>\n",
+       "      <td>124</td>\n",
+       "      <td>124</td>\n",
+       "      <td>1.955707</td>\n",
+       "      <td>9050.487805</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1986.0</td>\n",
+       "      <td>0.046045</td>\n",
+       "      <td>7840.280937</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>3</td>\n",
+       "      <td>125</td>\n",
+       "      <td>125</td>\n",
+       "      <td>1.440449</td>\n",
+       "      <td>9121.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1198.0</td>\n",
+       "      <td>0.039726</td>\n",
+       "      <td>7840.085072</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>2</td>\n",
+       "      <td>126</td>\n",
+       "      <td>126</td>\n",
+       "      <td>0.985704</td>\n",
+       "      <td>8770.901961</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>555.0</td>\n",
+       "      <td>0.015125</td>\n",
+       "      <td>7839.890518</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>4</td>\n",
+       "      <td>127</td>\n",
+       "      <td>127</td>\n",
+       "      <td>0.228593</td>\n",
+       "      <td>12266.085714</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>814.0</td>\n",
+       "      <td>0.031125</td>\n",
+       "      <td>7839.696621</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>4</td>\n",
+       "      <td>128</td>\n",
+       "      <td>128</td>\n",
+       "      <td>1.619940</td>\n",
+       "      <td>11077.037037</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1385.0</td>\n",
+       "      <td>0.059671</td>\n",
+       "      <td>7839.501980</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>128 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Join_Count  TARGET_FID  BlockID  BikeLane_miles          AADT  \\\n",
+       "0             0           1        1             NaN  32638.666667   \n",
+       "1             0           2        2             NaN           NaN   \n",
+       "2             3           3        3        0.807055  21234.741935   \n",
+       "3             3           4        4        2.096221   5915.125000   \n",
+       "4             3           5        5        0.984320  23940.078947   \n",
+       "..          ...         ...      ...             ...           ...   \n",
+       "123           3         124      124        1.955707   9050.487805   \n",
+       "124           3         125      125        1.440449   9121.500000   \n",
+       "125           2         126      126        0.985704   8770.901961   \n",
+       "126           4         127      127        0.228593  12266.085714   \n",
+       "127           4         128      128        1.619940  11077.037037   \n",
+       "\n",
+       "     SpeedLimit  AvgSpeed  Pop2019  Pop_Density  Shape_Length    Shape_Area  \\\n",
+       "0           NaN       NaN      0.0     0.000000   7840.490288  3.787844e+06   \n",
+       "1           NaN       NaN      0.0     0.000000   7840.686448  3.787843e+06   \n",
+       "2          25.0      15.0   1049.0     0.029594   7840.882970  3.787843e+06   \n",
+       "3          25.0      19.0   1816.0     0.030252   7841.080477  3.787844e+06   \n",
+       "4           NaN       NaN   1892.0     0.022647   7841.277327  3.787843e+06   \n",
+       "..          ...       ...      ...          ...           ...           ...   \n",
+       "123         NaN       NaN   1986.0     0.046045   7840.280937  3.787843e+06   \n",
+       "124         NaN       NaN   1198.0     0.039726   7840.085072  3.787843e+06   \n",
+       "125         NaN       NaN    555.0     0.015125   7839.890518  3.787843e+06   \n",
+       "126        25.0      16.0    814.0     0.031125   7839.696621  3.787844e+06   \n",
+       "127        25.0      16.0   1385.0     0.059671   7839.501980  3.787843e+06   \n",
+       "\n",
+       "     StationNum  \n",
+       "0             0  \n",
+       "1             0  \n",
+       "2             3  \n",
+       "3             3  \n",
+       "4             3  \n",
+       "..          ...  \n",
+       "123           3  \n",
+       "124           3  \n",
+       "125           2  \n",
+       "126           4  \n",
+       "127           4  \n",
+       "\n",
+       "[128 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spatio_data_file = \"./data/SpatialFeatures.csv\"\n",
+    "raw_spatio_feature = pd.read_csv(spatio_data_file)\n",
+    "raw_spatio_feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeLane_matrix = raw_spatio_feature[\"BikeLane_miles\"]\n",
+    "bikeLane_matrix = bikeLane_matrix.fillna(0).to_numpy()\n",
+    "bikeLane_matrix = bikeLane_matrix/bikeLane_matrix.max()\n",
+    "bikeLane_matrix_board = bikeLane_matrix[np.newaxis,:] * np.ones((1248,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +373,7 @@
     "\n",
     "left_inputs = layers.Input(shape = input_shape)\n",
     "\n",
-    "x = models.layers.LSTM(50, activation = \"tanh\")(left_inputs)\n",
+    "x = layers.LSTM(50, activation = \"tanh\")(left_inputs)\n",
     "\n",
     "x = layers.Dense(2*16*8, activation = \"linear\")(x)\n",
     "\n",
@@ -39,12 +385,220 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[2019],\n",
+       "        [  10],\n",
+       "        [   1],\n",
+       "        [   0],\n",
+       "        [  30]],\n",
+       "\n",
+       "       [[2019],\n",
+       "        [  10],\n",
+       "        [   1],\n",
+       "        [   1],\n",
+       "        [   0]],\n",
+       "\n",
+       "       [[2019],\n",
+       "        [  10],\n",
+       "        [   1],\n",
+       "        [   1],\n",
+       "        [  30]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[2019],\n",
+       "        [  10],\n",
+       "        [  26],\n",
+       "        [  23],\n",
+       "        [   0]],\n",
+       "\n",
+       "       [[2019],\n",
+       "        [  10],\n",
+       "        [  26],\n",
+       "        [  23],\n",
+       "        [  30]],\n",
+       "\n",
+       "       [[2019],\n",
+       "        [  10],\n",
+       "        [  27],\n",
+       "        [   0],\n",
+       "        [   0]]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "X_train_scaled = X_train[:,3:]\n",
+    "X_test_scaled = X_test[:,3:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "39/39 [==============================] - 1s 9ms/step - loss: 213.0473 - val_loss: 144.8039\n",
+      "Epoch 2/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 184.4646 - val_loss: 119.2632\n",
+      "Epoch 3/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 158.5347 - val_loss: 105.8267\n",
+      "Epoch 4/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 143.7741 - val_loss: 98.0472\n",
+      "Epoch 5/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 134.0348 - val_loss: 93.0242\n",
+      "Epoch 6/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 127.1140 - val_loss: 89.8005\n",
+      "Epoch 7/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 122.1602 - val_loss: 87.7104\n",
+      "Epoch 8/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 118.5489 - val_loss: 86.4234\n",
+      "Epoch 9/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 115.9217 - val_loss: 85.6522\n",
+      "Epoch 10/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 114.0212 - val_loss: 85.3681\n",
+      "Epoch 11/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 112.5865 - val_loss: 85.1347\n",
+      "Epoch 12/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 111.6269 - val_loss: 85.2543\n",
+      "Epoch 13/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 110.8616 - val_loss: 85.2738\n",
+      "Epoch 14/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 110.3609 - val_loss: 85.4121\n",
+      "Epoch 15/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 110.0156 - val_loss: 85.7094\n",
+      "Epoch 16/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 109.7098 - val_loss: 85.6584\n",
+      "Epoch 17/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 109.5667 - val_loss: 86.0608\n",
+      "Epoch 18/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 109.3852 - val_loss: 86.0898\n",
+      "Epoch 19/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 109.3077 - val_loss: 86.1924\n",
+      "Epoch 20/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 109.2454 - val_loss: 86.4365\n",
+      "Epoch 21/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 109.1953 - val_loss: 86.2306\n",
+      "Epoch 22/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 108.7120 - val_loss: 85.5497\n",
+      "Epoch 23/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 107.9923 - val_loss: 84.8486\n",
+      "Epoch 24/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 106.5528 - val_loss: 83.9956\n",
+      "Epoch 25/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 105.1779 - val_loss: 81.6498\n",
+      "Epoch 26/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 103.8916 - val_loss: 81.1214\n",
+      "Epoch 27/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 102.9861 - val_loss: 81.5862\n",
+      "Epoch 28/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 100.8951 - val_loss: 79.1956\n",
+      "Epoch 29/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 97.6820 - val_loss: 75.3084\n",
+      "Epoch 30/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 94.0599 - val_loss: 71.8201\n",
+      "Epoch 31/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 89.8942 - val_loss: 68.2872\n",
+      "Epoch 32/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 86.6234 - val_loss: 68.3562\n",
+      "Epoch 33/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 83.7273 - val_loss: 66.8059\n",
+      "Epoch 34/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 81.5555 - val_loss: 65.0340\n",
+      "Epoch 35/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 79.8541 - val_loss: 64.4435\n",
+      "Epoch 36/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 78.2599 - val_loss: 64.8526\n",
+      "Epoch 37/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 76.9122 - val_loss: 63.9882\n",
