Urban traffic congestion has become a major challenge in rapidly growing cities around the world. Existing traffic management systems often rely on fixed signal timings and manual interventions, which fail to respond effectively to changing traffic conditions. This results in longer commute times, increased fuel consumption, air pollution, and a higher risk of accidents.
Traffix AI is an innovative solution designed to tackle these problems by leveraging artificial intelligence and real-time data. The system collects traffic information from cameras, sensors, GPS devices, and environmental sources, processes the data using machine learning algorithms, and optimizes traffic signals dynamically to improve the flow of vehicles. It also predicts future traffic patterns, helping authorities and commuters plan better and avoid bottlenecks.
The project integrates reinforcement learning techniques to adjust signal durations based on current traffic density, while anomaly detection algorithms help in identifying accidents or abnormal traffic behavior instantly. Through an intuitive dashboard, traffic operators and users can access live updates, congestion alerts, and recommended alternative routes.
Built With
- framework
- language
Log in or sign up for Devpost to join the conversation.