予測モデリングと視覚化を備えた統合交通管理システム

pip install pandas matplotlib scikit-learn

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

   pip install -r requirements.txt

   python 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()