+      "Epoch 38/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 75.8087 - val_loss: 63.5033\n",
+      "Epoch 39/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 75.1495 - val_loss: 63.4638\n",
+      "Epoch 40/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 74.5850 - val_loss: 64.1694\n",
+      "Epoch 41/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 74.6376 - val_loss: 65.1026\n",
+      "Epoch 42/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 73.7184 - val_loss: 63.3465\n",
+      "Epoch 43/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 73.0771 - val_loss: 62.3124\n",
+      "Epoch 44/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 72.4952 - val_loss: 62.8214\n",
+      "Epoch 45/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 72.1373 - val_loss: 62.8891\n",
+      "Epoch 46/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 71.4127 - val_loss: 63.0681\n",
+      "Epoch 47/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 71.1406 - val_loss: 63.8471\n",
+      "Epoch 48/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 70.7038 - val_loss: 62.8992\n",
+      "Epoch 49/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 70.3270 - val_loss: 62.5290\n",
+      "Epoch 50/50\n",
+      "39/39 [==============================] - 0s 1ms/step - loss: 70.1907 - val_loss: 63.5971\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x2acdbdfdeb0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='adam', loss='mse')\n",
+    "model.fit([X_train], y_train, epochs=50, batch_size=32, validation_data=([X_test], y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8/8 [==============================] - 0s 1ms/step - loss: 63.5971\n",
+      "Root Mean Squared Error: 7.9747811027309465\n"
+     ]
+    }
+   ],
+   "source": [
+    "mse = model.evaluate(X_test, y_test)\n",
+    "\n",
+    "# Show rmse to see how model performs on the test set\n",
+    "rmse = np.sqrt(mse)\n",
+    "print(f'Root Mean Squared Error: {rmse}')"
+   ]
   }
  ],
  "metadata": {
@@ -54,8 +608,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.10.13"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,
diff --git a/model_v1.ipynb b/model_v1.ipynb
index db6c599..6e84e8c 100644
--- a/model_v1.ipynb
+++ b/model_v1.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,295 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>Join_Count</th>\n",
+       "      <th>TARGET_FID</th>\n",
+       "      <th>BlockID</th>\n",
+       "      <th>BikeLane_miles</th>\n",
+       "      <th>AADT</th>\n",
+       "      <th>SpeedLimit</th>\n",
+       "      <th>AvgSpeed</th>\n",
+       "      <th>Pop2019</th>\n",
+       "      <th>Pop_Density</th>\n",
+       "      <th>Shape_Length</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>StationNum</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>32638.666667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7840.490288</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7840.686448</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.807055</td>\n",
+       "      <td>21234.741935</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1049.0</td>\n",
+       "      <td>0.029594</td>\n",
+       "      <td>7840.882970</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.096221</td>\n",
+       "      <td>5915.125000</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1816.0</td>\n",
+       "      <td>0.030252</td>\n",
+       "      <td>7841.080477</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.984320</td>\n",
+       "      <td>23940.078947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1892.0</td>\n",
+       "      <td>0.022647</td>\n",
+       "      <td>7841.277327</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>3</td>\n",
+       "      <td>124</td>\n",
+       "      <td>124</td>\n",
+       "      <td>1.955707</td>\n",
+       "      <td>9050.487805</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1986.0</td>\n",
+       "      <td>0.046045</td>\n",
+       "      <td>7840.280937</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>3</td>\n",
+       "      <td>125</td>\n",
+       "      <td>125</td>\n",
+       "      <td>1.440449</td>\n",
+       "      <td>9121.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1198.0</td>\n",
+       "      <td>0.039726</td>\n",
+       "      <td>7840.085072</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>2</td>\n",
+       "      <td>126</td>\n",
+       "      <td>126</td>\n",
+       "      <td>0.985704</td>\n",
+       "      <td>8770.901961</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>555.0</td>\n",
+       "      <td>0.015125</td>\n",
+       "      <td>7839.890518</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>4</td>\n",
+       "      <td>127</td>\n",
+       "      <td>127</td>\n",
+       "      <td>0.228593</td>\n",
+       "      <td>12266.085714</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>814.0</td>\n",
+       "      <td>0.031125</td>\n",
+       "      <td>7839.696621</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>4</td>\n",
+       "      <td>128</td>\n",
+       "      <td>128</td>\n",
+       "      <td>1.619940</td>\n",
+       "      <td>11077.037037</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1385.0</td>\n",
+       "      <td>0.059671</td>\n",
+       "      <td>7839.501980</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>128 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Join_Count  TARGET_FID  BlockID  BikeLane_miles          AADT  \\\n",
+       "0             0           1        1             NaN  32638.666667   \n",
+       "1             0           2        2             NaN           NaN   \n",
+       "2             3           3        3        0.807055  21234.741935   \n",
+       "3             3           4        4        2.096221   5915.125000   \n",
+       "4             3           5        5        0.984320  23940.078947   \n",
+       "..          ...         ...      ...             ...           ...   \n",
+       "123           3         124      124        1.955707   9050.487805   \n",
+       "124           3         125      125        1.440449   9121.500000   \n",
+       "125           2         126      126        0.985704   8770.901961   \n",
+       "126           4         127      127        0.228593  12266.085714   \n",
+       "127           4         128      128        1.619940  11077.037037   \n",
+       "\n",
+       "     SpeedLimit  AvgSpeed  Pop2019  Pop_Density  Shape_Length    Shape_Area  \\\n",
+       "0           NaN       NaN      0.0     0.000000   7840.490288  3.787844e+06   \n",
+       "1           NaN       NaN      0.0     0.000000   7840.686448  3.787843e+06   \n",
+       "2          25.0      15.0   1049.0     0.029594   7840.882970  3.787843e+06   \n",
+       "3          25.0      19.0   1816.0     0.030252   7841.080477  3.787844e+06   \n",
+       "4           NaN       NaN   1892.0     0.022647   7841.277327  3.787843e+06   \n",
+       "..          ...       ...      ...          ...           ...           ...   \n",
+       "123         NaN       NaN   1986.0     0.046045   7840.280937  3.787843e+06   \n",
+       "124         NaN       NaN   1198.0     0.039726   7840.085072  3.787843e+06   \n",
+       "125         NaN       NaN    555.0     0.015125   7839.890518  3.787843e+06   \n",
+       "126        25.0      16.0    814.0     0.031125   7839.696621  3.787844e+06   \n",
+       "127        25.0      16.0   1385.0     0.059671   7839.501980  3.787843e+06   \n",
+       "\n",
+       "     StationNum  \n",
+       "0             0  \n",
+       "1             0  \n",
+       "2             3  \n",
+       "3             3  \n",
+       "4             3  \n",
+       "..          ...  \n",
+       "123           3  \n",
+       "124           3  \n",
+       "125           2  \n",
+       "126           4  \n",
+       "127           4  \n",
+       "\n",
+       "[128 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spatio_data_file = \"./data/SpatialFeatures.csv\"\n",
+    "raw_spatio_feature = pd.read_csv(spatio_data_file)\n",
+    "raw_spatio_feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeLane_matrix = raw_spatio_feature[\"BikeLane_miles\"]\n",
+    "bikeLane_matrix = bikeLane_matrix.fillna(0).to_numpy()\n",
+    "bikeLane_matrix = bikeLane_matrix/bikeLane_matrix.max()\n",
+    "bikeLane_matrix_board = bikeLane_matrix[np.newaxis,:] * np.ones((1248,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,15 +372,25 @@
     "\n",
     "input_shape = (5,1)\n",
     "\n",
+    "static_features = 128\n",
+    "\n",
     "left_inputs = layers.Input(shape = input_shape)\n",
     "\n",
+    "static_input = layers.Input(shape = static_features,)\n",
+    "\n",
     "x = layers.LSTM(50, activation = \"tanh\")(left_inputs)\n",
     "\n",
-    "x = layers.Dense(2*16*8, activation = \"linear\")(x)\n",
+    "# x = layers.Dense(128,activation= \"relu\")(x)\n",
+    "\n",
+    "\n",
+    "x = layers.concatenate([x,static_input])\n",
+    "\n",
+    "x = layers.Dense(2*16*8, activation = \"relu\")(x)\n",
     "\n",
     "output = layers.Reshape((2,16,8))(x)\n",
     "\n",
-    "model = models.Model([left_inputs],outputs = output)\n",
+    "model = models.Model([left_inputs,static_input],outputs = output)\n",
+    "\n",
     "\n",
     "\n",
     "\n"
@@ -100,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -108,134 +406,39 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/50\n",
-      "39/39 [==============================] - 1s 9ms/step - loss: 212.9115 - val_loss: 144.5777\n",
-      "Epoch 2/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 183.6068 - val_loss: 118.8507\n",
-      "Epoch 3/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 158.2998 - val_loss: 105.8254\n",
-      "Epoch 4/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 143.8591 - val_loss: 98.0465\n",
-      "Epoch 5/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 134.1396 - val_loss: 93.0498\n",
-      "Epoch 6/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 127.2775 - val_loss: 89.7777\n",
