The main goal of this project is to create a smart system capable of managing city traffic by optimizing traffic signal timing in real-time. Traditional traffic systems operate on fixed-time cycles or basic reactive mechanisms, which are often inefficient in dynamic urban settings. This project leverages reinforcement learning to build an agent that can learn from traffic data and improve its decisions over time. The system will receive live traffic inputs (such as vehicle count, flow rate, and congestion) and adjust signal timings to reduce traffic congestion, waiting times, and travel delays. By implementing such a system, the project not only enhances the efficiency of traffic management but also gives students a practical understanding of reinforcement learning in real-world applications.
The project unfolds across twelve weeks, involving progressive activities starting from foundational theory to model deployment. In the initial stages, students will study reinforcement learning principles, including agent-environment interaction and reward-based learning. They will then create a simulation environment that mimics real-world traffic flow using tools or synthetic data inputs, possibly integrating camera modules for image-based traffic input.
The middle phase of the project involves developing and training the RL model, experimenting with different environment settings to simulate various traffic patterns. Students will test and fine-tune the model to ensure it adapts well to fluctuating traffic conditions. Toward the end of the project, the system will be evaluated for effectiveness in reducing congestion and delays, followed by documentation, final testing, and a presentation. Throughout the process, students are expected to follow coding standards, ensure ethical implementation, and develop a scalable and modular architecture.
