Smart Traffic Management Dashboard

A complete end-to-end traffic analytics and forecasting system designed to help city planners, transportation analysts, and machine learning practitioners monitor traffic volume, analyze congestion patterns, and predict future traffic conditions. This solution transforms raw sensor data into actionable insights using Python, MySQL, and Power BI. The system includes automated data ingestion, feature engineering, a forecasting model, and a fully interactive Power BI dashboard.

Business Problem

Urban mobility systems face several challenges including peak hour congestion, unpredictable traffic surges, and weather-based fluctuations. Manual data inspection makes it difficult to understand behavioral patterns or anticipate congestion in advance.

Transportation decision-makers require:

• Accurate traffic volume analysis across different times of day
• Identification of congestion hotspots
• Understanding of weather impact on traffic flow
• Daily, weekly, and monthly patterns in traffic behavior
• Reliable forecasting to support planning and policy decisions This project provides automated data processing, analytical insights, and machine learning-driven forecasting to support data-driven transportation management.

Project Structure

Below is the structure adapted to your actual project layout: smart_traffic/

├── data/

│ └── raw/ # Original traffic dataset

├── src/

│ ├── config.py # MySQL connection configuration

│ ├── ingest.py # Loads CSV into MySQL

│ ├── features.py # Feature engineering and creation of derived metrics

│ └── forecast.py # Machine learning forecasting model

├── smart_traffic_dashboard.pbix # Power BI dashboard

├── .env # Environment variables for DB connection

└── requirements.txt # Python dependencies

Success Metrics and Dashboard Features:

The dashboard delivers insights across the following analytical dimensions:

Traffic Performance

• Total traffic volume
• Average hourly traffic
• Peak versus non-peak traffic comparison
• Daily and hourly traffic trends
• Monthly traffic patterns

Congestion Analysis

• Congestion classification (low, moderate, high)
• Hour by day-of-week congestion matrix
• High congestion percentage
• Congestion-level distribution comparison

Weather Impact

• Traffic volume during rainy conditions
• Traffic volume during snowy conditions
• Comparison across different weather categories
• Weather-based reduction or increase in average volume

Temporal Behavior

• Hourly cycle patterns
• Weekly cycle patterns
• Seasonal trends
• Holiday traffic deviations

Forecasting

• Prediction of next-hour traffic volume using machine learning
• Evaluation metrics including RMSE and R² score
• Visual comparison of actual versus predicted traffic

Getting Started

Prerequisites

• Python 3.9 or higher
• Power BI Desktop
• MySQL Server 8.0 or higher
• MySQL ODBC or .NET connector installed for Power BI
• Kaggle Metro Interstate Traffic Volume dataset

Installation

• Clone the repository: git clone https://github.com/sdlk4/smart-traffic-management.git
• cd smart-traffic-management

Install dependencies:

• pip install -r requirements.txt
• Configure environment variables:
• Copy .env.example to .env and provide your MySQL configuration:
• MYSQL_HOST=localhost
• MYSQL_USER=root
• MYSQL_PASSWORD=yourpassword
• MYSQL_DB=smart_traffic

Place the raw Kaggle dataset into:

• data/raw/Metro_Interstate_Traffic_Volume.csv

Load the dataset into MySQL:

• python src/ingest.py

Generate engineered features:

• python src/features.py

Run forecasting model:

• python src/forecast.py

Open the Power BI dashboard:

• Open smart_traffic_dashboard.pbix in Power BI Desktop and refresh the data source if needed.

How It Works

• Data Ingestion (src/ingest.py)
• Loads raw CSV
• Handles null or invalid timestamps
• Creates MySQL table
• Inserts data in optimized batches
• Feature Engineering (src/features.py)
• Extracts hour, month, and day-of-week
• Creates peak hour indicator
• Classifies congestion level using predefined volume thresholds
• Inserts enhanced dataset into MySQL for analytics and visualization
• Forecasting Model (src/forecast.py)
• Loads engineered data
• Creates lag-based historical features (1, 2, 3, 24, 168 hours)
• Trains XGBoost regression model
• Evaluates using RMSE and R²
• Generates prediction output for analysis
• Dashboard (smart_traffic_dashboard.pbix)

Includes:

• KPI cards
• Time-series analysis
• Congestion-level metrics
• Weather impact views
• Heatmap matrix
• Forecast vs actual comparison (if prediction table loaded)

Key Features

• End-to-end data pipeline
• Real-time aggregation using MySQL
• Feature-rich analytics dashboard
• Automated congestion classification
• Weather correlation analysis
• Machine learning forecasting model
• Fully documented workflow

Sample Insights

• Traffic peaks consistently between 6–9 AM and 4–7 PM
• Rain and snow cause significant reductions in peak volume
• Weekends exhibit lower congestion compared to weekdays
• Cloud coverage has moderate impact on traffic
• Holiday traffic patterns differ from normal weekdays, showing afternoon spikes

Technical Requirements

• Python Dependencies: Listed in requirements.txt below.
• Power BI Requirements: Power BI Desktop, Stable MySQL ODBC or .NET connector
• Active MySQL service

Performance Considerations

• Dataset size: ~48,204 rows
• Power BI refresh time: under 10 seconds
• Feature engineering time: under 5 seconds
• Machine learning model training: under 10 seconds
• Memory requirement: 4GB minimum recommended

Contributing

• Fork the repository
• Create a feature branch: git checkout -b feature/new_metric
• Commit changes
• Push branch
• Submit pull request with explanation

Common issues include:

• MySQL authentication errors (use mysql_native_password)
• Power BI not connecting (install correct 64-bit ODBC driver)
• Dataset path errors (verify raw CSV location)
• Forecasting script errors (ensure no missing lag rows)

Getting Help

• Review project issues on GitHub
• Create a new issue describing the error in detail
• Check troubleshooting steps above