-      "Epoch 7/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 122.2552 - val_loss: 87.7291\n",
-      "Epoch 8/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 118.6310 - val_loss: 86.4313\n",
-      "Epoch 9/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 115.9718 - val_loss: 85.7162\n",
-      "Epoch 10/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 114.0570 - val_loss: 85.3153\n",
-      "Epoch 11/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 112.6366 - val_loss: 85.1707\n",
-      "Epoch 12/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 111.6274 - val_loss: 85.1579\n",
-      "Epoch 13/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 110.8910 - val_loss: 85.2295\n",
-      "Epoch 14/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 110.3705 - val_loss: 85.3980\n",
-      "Epoch 15/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 109.9801 - val_loss: 85.5780\n",
-      "Epoch 16/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 109.7158 - val_loss: 85.8827\n",
-      "Epoch 17/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 109.5257 - val_loss: 86.0089\n",
-      "Epoch 18/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 109.4247 - val_loss: 86.1505\n",
-      "Epoch 19/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 109.0258 - val_loss: 85.7924\n",
-      "Epoch 20/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 108.8352 - val_loss: 85.9186\n",
-      "Epoch 21/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 108.5763 - val_loss: 85.5510\n",
-      "Epoch 22/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 108.1767 - val_loss: 85.3380\n",
-      "Epoch 23/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 107.4323 - val_loss: 84.9938\n",
-      "Epoch 24/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 106.1936 - val_loss: 82.6167\n",
-      "Epoch 25/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 104.6931 - val_loss: 81.7814\n",
-      "Epoch 26/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 102.9895 - val_loss: 79.9620\n",
-      "Epoch 27/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 101.5745 - val_loss: 78.4792\n",
-      "Epoch 28/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 98.5761 - val_loss: 76.5478\n",
-      "Epoch 29/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 94.6209 - val_loss: 72.0995\n",
-      "Epoch 30/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 90.4683 - val_loss: 70.5429\n",
-      "Epoch 31/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 86.6393 - val_loss: 67.5324\n",
-      "Epoch 32/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 83.6719 - val_loss: 66.9005\n",
-      "Epoch 33/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 81.2981 - val_loss: 65.7497\n",
-      "Epoch 34/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 79.8582 - val_loss: 65.4873\n",
-      "Epoch 35/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 78.4644 - val_loss: 64.4355\n",
-      "Epoch 36/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 77.4534 - val_loss: 65.0040\n",
-      "Epoch 37/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 76.9951 - val_loss: 65.3200\n",
-      "Epoch 38/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 76.4038 - val_loss: 64.2204\n",
-      "Epoch 39/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 75.8992 - val_loss: 64.0133\n",
-      "Epoch 40/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 75.4885 - val_loss: 64.4360\n",
-      "Epoch 41/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 75.2467 - val_loss: 64.6135\n",
-      "Epoch 42/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 74.6445 - val_loss: 64.3743\n",
-      "Epoch 43/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 74.2044 - val_loss: 63.9837\n",
-      "Epoch 44/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 73.8331 - val_loss: 64.3629\n",
-      "Epoch 45/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 73.4110 - val_loss: 63.6357\n",
-      "Epoch 46/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 72.9355 - val_loss: 63.7812\n",
-      "Epoch 47/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 72.2754 - val_loss: 63.3691\n",
-      "Epoch 48/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 71.8866 - val_loss: 64.9375\n",
-      "Epoch 49/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 71.0808 - val_loss: 63.7018\n",
-      "Epoch 50/50\n",
-      "39/39 [==============================] - 0s 2ms/step - loss: 70.5971 - val_loss: 62.9802\n"
+      " 1/39 [..............................] - ETA: 23s - loss: 131.9053"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "<keras.callbacks.History at 0x1b1db2e4a90>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ValueError",
+     "evalue": "in user code:\n\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1972, in test_function  *\n        return step_function(self, iterator)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1956, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1944, in run_step  **\n        outputs = model.test_step(data)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1850, in test_step\n        y_pred = self(x, training=False)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\input_spec.py\", line 219, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Layer \"model_6\" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 5, 1) dtype=int32>]\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[47], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmse\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbikeLane_matrix_board\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbikeLane_matrix_board\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32md:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_file8nzlr_ef.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__test_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1972, in test_function  *\n        return step_function(self, iterator)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1956, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1944, in run_step  **\n        outputs = model.test_step(data)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1850, in test_step\n        y_pred = self(x, training=False)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\input_spec.py\", line 219, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Layer \"model_6\" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 5, 1) dtype=int32>]\n"
+     ]
     }
    ],
    "source": [
     "model.compile(optimizer='adam', loss='mse')\n",
-    "model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))"
+    "model.fit([X_train,bikeLane_matrix_board], y_train, epochs=50, batch_size=32, validation_data=([X_test,bikeLane_matrix_board], y_test))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8/8 [==============================] - 0s 1000us/step - loss: 62.9802\n",
-      "Root Mean Squared Error: 7.936009544951329\n"
+      "8/8 [==============================] - 0s 714us/step - loss: 50.3341\n",
+      "Root Mean Squared Error: 7.094656058773051\n"
      ]
     }
    ],
@@ -271,7 +474,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,
diff --git a/model_v2.ipynb b/model_v2.ipynb
new file mode 100644
index 0000000..6c61c70
--- /dev/null
+++ b/model_v2.ipynb
@@ -0,0 +1,533 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5py.File('./data/TrainingData.h5', 'r') as f:\n",
+    "    # Access the trip dataset and their corresponding timestamps\n",
+    "    traffic_data = f['trip'][()]\n",
+    "    dates = f['timeslot'][()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "formatted_dates = []\n",
+    "\n",
+    "for date_string in dates:\n",
+    "    formatted_date = datetime.strptime(date_string.decode(), '%Y%m%d%H%M')\n",
+    "\n",
+    "    year = formatted_date.year\n",
+    "    month = formatted_date.month\n",
+    "    day = formatted_date.day\n",
+    "    hour = formatted_date.hour\n",
+    "    minute = formatted_date.minute\n",
+    "\n",
+    "    formatted_dates.append(np.array([year, month, day, hour, minute]))\n",
+    "\n",
+    "formatted_dates = np.array(formatted_dates).reshape(1488, 5, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_size = 240\n",
+    "\n",
+    "train_traffic_data = traffic_data[:-test_size]\n",
+    "test_traffic_data = traffic_data[-test_size:]\n",
+    "\n",
+    "train_formatted_dates = formatted_dates[:-test_size]\n",
+    "test_formatted_dates = formatted_dates[-test_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test = train_formatted_dates, test_formatted_dates\n",
+    "y_train, y_test = train_traffic_data, test_traffic_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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>Join_Count</th>\n",
+       "      <th>TARGET_FID</th>\n",
+       "      <th>BlockID</th>\n",
+       "      <th>BikeLane_miles</th>\n",
+       "      <th>AADT</th>\n",
+       "      <th>SpeedLimit</th>\n",
+       "      <th>AvgSpeed</th>\n",
+       "      <th>Pop2019</th>\n",
+       "      <th>Pop_Density</th>\n",
+       "      <th>Shape_Length</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>StationNum</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>32638.666667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7840.490288</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7840.686448</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.807055</td>\n",
+       "      <td>21234.741935</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1049.0</td>\n",
+       "      <td>0.029594</td>\n",
+       "      <td>7840.882970</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.096221</td>\n",
+       "      <td>5915.125000</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1816.0</td>\n",
+       "      <td>0.030252</td>\n",
+       "      <td>7841.080477</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.984320</td>\n",
+       "      <td>23940.078947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1892.0</td>\n",
