Integrated Traffic Management System with Predictive Modeling and Visualization

Overview

The Traffic Management System (TMS) presented here integrates predictive modeling and real-time visualization to facilitate efficient traffic control and incident management. Developed using Python and Tkinter for the graphical interface, this system leverages machine learning algorithms to forecast traffic volume based on weather conditions and rush hour dynamics. The application visualizes historical and predicted traffic data through interactive graphs, providing insights crucial for decision-making in urban traffic management.

Key Features

• Traffic Prediction: Utilizes machine learning models (Linear Regression and Random Forest) to predict traffic volume based on temperature, precipitation, and rush hour indicators.
• Graphical Visualization: Displays historical traffic trends alongside predicted volumes on interactive graphs, enhancing understanding and monitoring capabilities.
• Real-time Traffic Simulation: Simulates traffic light changes to replicate real-world scenarios, aiding in assessing system responses under various conditions.
• Incident Reporting: Allows users to report incidents, capturing location and description for prompt management and response.

Getting Started

Prerequisites

Ensure Python 3.x is installed. Install dependencies using pip:

pip install pandas matplotlib scikit-learn

Installation

1. Clone the repository:

   git clone <https://github.com/EkeminiThompson/traffic_management_system.git>
   cd traffic-management-system

1. Install dependencies:

   pip install -r requirements.txt

1. Run the application:

   python main.py

Usage

1. Traffic Prediction:

   • Select a location, date, and model (Linear Regression or Random Forest).
   • Click "Predict Traffic" to see the predicted traffic volume.
   • Clear the graph using "Clear Graph" button.

2. Graphical Visualization:

   • The graph shows historical traffic data and predicted volumes for the selected date.
   • Red dashed line indicates the prediction date, and green dot shows the predicted traffic volume.

3. Traffic Light Control:

   • Simulates changing traffic light colors (Red, Green, Yellow) to assess traffic flow dynamics.

4. Incident Reporting:

   • Report traffic incidents by entering location and description.
   • Click "Report Incident" to submit the report.

Code Overview

main.py

# Main application using Tkinter for GUI

import tkinter as tk
from tkinter import messagebox, ttk
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import random
from datetime import datetime
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor

# Mock data for demonstration
data = {
    'temperature': [25, 28, 30, 22, 20],
    'precipitation': [0, 0, 0.2, 0.5, 0],
    'hour': [8, 9, 10, 17, 18],
    'traffic_volume': [100, 200, 400, 300, 250]
}
df = pd.DataFrame(data)

# Feature engineering
df['is_rush_hour'] = df['hour'].apply(lambda x: 1 if (x >= 7 and x <= 9) or (x >= 16 and x <= 18) else 0)

# Model training
X = df[['temperature', 'precipitation', 'is_rush_hour']]
y = df['traffic_volume']

# Create models
linear_model = LinearRegression()
linear_model.fit(X, y)

forest_model = RandomForestRegressor(n_estimators=100, random_state=42)
forest_model.fit(X, y)

class TrafficManagementApp:
    def __init__(self, root):
        # Initialization of GUI
        # ...

    def on_submit(self):
        # Handling traffic prediction submission
        # ...

    def update_graph(self, location, date_str, prediction):
        # Updating graph with historical and predicted traffic data
        # ...

    # Other methods for GUI components and functionality

if __name__ == "__main__":
    root = tk.Tk()
    app = TrafficManagementApp(root)
    root.mainloop()

Conclusion

The Traffic Management System is a sophisticated tool for urban planners and traffic controllers, combining advanced predictive analytics with intuitive graphical interfaces. By forecasting traffic patterns and visualizing data trends, the system enhances decision-making capabilities and facilitates proactive management of traffic resources. Its user-friendly design ensures accessibility and practicality, making it a valuable asset in modern urban infrastructure management.