Real-Time Traffic Signal Optimisation Using Deep Q-Network Algorithm and Camera Data

Traffic congestion has become a problem in developing countries’ urban areas. This is largely caused by traffic signals that have fixed-timing which causes them to fail to adapt to changing traffic conditions in real-time. This research introduces a Reinforcement Learning-based solution using a Deep Q-Network algorithm to optimise traffic signal lights control, aiming at reducing congestion and enhancing traffic flow efficiency. The system is developed in a virtual environment using PTV VISSIM simulation software and the real-time traffic data is collected using simulated cameras. The collected traffic data is then processed using Deep Q-Network algorithm which is implemented using Python and TensorFlow. By optimising traffic signal light timings to be adaptive, the system introduces significant improvements in reducing traffic waiting times at intersections and improving traffic flow on the road in comparison to the traditional fixed-timing systems. The system ensures scalability and effectiveness in offering a promising framework for adaptive traffic management in urban roads.