+       "      <td>0.022647</td>\n",
+       "      <td>7841.277327</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>3</td>\n",
+       "      <td>124</td>\n",
+       "      <td>124</td>\n",
+       "      <td>1.955707</td>\n",
+       "      <td>9050.487805</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1986.0</td>\n",
+       "      <td>0.046045</td>\n",
+       "      <td>7840.280937</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>3</td>\n",
+       "      <td>125</td>\n",
+       "      <td>125</td>\n",
+       "      <td>1.440449</td>\n",
+       "      <td>9121.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1198.0</td>\n",
+       "      <td>0.039726</td>\n",
+       "      <td>7840.085072</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>2</td>\n",
+       "      <td>126</td>\n",
+       "      <td>126</td>\n",
+       "      <td>0.985704</td>\n",
+       "      <td>8770.901961</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>555.0</td>\n",
+       "      <td>0.015125</td>\n",
+       "      <td>7839.890518</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>4</td>\n",
+       "      <td>127</td>\n",
+       "      <td>127</td>\n",
+       "      <td>0.228593</td>\n",
+       "      <td>12266.085714</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>814.0</td>\n",
+       "      <td>0.031125</td>\n",
+       "      <td>7839.696621</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>4</td>\n",
+       "      <td>128</td>\n",
+       "      <td>128</td>\n",
+       "      <td>1.619940</td>\n",
+       "      <td>11077.037037</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1385.0</td>\n",
+       "      <td>0.059671</td>\n",
+       "      <td>7839.501980</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>128 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Join_Count  TARGET_FID  BlockID  BikeLane_miles          AADT  \\\n",
+       "0             0           1        1             NaN  32638.666667   \n",
+       "1             0           2        2             NaN           NaN   \n",
+       "2             3           3        3        0.807055  21234.741935   \n",
+       "3             3           4        4        2.096221   5915.125000   \n",
+       "4             3           5        5        0.984320  23940.078947   \n",
+       "..          ...         ...      ...             ...           ...   \n",
+       "123           3         124      124        1.955707   9050.487805   \n",
+       "124           3         125      125        1.440449   9121.500000   \n",
+       "125           2         126      126        0.985704   8770.901961   \n",
+       "126           4         127      127        0.228593  12266.085714   \n",
+       "127           4         128      128        1.619940  11077.037037   \n",
+       "\n",
+       "     SpeedLimit  AvgSpeed  Pop2019  Pop_Density  Shape_Length    Shape_Area  \\\n",
+       "0           NaN       NaN      0.0     0.000000   7840.490288  3.787844e+06   \n",
+       "1           NaN       NaN      0.0     0.000000   7840.686448  3.787843e+06   \n",
+       "2          25.0      15.0   1049.0     0.029594   7840.882970  3.787843e+06   \n",
+       "3          25.0      19.0   1816.0     0.030252   7841.080477  3.787844e+06   \n",
+       "4           NaN       NaN   1892.0     0.022647   7841.277327  3.787843e+06   \n",
+       "..          ...       ...      ...          ...           ...           ...   \n",
+       "123         NaN       NaN   1986.0     0.046045   7840.280937  3.787843e+06   \n",
+       "124         NaN       NaN   1198.0     0.039726   7840.085072  3.787843e+06   \n",
+       "125         NaN       NaN    555.0     0.015125   7839.890518  3.787843e+06   \n",
+       "126        25.0      16.0    814.0     0.031125   7839.696621  3.787844e+06   \n",
+       "127        25.0      16.0   1385.0     0.059671   7839.501980  3.787843e+06   \n",
+       "\n",
+       "     StationNum  \n",
+       "0             0  \n",
+       "1             0  \n",
+       "2             3  \n",
+       "3             3  \n",
+       "4             3  \n",
+       "..          ...  \n",
+       "123           3  \n",
+       "124           3  \n",
+       "125           2  \n",
+       "126           4  \n",
+       "127           4  \n",
+       "\n",
+       "[128 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spatio_data_file = \"./data/SpatialFeatures.csv\"\n",
+    "raw_spatio_feature = pd.read_csv(spatio_data_file)\n",
+    "raw_spatio_feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeLane_matrix = raw_spatio_feature[\"BikeLane_miles\"]\n",
+    "bikeLane_matrix = bikeLane_matrix.fillna(0).to_numpy()\n",
+    "bikeLane_matrix = bikeLane_matrix/bikeLane_matrix.max()\n",
+    "bikeLane_matrix_board = bikeLane_matrix[np.newaxis,:] * np.ones((1248,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Graph disconnected: cannot obtain value for tensor KerasTensor(type_spec=TensorSpec(shape=(None, 128), dtype=tf.float32, name='input_31'), name='input_31', description=\"created by layer 'input_31'\") at layer \"concatenate_16\". The following previous layers were accessed without issue: ['lstm_25', 'dense_23']",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[60], line 24\u001b[0m\n\u001b[0;32m     20\u001b[0m x \u001b[38;5;241m=\u001b[39m layers\u001b[38;5;241m.\u001b[39mDense(\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m16\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m8\u001b[39m,activation\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m\"\u001b[39m) (x)\n\u001b[0;32m     22\u001b[0m output \u001b[38;5;241m=\u001b[39m layers\u001b[38;5;241m.\u001b[39mReshape((\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m16\u001b[39m,\u001b[38;5;241m8\u001b[39m))(x)\n\u001b[1;32m---> 24\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mleft_inputs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32md:\\Anoconda\\envs\\py38\\lib\\site-packages\\tensorflow\\python\\trackable\\base.py:204\u001b[0m, in \u001b[0;36mno_automatic_dependency_tracking.<locals>._method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    202\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_self_setattr_tracking \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m    203\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 204\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    205\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    206\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_self_setattr_tracking \u001b[38;5;241m=\u001b[39m previous_value  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n",
+      "File \u001b[1;32md:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\functional.py:167\u001b[0m, in \u001b[0;36mFunctional.__init__\u001b[1;34m(self, inputs, outputs, name, trainable, **kwargs)\u001b[0m\n\u001b[0;32m    158\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\n\u001b[0;32m    159\u001b[0m         [\n\u001b[0;32m    160\u001b[0m             functional_utils\u001b[38;5;241m.\u001b[39mis_input_keras_tensor(t)\n\u001b[0;32m    161\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mnest\u001b[38;5;241m.\u001b[39mflatten(inputs)\n\u001b[0;32m    162\u001b[0m         ]\n\u001b[0;32m    163\u001b[0m     ):\n\u001b[0;32m    164\u001b[0m         inputs, outputs \u001b[38;5;241m=\u001b[39m functional_utils\u001b[38;5;241m.\u001b[39mclone_graph_nodes(\n\u001b[0;32m    165\u001b[0m             inputs, outputs\n\u001b[0;32m    166\u001b[0m         )\n\u001b[1;32m--> 167\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_graph_network\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32md:\\Anoconda\\envs\\py38\\lib\\site-packages\\tensorflow\\python\\trackable\\base.py:204\u001b[0m, in \u001b[0;36mno_automatic_dependency_tracking.<locals>._method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    202\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_self_setattr_tracking \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m    203\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 204\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    205\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    206\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_self_setattr_tracking \u001b[38;5;241m=\u001b[39m previous_value  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n",
+      "File \u001b[1;32md:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\functional.py:266\u001b[0m, in \u001b[0;36mFunctional._init_graph_network\u001b[1;34m(self, inputs, outputs)\u001b[0m\n\u001b[0;32m    263\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_input_coordinates\u001b[38;5;241m.\u001b[39mappend((layer, node_index, tensor_index))\n\u001b[0;32m    265\u001b[0m \u001b[38;5;66;03m# Keep track of the network's nodes and layers.\u001b[39;00m\n\u001b[1;32m--> 266\u001b[0m nodes, nodes_by_depth, layers, _ \u001b[38;5;241m=\u001b[39m \u001b[43m_map_graph_network\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutputs\u001b[49m\n\u001b[0;32m    268\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    269\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_network_nodes \u001b[38;5;241m=\u001b[39m nodes\n\u001b[0;32m    270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_nodes_by_depth \u001b[38;5;241m=\u001b[39m nodes_by_depth\n",
