Machine_Learning/NEXT-GEN TRAFFIC MANAGEMENT SYSTEM/Next-Gen Traffic Management System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ded6c2c-4b04-4773-93ae-16a4437d1f52",
   "metadata": {},
   "source": [
    "# Next-Gen Traffic Management System (NGTMS)\r\n",
    "\r\n",
    "## Overview\r\n",
    "The Next-Gen Traffic Management System (NGTMS) is an advanced solution designed to enhance urban mobility by optimizing traffic flow, minimizing congestion, and improving travel times. By leveraging real-time data from various sources, such as traffic volume, weather conditions, and special events, NGTMS intelligently adapts to changing traffic scenarios. This system prioritizes emergency vehicles, offers alternative route suggestions, and provides visualizations to assist in traffic management decision-making.\r\n",
    "\r\n",
    "## Key Features\r\n",
    "- **Real-Time Traffic Monitoring**: Continuously tracks traffic conditions to make informed decisions.\r\n",
    "- **Emergency Vehicle Priority**: Ensures that emergency vehicles receive priority access during high-traffic situations.\r\n",
    "- **Dynamic Route Suggestions**: Offers drivers alternative routes based on current traffic conditions.\r\n",
    "- **Weather and Event Adaptation**: Adjusts traffic management strategies according to weather patterns and special events.\r\n",
    "- **Visual Analytics**: Provides insightful visualizations to showcase traffic trends and system performance.\r\n",
    "\r\n",
    "## Technical Stack\r\n",
    "### Programming Language\r\n",
    "- **Python**: The primary language used for developing the NGTMS due to its extensive libraries for data analysis, machine learning, and visualization.\r\n",
    "\r\n",
    "### Libraries\r\n",
    "- **Pandas**: For data manipulation and analysis, enabling easy handling of large datasets.\r\n",
    "- **NumPy**: For numerical computations and efficient handling of arrays.\r\n",
    "- **Matplotlib**: For creating static, interactive, and animated visualizations in Python.\r\n",
    "- **Seaborn**: For statistical data visualization, making it easier to generate informative graphics.\r\n",
    "- **Scikit-learn**: For implementing machine learning algorithms, model evaluation, and preprocessing tasks.\r\n",
    "- **TensorFlow/Keras**: For building deep learning models if advanced predictive analytics are implemented.\r\n",
    "- **Flask/Django**: For developing web applications to interface with the traffic management system.\r\n",
    "\r\n",
    "### Data Sources\r\n",
    "- **Traffic Sensors**: Real-time data from road sensors and cameras.\r\n",
    "- **Weather APIs**: To fetch current weather conditions affecting traffic.\r\n",
    "- **Event APIs**: Information about local events that could impact traffic flow.\r\n",
    "- **Geolocation Services**: To provide real-time traffic informficient roadways for all users.\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2856957-d077-433f-9a01-550eee5a1c2f",
   "metadata": {},
   "source": [
    "## Install Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0ed4ed7-4c17-4ada-977d-a76d65db6658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\asus\\anaconda3\\lib\\site-packages (3.8.0)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: seaborn in c:\\users\\asus\\anaconda3\\lib\\site-packages (0.12.2)\n",
      "Requirement already satisfied: pandas in c:\\users\\asus\\anaconda3\\lib\\site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy in c:\\users\\asus\\anaconda3\\lib\\site-packages (1.24.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "pip install matplotlib seaborn pandas numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928a0c5a-1a14-41ea-ad0f-4003c5e9d42b",
   "metadata": {},
   "source": [
    "## Data Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0029b5c4-7ef1-44a6-a50b-1082dd0ce2cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 100.00%\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   congested       1.00      1.00      1.00        96\n",
      "      normal       1.00      1.00      1.00       104\n",
      "\n",
      "    accuracy                           1.00       200\n",
      "   macro avg       1.00      1.00      1.00       200\n",
      "weighted avg       1.00      1.00      1.00       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Generate synthetic dataset\n",
    "def create_synthetic_data(num_entries=1000):\n",
    "    vehicle_types = ['car', 'truck', 'emergency']\n",
    "    weather_conditions = ['clear', 'rain', 'fog']\n",
