A comparison of approximate dynamic programming and simple genetic algorithm for traffic control in oversaturated conditions — Case study of a simple symmetric network

The performance of two algorithms for finding traffic signal timings in a small symmetric network with oversaturated conditions was analyzed. The two algorithms include an approximate dynamic programming approach using a “post-decision” state variable (ADP) and a simple genetic algorithm (GA). Results were found by using microscopic simulation and compared based on typical measures of performance (delay, throughput, number of stops) and also on measures that considered the efficiency of green time utilization and queue occupancy of the links. The symmetric characteristics of the small network allowed a straightforward analysis of the operation of the signals, providing some insights on the quality of the solutions. Results showed that even though the solutions from ADP were very different from those in GA, the network performance for both methods was similar, used green time efficiently preventing queue backups, and served all approaches according to current demands. The potential of ADP using the “post-decision” state variable is currently under further analysis using more challenging conditions, additional constraints, and domain knowledge as part of the algorithm formulation.