+      "File \u001b[1;32md:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\functional.py:1142\u001b[0m, in \u001b[0;36m_map_graph_network\u001b[1;34m(inputs, outputs)\u001b[0m\n\u001b[0;32m   1140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mnest\u001b[38;5;241m.\u001b[39mflatten(node\u001b[38;5;241m.\u001b[39mkeras_inputs):\n\u001b[0;32m   1141\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mid\u001b[39m(x) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m computable_tensors:\n\u001b[1;32m-> 1142\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1143\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGraph disconnected: cannot obtain value for \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1144\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtensor \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m at layer \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlayer\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   1145\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe following previous layers were accessed \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1146\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwithout issue: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlayers_with_complete_input\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1147\u001b[0m         )\n\u001b[0;32m   1148\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mnest\u001b[38;5;241m.\u001b[39mflatten(node\u001b[38;5;241m.\u001b[39moutputs):\n\u001b[0;32m   1149\u001b[0m     computable_tensors\u001b[38;5;241m.\u001b[39madd(\u001b[38;5;28mid\u001b[39m(x))\n",
+      "\u001b[1;31mValueError\u001b[0m: Graph disconnected: cannot obtain value for tensor KerasTensor(type_spec=TensorSpec(shape=(None, 128), dtype=tf.float32, name='input_31'), name='input_31', description=\"created by layer 'input_31'\") at layer \"concatenate_16\". The following previous layers were accessed without issue: ['lstm_25', 'dense_23']"
+     ]
+    }
+   ],
+   "source": [
+    "import keras\n",
+    "from keras import layers\n",
+    "from keras import models\n",
+    "\n",
+    "spatio_feature = None\n",
+    "\n",
+    "input_shape = (5,1)\n",
+    "\n",
+    "left_inputs = layers.Input(shape = (input_shape))\n",
+    "\n",
+    "right_inputs = layers.Input(shape = (128))\n",
+    "\n",
+    "x = layers.LSTM(128, activation = \"tanh\")(left_inputs)\n",
+    "\n",
+    "x = layers.Dense(2*16*8, activation = \"linear\")(x)\n",
+    "\n",
+    "\n",
+    "x = layers.concatenate([x,right_inputs])\n",
+    "\n",
+    "x = layers.Dense(2*16*8,activation= \"relu\") (x)\n",
+    "\n",
+    "output = layers.Reshape((2,16,8))(x)\n",
+    "\n",
+    "model = models.Model([left_inputs],outputs = output)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_7\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_24 (InputLayer)       [(None, 5, 1)]            0         \n",
+      "                                                                 \n",
+      " lstm_22 (LSTM)              (None, 128)               66560     \n",
+      "                                                                 \n",
+      " dense_17 (Dense)            (None, 256)               33024     \n",
+      "                                                                 \n",
+      " concatenate_13 (Concatenat  (2, 384)                  0         \n",
+      " e)                                                              \n",
+      "                                                                 \n",
+      " dense_18 (Dense)            (2, 256)                  98560     \n",
+      "                                                                 \n",
+      " reshape_8 (Reshape)         (2, 2, 16, 8)             0         \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 198144 (774.00 KB)\n",
+      "Trainable params: 198144 (774.00 KB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "in user code:\n\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1338, in train_function  *\n        return step_function(self, iterator)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1322, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1303, in run_step  **\n        outputs = model.train_step(data)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1080, in train_step\n        y_pred = self(x, training=True)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\backend.py\", line 3582, in concatenate\n        return tf.concat([to_dense(x) for x in tensors], axis)\n\n    ValueError: Exception encountered when calling layer 'concatenate_13' (type Concatenate).\n    \n    Dimension 0 in both shapes must be equal, but are 32 and 2. Shapes are [32] and [2]. for '{{node model_7/concatenate_13/concat}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](model_7/dense_17/BiasAdd, model_7/concatenate_13/Cast, model_7/concatenate_13/concat/axis)' with input shapes: [32,256], [2,128], [] and with computed input tensors: input[2] = <1>.\n    \n    Call arguments received by layer 'concatenate_13' (type Concatenate):\n      • inputs=['tf.Tensor(shape=(32, 256), dtype=float32)', 'tf.Tensor(shape=(2, 128), dtype=float32)']\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[55], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmse\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32md:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_fileootg_h1c.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1338, in train_function  *\n        return step_function(self, iterator)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1322, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1303, in run_step  **\n        outputs = model.train_step(data)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 1080, in train_step\n        y_pred = self(x, training=True)\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"d:\\Anoconda\\envs\\py38\\lib\\site-packages\\keras\\src\\backend.py\", line 3582, in concatenate\n        return tf.concat([to_dense(x) for x in tensors], axis)\n\n    ValueError: Exception encountered when calling layer 'concatenate_13' (type Concatenate).\n    \n    Dimension 0 in both shapes must be equal, but are 32 and 2. Shapes are [32] and [2]. for '{{node model_7/concatenate_13/concat}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](model_7/dense_17/BiasAdd, model_7/concatenate_13/Cast, model_7/concatenate_13/concat/axis)' with input shapes: [32,256], [2,128], [] and with computed input tensors: input[2] = <1>.\n    \n    Call arguments received by layer 'concatenate_13' (type Concatenate):\n      • inputs=['tf.Tensor(shape=(32, 256), dtype=float32)', 'tf.Tensor(shape=(2, 128), dtype=float32)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='adam', loss='mse')\n",
+    "model.fit([X_train], y_train, epochs=50, batch_size=32, validation_data=([X_test], y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8/8 [==============================] - 0s 1ms/step - loss: 56.9645\n",
+      "Root Mean Squared Error: 7.5474830524649805\n"
+     ]
+    }
+   ],
+   "source": [
+    "mse = model.evaluate(X_test, y_test)\n",
+    "\n",
+    "# Show rmse to see how model performs on the test set\n",
+    "rmse = np.sqrt(mse)\n",
+    "print(f'Root Mean Squared Error: {rmse}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py38",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/model_v3_yshape.ipynb b/model_v3_yshape.ipynb
new file mode 100644
index 0000000..e901e48
--- /dev/null
+++ b/model_v3_yshape.ipynb
@@ -0,0 +1,628 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5py.File('./data/TrainingData.h5', 'r') as f:\n",
+    "    # Access the trip dataset and their corresponding timestamps\n",
+    "    traffic_data = f['trip'][()]\n",
+    "    dates = f['timeslot'][()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "formatted_dates = []\n",
+    "\n",
+    "for date_string in dates:\n",
+    "    formatted_date = datetime.strptime(date_string.decode(), '%Y%m%d%H%M')\n",
+    "\n",
+    "    year = formatted_date.year\n",
+    "    month = formatted_date.month\n",
+    "    day = formatted_date.day\n",
+    "    hour = formatted_date.hour\n",
+    "    minute = formatted_date.minute\n",
+    "\n",
+    "    formatted_dates.append(np.array([year, month, day, hour, minute]))\n",
+    "\n",
+    "formatted_dates = np.array(formatted_dates).reshape(1488, 5, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_size = 240\n",
+    "\n",
+    "train_traffic_data = traffic_data[:-test_size]\n",
+    "test_traffic_data = traffic_data[-test_size:]\n",
+    "\n",
+    "train_formatted_dates = formatted_dates[:-test_size]\n",
+    "test_formatted_dates = formatted_dates[-test_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test = train_formatted_dates, test_formatted_dates\n",
+    "y_train, y_test = train_traffic_data, test_traffic_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Join_Count</th>\n",
+       "      <th>TARGET_FID</th>\n",
+       "      <th>BlockID</th>\n",
+       "      <th>BikeLane_miles</th>\n",
+       "      <th>AADT</th>\n",
+       "      <th>SpeedLimit</th>\n",
+       "      <th>AvgSpeed</th>\n",
+       "      <th>Pop2019</th>\n",
+       "      <th>Pop_Density</th>\n",
+       "      <th>Shape_Length</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>StationNum</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.807055</td>\n",
+       "      <td>21234.741935</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1049.0</td>\n",
+       "      <td>0.029594</td>\n",
+       "      <td>7840.882970</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.096221</td>\n",
+       "      <td>5915.125000</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>19.0</td>\n",
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+       "      <td>0.030252</td>\n",
+       "      <td>7841.080477</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.984320</td>\n",
+       "      <td>23940.078947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1892.0</td>\n",
+       "      <td>0.022647</td>\n",
+       "      <td>7841.277327</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>3</td>\n",
+       "      <td>124</td>\n",
+       "      <td>124</td>\n",
+       "      <td>1.955707</td>\n",
+       "      <td>9050.487805</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1986.0</td>\n",
+       "      <td>0.046045</td>\n",
+       "      <td>7840.280937</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>3</td>\n",
+       "      <td>125</td>\n",
+       "      <td>125</td>\n",
+       "      <td>1.440449</td>\n",
+       "      <td>9121.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1198.0</td>\n",
+       "      <td>0.039726</td>\n",
+       "      <td>7840.085072</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>2</td>\n",
