Real-Time Traffic Prediction and Optimization using Machine Learning

Description

The Real-Time Traffic Prediction and Optimization System leverages machine learning to predict traffic congestion and optimize signal timings in urban areas. The system uses real-time data from traffic APIs such as GraphHopper and Meteomatics to provide accurate congestion forecasts and route recommendations. A user-friendly Streamlit dashboard visualizes traffic patterns, enhancing decision-making for urban traffic management.

The system also offers actionable insights on traffic flow and potential bottlenecks, which can help reduce overall congestion. By optimizing signal timings and routing suggestions, the platform aims to improve urban mobility, reduce fuel consumption, and enhance commuter experience in real-time.

Conclusion

This system offers a data-driven solution to urban traffic problems, using machine learning and real-time data for efficient traffic management. It highlights the power of predictive analytics in improving urban infrastructure and sustainability.

With the ability to optimize traffic flow and reduce congestion, the system sets the foundation for smarter, more efficient cities. Its adaptability to various urban settings makes it a valuable tool for modern urban planners and traffic managers.