A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing

This paper presents a novel de-centralized flexible phasing scheme, cycle-free, adaptive traffic signal controller using a Nash bargaining game-theoretic framework. The Nash bargaining algorithm optimizes the traffic signal timings at each signalized intersection by modeling each phase as a player in a game, where players cooperate to reach a mutually agreeable outcome. The controller is implemented and tested in the INTEGRATION microscopic traffic assignment and simulation software, comparing its performance to that of a traditional decentralized adaptive cycle length and phase split traffic signal controller and a centralized fully-coordinated adaptive phase split, cycle length, and offset optimization controller. The comparisons are conducted in the town of Blacksburg, Virginia (38 traffic signalized intersections) and in downtown Los Angeles, California (457 signalized intersections). The results for the downtown Blacksburg evaluation show significant network-wide efficiency improvements. Specifically, there is a $23.6\; \%$ reduction in travel time, a $37.6\; \%$ reduction in queue lengths, and a $10.4\; \%$ reduction in $\mathrm{CO}2$ emissions relative to traditional adaptive traffic signal controllers. In addition, the testing on the downtown Los Angeles network produces a $35.1\; \%$ reduction in travel time on the intersection approaches, a $54.7\; \%$ reduction in queue lengths, and a $10\; \%$ reduction in $\mathrm{CO}2$ emissions compared to traditional adaptive traffic signal controllers. The results demonstrate significant potential benefits of using the proposed controller over other state-of-the-art centralized and de-centralized adaptive traffic signal controllers on large-scale networks both during uncongested and congested conditions.