+       "      <td>126</td>\n",
+       "      <td>126</td>\n",
+       "      <td>0.985704</td>\n",
+       "      <td>8770.901961</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>555.0</td>\n",
+       "      <td>0.015125</td>\n",
+       "      <td>7839.890518</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>4</td>\n",
+       "      <td>127</td>\n",
+       "      <td>127</td>\n",
+       "      <td>0.228593</td>\n",
+       "      <td>12266.085714</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>814.0</td>\n",
+       "      <td>0.031125</td>\n",
+       "      <td>7839.696621</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>4</td>\n",
+       "      <td>128</td>\n",
+       "      <td>128</td>\n",
+       "      <td>1.619940</td>\n",
+       "      <td>11077.037037</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1385.0</td>\n",
+       "      <td>0.059671</td>\n",
+       "      <td>7839.501980</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>128 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Join_Count  TARGET_FID  BlockID  BikeLane_miles          AADT  \\\n",
+       "0             0           1        1             NaN  32638.666667   \n",
+       "1             0           2        2             NaN           NaN   \n",
+       "2             3           3        3        0.807055  21234.741935   \n",
+       "3             3           4        4        2.096221   5915.125000   \n",
+       "4             3           5        5        0.984320  23940.078947   \n",
+       "..          ...         ...      ...             ...           ...   \n",
+       "123           3         124      124        1.955707   9050.487805   \n",
+       "124           3         125      125        1.440449   9121.500000   \n",
+       "125           2         126      126        0.985704   8770.901961   \n",
+       "126           4         127      127        0.228593  12266.085714   \n",
+       "127           4         128      128        1.619940  11077.037037   \n",
+       "\n",
+       "     SpeedLimit  AvgSpeed  Pop2019  Pop_Density  Shape_Length    Shape_Area  \\\n",
+       "0           NaN       NaN      0.0     0.000000   7840.490288  3.787844e+06   \n",
+       "1           NaN       NaN      0.0     0.000000   7840.686448  3.787843e+06   \n",
+       "2          25.0      15.0   1049.0     0.029594   7840.882970  3.787843e+06   \n",
+       "3          25.0      19.0   1816.0     0.030252   7841.080477  3.787844e+06   \n",
+       "4           NaN       NaN   1892.0     0.022647   7841.277327  3.787843e+06   \n",
+       "..          ...       ...      ...          ...           ...           ...   \n",
+       "123         NaN       NaN   1986.0     0.046045   7840.280937  3.787843e+06   \n",
+       "124         NaN       NaN   1198.0     0.039726   7840.085072  3.787843e+06   \n",
+       "125         NaN       NaN    555.0     0.015125   7839.890518  3.787843e+06   \n",
+       "126        25.0      16.0    814.0     0.031125   7839.696621  3.787844e+06   \n",
+       "127        25.0      16.0   1385.0     0.059671   7839.501980  3.787843e+06   \n",
+       "\n",
+       "     StationNum  \n",
+       "0             0  \n",
+       "1             0  \n",
+       "2             3  \n",
+       "3             3  \n",
+       "4             3  \n",
+       "..          ...  \n",
+       "123           3  \n",
+       "124           3  \n",
+       "125           2  \n",
+       "126           4  \n",
+       "127           4  \n",
+       "\n",
+       "[128 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spatio_data_file = \"./data/SpatialFeatures.csv\"\n",
+    "raw_spatio_feature = pd.read_csv(spatio_data_file)\n",
+    "raw_spatio_feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeLane_matrix = raw_spatio_feature[\"BikeLane_miles\"]\n",
+    "bikeLane_matrix = bikeLane_matrix.fillna(0).to_numpy()\n",
+    "bikeLane_matrix = bikeLane_matrix/bikeLane_matrix.max()\n",
+    "bikeLane_matrix_board = bikeLane_matrix[np.newaxis,:] * np.ones((1248,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keras\n",
+    "from keras import layers\n",
+    "from keras import models\n",
+    "\n",
+    "spatio_feature = None\n",
+    "\n",
+    "input_shape = (5,1)\n",
+    "\n",
+    "left_inputs = layers.Input(shape = (input_shape))\n",
+    "\n",
+    "x = layers.LSTM(128, activation = \"tanh\")(left_inputs)\n",
+    "\n",
+    "x = layers.Dense(2*16*8, activation = \"linear\")(x)\n",
+    "\n",
+    "right_inputs = layers.Input(shape = (input_shape))\n",
+    "\n",
+    "y = layers.LSTM(128,activation= \"tanh\")(right_inputs)\n",
+    "\n",
+    "y = layers.Dense(2*16*8,activation= \"relu\")(y)\n",
+    "\n",
+    "x = layers.concatenate([x,y])\n",
+    "\n",
+    "x = layers.Dense(2*16*8,activation= \"relu\") (x)\n",
+    "\n",
+    "output = layers.Reshape((2,16,8))(x)\n",
+    "\n",
+    "model = models.Model([left_inputs,right_inputs],outputs = output)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_2\"\n",
+      "__________________________________________________________________________________________________\n",
+      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
+      "==================================================================================================\n",
+      " input_5 (InputLayer)        [(None, 5, 1)]               0         []                            \n",
+      "                                                                                                  \n",
+      " input_6 (InputLayer)        [(None, 5, 1)]               0         []                            \n",
+      "                                                                                                  \n",
+      " lstm_4 (LSTM)               (None, 128)                  66560     ['input_5[0][0]']             \n",
+      "                                                                                                  \n",
+      " lstm_5 (LSTM)               (None, 128)                  66560     ['input_6[0][0]']             \n",
+      "                                                                                                  \n",
+      " dense_6 (Dense)             (None, 256)                  33024     ['lstm_4[0][0]']              \n",
+      "                                                                                                  \n",
+      " dense_7 (Dense)             (None, 256)                  33024     ['lstm_5[0][0]']              \n",
+      "                                                                                                  \n",
+      " concatenate_2 (Concatenate  (None, 512)                  0         ['dense_6[0][0]',             \n",
+      " )                                                                   'dense_7[0][0]']             \n",
+      "                                                                                                  \n",
+      " dense_8 (Dense)             (None, 256)                  131328    ['concatenate_2[0][0]']       \n",
+      "                                                                                                  \n",
+      " reshape_2 (Reshape)         (None, 2, 16, 8)             0         ['dense_8[0][0]']             \n",
+      "                                                                                                  \n",
+      "==================================================================================================\n",
+      "Total params: 330496 (1.26 MB)\n",
+      "Trainable params: 330496 (1.26 MB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "39/39 [==============================] - 2s 15ms/step - loss: 54.8777 - val_loss: 51.2985\n",
+      "Epoch 2/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 55.1405 - val_loss: 54.7360\n",
+      "Epoch 3/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 55.2071 - val_loss: 52.4979\n",
+      "Epoch 4/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 53.2430 - val_loss: 64.0301\n",
+      "Epoch 5/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 53.3346 - val_loss: 49.7368\n",
+      "Epoch 6/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 53.5821 - val_loss: 53.5882\n",
+      "Epoch 7/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 54.0866 - val_loss: 57.8746\n",
+      "Epoch 8/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 55.6869 - val_loss: 64.1826\n",
+      "Epoch 9/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 53.7663 - val_loss: 61.7465\n",
+      "Epoch 10/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 54.7494 - val_loss: 65.6936\n",
+      "Epoch 11/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 53.1544 - val_loss: 52.5802\n",
+      "Epoch 12/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 55.2133 - val_loss: 56.6710\n",
+      "Epoch 13/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 54.6228 - val_loss: 55.5211\n",
+      "Epoch 14/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 52.1401 - val_loss: 58.5715\n",
+      "Epoch 15/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 52.5612 - val_loss: 58.7202\n",
+      "Epoch 16/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 53.4948 - val_loss: 59.2302\n",
+      "Epoch 17/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 52.5986 - val_loss: 48.9652\n",
+      "Epoch 18/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 51.9470 - val_loss: 64.2186\n",
+      "Epoch 19/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 55.1345 - val_loss: 53.9188\n",
+      "Epoch 20/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 52.8832 - val_loss: 66.7527\n",
+      "Epoch 21/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 53.5199 - val_loss: 48.4578\n",
+      "Epoch 22/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 53.8766 - val_loss: 51.3250\n",
+      "Epoch 23/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 50.4563 - val_loss: 60.5626\n",
+      "Epoch 24/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 51.8139 - val_loss: 52.1807\n",
