Multi-Agent Learning Automata for Online Adaptive Control of Large-Scale Traffic Signal Systems

Adaptive traffic control systems are gaining attention in recent years as traditional hand-crafted traffic control experiences performance fall-offs with increasingly complicated metropolitan traffic patterns. This paper studies a learning automata (LA)-based traffic signal control scheme that adapts to real-time traffic patterns and optimizes traffic flows by dynamically changing the green split timings. A novel LA algorithm, called K-Neighbor Multi-Agent Learning Automata (KN-MALA), is proposed to learn the optimal decision online and adjust the traffic light accordingly in an attempt to minimize the overall waiting time at an intersection. In particular, KN-MALA employs an online distributed learning framework that integrates the traffic condition of neighboring intersections to efficiently learn and infer optimal decisions for large-scale traffic signal systems. Furthermore, a parameter insensitive update mechanism is designed for KN-MALA to overcome the instability caused by initialization variations. Experiments are conducted on real-world traffic patterns of Sioux Falls City and the performance of the proposed algorithm is compared with the pre-timed traffic light control scheme and an adaptive traffic light control scheme based on single-agent learning automata. The results show that the proposed algorithm outperforms the other schemes in terms of quick traffic clearance under various traffic patterns and initial conditions.