    "    events = ['none', 'parade', 'accident']\n",
    "    \n",
    "    data = {\n",
    "        'Timestamp': [datetime.datetime.now() + datetime.timedelta(minutes=i) for i in range(num_entries)],\n",
    "        'Vehicle_Type': [random.choice(vehicle_types) for _ in range(num_entries)],\n",
    "        'Traffic_Volume': [random.randint(1, 10) for _ in range(num_entries)],\n",
    "        'Weather_Condition': [random.choice(weather_conditions) for _ in range(num_entries)],\n",
    "        'Event': [random.choice(events) for _ in range(num_entries)],\n",
    "        'Road_Condition': [random.choice(['wet', 'dry']) for _ in range(num_entries)],\n",
    "        'Time_of_Day': [random.randint(0, 23) for _ in range(num_entries)]\n",
    "    }\n",
    "\n",
    "    return pd.DataFrame(data)\n",
    "\n",
    "# Create dataset\n",
    "traffic_data = create_synthetic_data()\n",
    "traffic_data['Traffic_Condition'] = ['normal' if row['Traffic_Volume'] <= 5 else 'congested' for index, row in traffic_data.iterrows()]\n",
    "\n",
    "# Preprocessing\n",
    "le = LabelEncoder()\n",
    "traffic_data['Vehicle_Type'] = le.fit_transform(traffic_data['Vehicle_Type'])\n",
    "traffic_data['Weather_Condition'] = le.fit_transform(traffic_data['Weather_Condition'])\n",
    "traffic_data['Event'] = le.fit_transform(traffic_data['Event'])\n",
    "traffic_data['Road_Condition'] = le.fit_transform(traffic_data['Road_Condition'])\n",
    "traffic_data['Traffic_Condition'] = le.fit_transform(traffic_data['Traffic_Condition'])\n",
    "\n",
    "# Feature selection\n",
    "features = ['Vehicle_Type', 'Traffic_Volume', 'Weather_Condition', 'Event', 'Road_Condition', 'Time_of_Day']\n",
    "X = traffic_data[features]\n",
    "y = traffic_data['Traffic_Condition']\n",
    "\n",
    "# Train-Test Split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Model training\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluation\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "report = classification_report(y_test, y_pred, target_names=le.inverse_transform(np.unique(y)))\n",
    "\n",
    "print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
    "print(\"Classification Report:\")\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b55b47-d024-45bc-9cde-6b2fa26274f1",
   "metadata": {},
   "source": [
    "## Visualization 1: Traffic Volume Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0924b36-c316-4ef7-b41c-a35c7c6b86d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Create synthetic data for visualization\n",
    "def create_synthetic_data(num_entries=100):\n",
    "    data = {\n",
    "        'Traffic_Volume': np.random.randint(1, 100, size=num_entries),\n",
    "        'Weather_Condition': np.random.choice(['Clear', 'Rain', 'Fog'], size=num_entries),\n",
    "        'Event': np.random.choice(['None', 'Parade', 'Accident'], size=num_entries),\n",
    "        'Emergency_Vehicles': np.random.randint(0, 5, size=num_entries)\n",
    "    }\n",
    "    return pd.DataFrame(data)\n",
    "\n",
    "# Generate synthetic dataset\n",
    "traffic_data = create_synthetic_data()\n",
    "\n",
    "# Convert Emergency_Vehicles to a categorical type for visualization\n",
    "traffic_data['Emergency_Vehicles'] = traffic_data['Emergency_Vehicles'].astype(str)\n",
    "\n",
    "# Set plot style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Visualization 1: Traffic Volume Distribution\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(traffic_data['Traffic_Volume'], bins=10, kde=True, color='blue')\n",
    "plt.title('Traffic Volume Distribution')\n",
    "plt.xlabel('Traffic Volume')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c43e7bdb-032f-4871-8551-f5cfbab9ecae",
   "metadata": {},
   "source": [
    "## Visualization 2: Traffic Volume by Weather Condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a66db83c-87d1-4c26-a459-045c072827f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization 2: Traffic Volume by Weather Condition\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(x='Weather_Condition', y='Traffic_Volume', data=traffic_data, palette='Set2')\n",
    "plt.title('Traffic Volume by Weather Condition')\n",
    "plt.xlabel('Weather Condition')\n",
    "plt.ylabel('Traffic Volume')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "812643ac-31e9-4d38-aff9-887a3dc852d1",
   "metadata": {},
   "source": [