+      "Epoch 25/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 49.4824 - val_loss: 60.1553\n",
+      "Epoch 26/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 49.2602 - val_loss: 60.9033\n",
+      "Epoch 27/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 51.0772 - val_loss: 58.1736\n",
+      "Epoch 28/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 49.2613 - val_loss: 58.5493\n",
+      "Epoch 29/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 49.6795 - val_loss: 65.7093\n",
+      "Epoch 30/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 50.2725 - val_loss: 58.2873\n",
+      "Epoch 31/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 52.4984 - val_loss: 65.8380\n",
+      "Epoch 32/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 52.5961 - val_loss: 63.5864\n",
+      "Epoch 33/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 49.4923 - val_loss: 51.8561\n",
+      "Epoch 34/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 51.5506 - val_loss: 60.6100\n",
+      "Epoch 35/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 48.5454 - val_loss: 70.8238\n",
+      "Epoch 36/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 49.0989 - val_loss: 67.8033\n",
+      "Epoch 37/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 48.3473 - val_loss: 50.0989\n",
+      "Epoch 38/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 48.2508 - val_loss: 54.6217\n",
+      "Epoch 39/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 48.6460 - val_loss: 65.7264\n",
+      "Epoch 40/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 48.6715 - val_loss: 66.6947\n",
+      "Epoch 41/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 49.4361 - val_loss: 50.9359\n",
+      "Epoch 42/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 47.1807 - val_loss: 55.0176\n",
+      "Epoch 43/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 51.3290 - val_loss: 45.2042\n",
+      "Epoch 44/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 48.1886 - val_loss: 48.6589\n",
+      "Epoch 45/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 47.7480 - val_loss: 54.7440\n",
+      "Epoch 46/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 47.5081 - val_loss: 56.8295\n",
+      "Epoch 47/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 46.0584 - val_loss: 50.8201\n",
+      "Epoch 48/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 45.7069 - val_loss: 52.5394\n",
+      "Epoch 49/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 46.6425 - val_loss: 52.1247\n",
+      "Epoch 50/50\n",
+      "39/39 [==============================] - 0s 4ms/step - loss: 47.2073 - val_loss: 51.9383\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x1684bb4d7f0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='adam', loss='mse')\n",
+    "model.fit([X_train,X_train], y_train, epochs=50, batch_size=32, validation_data=([X_test,X_test], y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8/8 [==============================] - 0s 1ms/step - loss: 51.9383\n",
+      "Root Mean Squared Error: 7.206825348515386\n"
+     ]
+    }
+   ],
+   "source": [
+    "mse = model.evaluate([X_test,X_test], y_test)\n",
+    "\n",
+    "# Show rmse to see how model performs on the test set\n",
+    "rmse = np.sqrt(mse)\n",
+    "print(f'Root Mean Squared Error: {rmse}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py38",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/model_v4_static_feature.ipynb b/model_v4_static_feature.ipynb
new file mode 100644
index 0000000..28769ad
--- /dev/null
+++ b/model_v4_static_feature.ipynb
@@ -0,0 +1,634 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5py.File('./data/TrainingData.h5', 'r') as f:\n",
+    "    # Access the trip dataset and their corresponding timestamps\n",
+    "    traffic_data = f['trip'][()]\n",
+    "    dates = f['timeslot'][()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "formatted_dates = []\n",
+    "\n",
+    "for date_string in dates:\n",
+    "    formatted_date = datetime.strptime(date_string.decode(), '%Y%m%d%H%M')\n",
+    "\n",
+    "    year = formatted_date.year\n",
+    "    month = formatted_date.month\n",
+    "    day = formatted_date.day\n",
+    "    hour = formatted_date.hour\n",
+    "    minute = formatted_date.minute\n",
+    "\n",
+    "    formatted_dates.append(np.array([year, month, day, hour, minute]))\n",
+    "\n",
+    "formatted_dates = np.array(formatted_dates).reshape(1488, 5, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_size = 240\n",
+    "\n",
+    "train_traffic_data = traffic_data[:-test_size]\n",
+    "test_traffic_data = traffic_data[-test_size:]\n",
+    "\n",
+    "train_formatted_dates = formatted_dates[:-test_size]\n",
+    "test_formatted_dates = formatted_dates[-test_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test = train_formatted_dates, test_formatted_dates\n",
+    "y_train, y_test = train_traffic_data, test_traffic_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Join_Count</th>\n",
+       "      <th>TARGET_FID</th>\n",
+       "      <th>BlockID</th>\n",
+       "      <th>BikeLane_miles</th>\n",
+       "      <th>AADT</th>\n",
+       "      <th>SpeedLimit</th>\n",
+       "      <th>AvgSpeed</th>\n",
+       "      <th>Pop2019</th>\n",
+       "      <th>Pop_Density</th>\n",
+       "      <th>Shape_Length</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>StationNum</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>32638.666667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7840.490288</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7840.686448</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.807055</td>\n",
+       "      <td>21234.741935</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1049.0</td>\n",
+       "      <td>0.029594</td>\n",
+       "      <td>7840.882970</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.096221</td>\n",
+       "      <td>5915.125000</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1816.0</td>\n",
+       "      <td>0.030252</td>\n",
+       "      <td>7841.080477</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.984320</td>\n",
+       "      <td>23940.078947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1892.0</td>\n",
+       "      <td>0.022647</td>\n",
+       "      <td>7841.277327</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>3</td>\n",
+       "      <td>124</td>\n",
+       "      <td>124</td>\n",
+       "      <td>1.955707</td>\n",
+       "      <td>9050.487805</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1986.0</td>\n",
+       "      <td>0.046045</td>\n",
+       "      <td>7840.280937</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>3</td>\n",
+       "      <td>125</td>\n",
+       "      <td>125</td>\n",
+       "      <td>1.440449</td>\n",
+       "      <td>9121.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1198.0</td>\n",
+       "      <td>0.039726</td>\n",
+       "      <td>7840.085072</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>2</td>\n",
+       "      <td>126</td>\n",
+       "      <td>126</td>\n",
+       "      <td>0.985704</td>\n",
+       "      <td>8770.901961</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>555.0</td>\n",
+       "      <td>0.015125</td>\n",
+       "      <td>7839.890518</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>4</td>\n",
+       "      <td>127</td>\n",
+       "      <td>127</td>\n",
+       "      <td>0.228593</td>\n",
+       "      <td>12266.085714</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>814.0</td>\n",
+       "      <td>0.031125</td>\n",
+       "      <td>7839.696621</td>\n",
+       "      <td>3.787844e+06</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>4</td>\n",
+       "      <td>128</td>\n",
+       "      <td>128</td>\n",
+       "      <td>1.619940</td>\n",
+       "      <td>11077.037037</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1385.0</td>\n",
+       "      <td>0.059671</td>\n",
+       "      <td>7839.501980</td>\n",
+       "      <td>3.787843e+06</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>128 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Join_Count  TARGET_FID  BlockID  BikeLane_miles          AADT  \\\n",
+       "0             0           1        1             NaN  32638.666667   \n",
+       "1             0           2        2             NaN           NaN   \n",
+       "2             3           3        3        0.807055  21234.741935   \n",
+       "3             3           4        4        2.096221   5915.125000   \n",
+       "4             3           5        5        0.984320  23940.078947   \n",
+       "..          ...         ...      ...             ...           ...   \n",
+       "123           3         124      124        1.955707   9050.487805   \n",
+       "124           3         125      125        1.440449   9121.500000   \n",
+       "125           2         126      126        0.985704   8770.901961   \n",
+       "126           4         127      127        0.228593  12266.085714   \n",
+       "127           4         128      128        1.619940  11077.037037   \n",
+       "\n",
+       "     SpeedLimit  AvgSpeed  Pop2019  Pop_Density  Shape_Length    Shape_Area  \\\n",
+       "0           NaN       NaN      0.0     0.000000   7840.490288  3.787844e+06   \n",
+       "1           NaN       NaN      0.0     0.000000   7840.686448  3.787843e+06   \n",
+       "2          25.0      15.0   1049.0     0.029594   7840.882970  3.787843e+06   \n",
+       "3          25.0      19.0   1816.0     0.030252   7841.080477  3.787844e+06   \n",
+       "4           NaN       NaN   1892.0     0.022647   7841.277327  3.787843e+06   \n",
+       "..          ...       ...      ...          ...           ...           ...   \n",
+       "123         NaN       NaN   1986.0     0.046045   7840.280937  3.787843e+06   \n",
+       "124         NaN       NaN   1198.0     0.039726   7840.085072  3.787843e+06   \n",
+       "125         NaN       NaN    555.0     0.015125   7839.890518  3.787843e+06   \n",
