Managing Congestions at Multiple Intersections During Disasters with Chronological Coordination of Intelligent Traffic Light Management

Optimizing congestion and accidents is a challenging task due to multiple intersection factors such as increasing traffic flow, disasters, and weather fluctuations. In the face of uncertainties, Intelligent Traffic Light Management (ITLM) has emerged as an efficient approach for monitoring traffic flow changes and adapting the system to ensure quality conditions. Reducing congestion with current ITLM-based agents is a time-consuming process, as it involves extensive messaging. None of the prior studies have taken into account the chronological interaction of agents when managing disaster conditions. This study proposes a new system called CCITLM (Chronological Coordination of ITLM) for multiple intersections that reduces congestion and traffic flow delays during disasters by ensuring timely coordination among ITLM agents. This system aims to improve congestion control, using the chronological exchange of traffic data between autonomous agents and employing the Chicken Swarm Optimization (CSO) algorithm. A CCITLM-based traffic simulator was developed and evaluated using a 4×4 traffic network with sixteen intersections. Its performance in terms of the optimal vehicle waiting time and lane occupancy rate at all intersections were compared. CCITLM achieved the highest performance with an average waiting time of 28.4 seconds and a lane occupancy rate of 50 %.