Real – Time Traffic Prediction Using Machine Learning
- Version
- Download 19
- File Size 447.08 KB
- File Count 1
- Create Date 24 July 2025
- Last Updated 24 July 2025
Real - Time Traffic Prediction Using Machine Learning
E. KATARAJU , S. VARDHINI SRILAKSHMI
Assistant professor, 2 MCA Final Semester, Master of Computer Applications,
Sanketika Vidya Parishad Engineering College, Vishakhapatnam,
Andhra Pradesh, India.
Abstract
Traffic congestion is a growing challenge in urban cities like Visakhapatnam (Vizag), resulting in increased travel time, fuel consumption, and pollution. This project aims to provide a real-time traffic prediction system that helps commuters and city planners visualize and anticipate traffic flow across popular city locations. The system is developed using HTML, CSS, and JavaScript for the frontend, a Flask-based Python backend, and machine learning for predictive modeling. The frontend includes an interactive map with live updates using Leaflet.js, showing traffic status at key Vizag locations. Traffic status is classified into categories such as "Smooth", "Moderate", and "Heavy", and visualized using color-coded markers and cards. The backend collects traffic data (simulated or real-time API-based) and applies machine learning algorithms to predict short-term traffic conditions (e.g., for the next 15 minutes). The prediction model is trained using historical traffic patterns, time-based factors, and location-specific attributes. Flask handles the API endpoints and serves the traffic data dynamically to the frontend. This system offers a scalable and modular architecture where real traffic APIs or datasets can be integrated for future enhancements. It demonstrates the practical application of AI in smart city solutions and urban transportation planning.
INDEX TERMS: Real-Time Traffic Prediction, Machine Learning for Urban Mobility, Flask-Based Web Application, Traffic Visualization with Leaflet.js, Smart City Traffic Monitoring,Interactive Dashboard for Traffic Analysis, Congestion Level Classification, Simulated Traffic Data Visualization
Download