+       "126        25.0      16.0    814.0     0.031125   7839.696621  3.787844e+06   \n",
+       "127        25.0      16.0   1385.0     0.059671   7839.501980  3.787843e+06   \n",
+       "\n",
+       "     StationNum  \n",
+       "0             0  \n",
+       "1             0  \n",
+       "2             3  \n",
+       "3             3  \n",
+       "4             3  \n",
+       "..          ...  \n",
+       "123           3  \n",
+       "124           3  \n",
+       "125           2  \n",
+       "126           4  \n",
+       "127           4  \n",
+       "\n",
+       "[128 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spatio_data_file = \"./data/SpatialFeatures.csv\"\n",
+    "raw_spatio_feature = pd.read_csv(spatio_data_file)\n",
+    "raw_spatio_feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeLane_matrix = raw_spatio_feature[\"BikeLane_miles\"]\n",
+    "bikeLane_matrix = bikeLane_matrix.fillna(0).to_numpy()\n",
+    "bikeLane_matrix = bikeLane_matrix/bikeLane_matrix.max()\n",
+    "bikeLane_matrix_board = bikeLane_matrix[np.newaxis,:] * np.ones((1248,1))\n",
+    "\n",
+    "\n",
+    "static_input = bikeLane_matrix_board\n",
+    "static_input_test = bikeLane_matrix[np.newaxis,:] * np.ones((240,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keras\n",
+    "from keras import layers\n",
+    "from keras import models\n",
+    "\n",
+    "spatio_feature = None\n",
+    "\n",
+    "input_shape = (5,1)\n",
+    "\n",
+    "static_input_shape = (128)\n",
+    "\n",
+    "left_inputs = layers.Input(shape = (input_shape))\n",
+    "\n",
+    "x = layers.LSTM(128, activation = \"tanh\")(left_inputs)\n",
+    "\n",
+    "# x = layers.Dense(16*8, activation = \"linear\")(x)\n",
+    "\n",
+    "right_inputs = layers.Input(shape = (static_input_shape))\n",
+    "\n",
+    "y = right_inputs\n",
+    "\n",
+    "\n",
+    "x = layers.concatenate([x,y])\n",
+    "\n",
+    "x = layers.Dense(2*16*8,activation= \"relu\") (x)\n",
+    "\n",
+    "output = layers.Reshape((2,16,8))(x)\n",
+    "\n",
+    "model = models.Model([left_inputs,right_inputs],outputs = output)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_2\"\n",
+      "__________________________________________________________________________________________________\n",
+      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
+      "==================================================================================================\n",
+      " input_9 (InputLayer)        [(None, 5, 1)]               0         []                            \n",
+      "                                                                                                  \n",
+      " lstm_4 (LSTM)               (None, 128)                  66560     ['input_9[0][0]']             \n",
+      "                                                                                                  \n",
+      " input_10 (InputLayer)       [(None, 128)]                0         []                            \n",
+      "                                                                                                  \n",
+      " concatenate_4 (Concatenate  (None, 256)                  0         ['lstm_4[0][0]',              \n",
+      " )                                                                   'input_10[0][0]']            \n",
+      "                                                                                                  \n",
+      " dense_6 (Dense)             (None, 256)                  65792     ['concatenate_4[0][0]']       \n",
+      "                                                                                                  \n",
+      " reshape_2 (Reshape)         (None, 2, 16, 8)             0         ['dense_6[0][0]']             \n",
+      "                                                                                                  \n",
+      "==================================================================================================\n",
+      "Total params: 132352 (517.00 KB)\n",
+      "Trainable params: 132352 (517.00 KB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "39/39 [==============================] - 1s 7ms/step - loss: 195.3423 - val_loss: 122.7748\n",
+      "Epoch 2/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 159.8013 - val_loss: 111.0790\n",
+      "Epoch 3/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 149.2726 - val_loss: 109.4974\n",
+      "Epoch 4/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 146.6860 - val_loss: 109.9132\n",
+      "Epoch 5/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 145.9674 - val_loss: 110.0577\n",
+      "Epoch 6/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 145.7595 - val_loss: 110.2798\n",
+      "Epoch 7/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 145.6669 - val_loss: 110.4378\n",
+      "Epoch 8/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 145.5251 - val_loss: 110.3182\n",
+      "Epoch 9/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 145.5142 - val_loss: 110.0210\n",
+      "Epoch 10/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 145.2310 - val_loss: 109.9615\n",
+      "Epoch 11/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 143.9531 - val_loss: 108.3879\n",
+      "Epoch 12/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 141.4673 - val_loss: 106.4769\n",
+      "Epoch 13/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 138.7005 - val_loss: 102.2574\n",
+      "Epoch 14/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 131.1469 - val_loss: 96.1004\n",
+      "Epoch 15/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 119.1949 - val_loss: 87.1408\n",
+      "Epoch 16/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 109.1737 - val_loss: 82.5326\n",
+      "Epoch 17/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 102.8606 - val_loss: 80.7252\n",
+      "Epoch 18/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 100.3107 - val_loss: 78.7766\n",
+      "Epoch 19/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 99.4495 - val_loss: 79.5350\n",
+      "Epoch 20/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 98.5605 - val_loss: 78.4435\n",
+      "Epoch 21/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 97.5860 - val_loss: 81.1774\n",
+      "Epoch 22/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 96.2666 - val_loss: 79.1634\n",
+      "Epoch 23/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 94.7172 - val_loss: 78.6962\n",
+      "Epoch 24/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 92.0947 - val_loss: 77.0280\n",
+      "Epoch 25/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 91.2972 - val_loss: 76.0381\n",
+      "Epoch 26/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 90.9750 - val_loss: 76.7729\n",
+      "Epoch 27/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 89.8379 - val_loss: 76.8481\n",
+      "Epoch 28/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 89.7722 - val_loss: 77.1863\n",
+      "Epoch 29/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 88.6945 - val_loss: 76.4976\n",
+      "Epoch 30/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 87.9703 - val_loss: 75.7619\n",
+      "Epoch 31/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 87.8983 - val_loss: 76.3760\n",
+      "Epoch 32/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 86.7892 - val_loss: 75.5612\n",
+      "Epoch 33/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 87.2548 - val_loss: 75.8207\n",
+      "Epoch 34/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 86.6262 - val_loss: 74.3852\n",
+      "Epoch 35/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 83.0383 - val_loss: 71.6389\n",
+      "Epoch 36/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 81.3606 - val_loss: 72.0317\n",
+      "Epoch 37/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 80.9233 - val_loss: 72.5051\n",
+      "Epoch 38/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 80.5564 - val_loss: 73.2485\n",
+      "Epoch 39/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 79.3356 - val_loss: 70.0708\n",
+      "Epoch 40/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 79.0890 - val_loss: 69.8045\n",
+      "Epoch 41/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 78.2170 - val_loss: 69.6172\n",
+      "Epoch 42/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 77.9804 - val_loss: 69.9331\n",
+      "Epoch 43/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 77.2316 - val_loss: 69.1138\n",
+      "Epoch 44/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 76.6667 - val_loss: 67.2575\n",
+      "Epoch 45/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 76.3032 - val_loss: 69.4175\n",
+      "Epoch 46/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 75.8204 - val_loss: 66.3227\n",
+      "Epoch 47/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 75.5402 - val_loss: 67.3108\n",
+      "Epoch 48/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 74.5297 - val_loss: 66.1790\n",
+      "Epoch 49/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 74.2953 - val_loss: 65.7092\n",
+      "Epoch 50/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 73.6489 - val_loss: 66.4811\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x22f1630acd0>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='adam', loss='mse')\n",
+    "model.fit([X_train,static_input], y_train, epochs=50, batch_size=32, validation_data=([X_test,static_input_test], y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8/8 [==============================] - 0s 1ms/step - loss: 66.4811\n",
+      "Root Mean Squared Error: 8.153597228592622\n"
+     ]
+    }
+   ],
+   "source": [
+    "mse = model.evaluate([X_test,static_input_test], y_test)\n",
+    "\n",
+    "# Show rmse to see how model performs on the test set\n",
+    "rmse = np.sqrt(mse)\n",
+    "print(f'Root Mean Squared Error: {rmse}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py38",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}