Adaptive traffic signal control for urban intersections using reinforcement learning: a SUMO simulation case study on Gulshan-2

Abstract

Traffic congestion is an ever-growing concern in rapidly increasing population and

increase of vehicles on roads. On top of that, our road condition, pedestrian be- havior, and driving behavior makes the situation much worse. As Bangladesh has

started adapting to the fixed-time traffic light system, finding the optimal adaptive traffic light management system is required to mitigate the issue. Keeping the lack of intra-vehicular communication devices in mind, we have studied different traffic light controlling systems and how these perform on Bangladeshi roadways. In our study, we have used traffic simulator to simulate Gulshan-2 intersection, one of the major congestion points in Dhaka city. Based on traffic data, we have created a vehicular network system to find out how the existing traffic light models per- form on mitigating the traffic jam using an adaptive traffic management system. In our

study, we simulate the Gulshan-2 intersection using a realistic traffic dataset de- rived from field data and analyze three control models, Fixed-Time, Q-Learning,

and Deep Q-Network (DQN). The results show that in ideal traffic condition, when no anomalies are present; the Q-Learning controller reduced average vehicle delay by

approximately 60-70% and improved throughput by 35-45% compared to the fixed- time system, demonstrating its superior adaptability to dynamic traffic flows. How- ever, when we add real-life anomalies such as potholes and jaywalking, the baseline

controllers’ performance drops. By acknowledging the drawbacks of QL controller

in abnormal scenarios, we have introduced a hybrid model CRQL (Congestion Re- sponsive Queue Learning) and made some improvements. In abnormal situation

CRQL performs better in regard to throughput, which is a 76.9% improvement over Fixed-Time, 37.4% improvement over Q-Learning and 215.2% improvement over

DQN. This reflects a overall performance improvement under disruptive and non- stationary environment.

In short, the study helps to see how existing models perform under real-life anoma- lies, which factors are significant to create Ad hoc model for our extreme pedestrian

patterns and road conditions and how our proposed solution; the CRQL ad hoc or hybrid model performs to address the problems.