    "## Visualization 3: Emergency Vehicles Count During Events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "059fc161-0688-4281-83ae-fd3ce076c4ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization 3: Emergency Vehicles Count During Events\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.countplot(x='Event', hue='Emergency_Vehicles', data=traffic_data, palette='Set1')\n",
    "plt.title('Count of Emergency Vehicles During Events')\n",
    "plt.xlabel('Event Type')\n",
    "plt.ylabel('Count of Emergency Vehicles')\n",
    "plt.legend(title='Number of Emergency Vehicles', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e355f53-dc85-449c-b386-97d63af5f8da",
   "metadata": {},
   "source": [
    "## Effectiveness of Emergency Vehicle Priority\n",
    "\n",
    "**Purpose:** Illustrate how often emergency vehicles can pass without delay during high-traffic situations.\n",
    "\n",
    "### Visualization\n",
    "To showcase the effectiveness of the emergency vehicle priority system, we will create a bar plot comparing the average wait time for emergency vehicles during congested versus normal traffic conditions. This visualization will help demonstrate the benefits of prioritizing emergency vehicles in traffic management.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f794e1f-22b0-4453-bac3-51b0317a2a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new column for wait time simulation\n",
    "traffic_data['Wait_Time'] = np.where(traffic_data['Traffic_Volume'] > 50, 5, 1)  # Arbitrary wait time\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "emergency_wait_times = traffic_data.groupby('Traffic_Volume')['Wait_Time'].mean().reset_index()\n",
    "sns.barplot(data=emergency_wait_times, x='Traffic_Volume', y='Wait_Time', palette='Set2')\n",
    "plt.title('Average Wait Time for Emergency Vehicles Based on Traffic Volume')\n",
    "plt.xlabel('Traffic Volume')\n",
    "plt.ylabel('Average Wait Time (minutes)')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d68ce4-56ca-4974-b42d-c549ceebf781",
   "metadata": {},
   "source": [
    "# Shortest Path Suggestions Based on Traffic\n",
    "\n",
    "## Purpose\n",
    "The **Shortest Path Suggestions** feature of the Next-Gen Traffic Management System (NGTMS) is designed to provide drivers with alternative routes based on real-time traffic conditions. By analyzing traffic volume and congestion levels, the system aims to enhance travel efficiency and reduce delays. This feature is particularly beneficial during peak hours, accidents, or road closures, allowing users to avoid congested areas and reach their destinations faster.\n",
    "\n",
    "## Visualization\n",
    "To visualize the effectiveness of this feature, a **heatmap** will be utilized. This heatmap will display the frequency of alternative route suggestions based on traffic volume or congestion levels. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "292def04-a6c9-4052-81d5-1d0d15999eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulating alternative paths suggestions based on traffic volume\n",
    "traffic_data['Alternative_Path_Suggestions'] = np.where(traffic_data['Traffic_Volume'] > 50, 'Yes', 'No')\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(pd.crosstab(traffic_data['Event'], traffic_data['Alternative_Path_Suggestions']),\n",
    "            annot=True, fmt='d', cmap='YlGnBu')\n",
    "plt.title('Heatmap of Alternative Path Suggestions Based on Events')\n",
    "plt.xlabel('Alternative Path Suggested')\n",
    "plt.ylabel('Event Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "37165a2b-c37b-440c-a05d-78acd9586fc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulating pre- and post-implementation data\n",
    "traffic_data['Implementation_Phase'] = np.where(np.random.rand(len(traffic_data)) > 0.5, 'Before', 'After')\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(x='Implementation_Phase', y='Traffic_Volume', data=traffic_data, palette='Set3')\n",
    "plt.title('Comparison of Traffic Volume Before and After Model Implementation')\n",
    "plt.xlabel('Phase of Implementation')\n",
    "plt.ylabel('Traffic Volume')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58a6486-1853-4201-9de5-bc6dabd4887d",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The NGTMS represents a significant advancement in traffic management technology, utilizing a robust technical stack to provide adaptive and intelligent solutions for urban traffic challenges. By integrating real-time data and advanced algorithms, the system aims to create safer and more efficient roadways for all users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "573f2d88-7112-4148-ba28-6a98c70300f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}