Development and Testing of a Novel Game Theoretic De-Centralized Traffic Signal Controller

Abstract

The paper presents a novel de-centralized traffic signal controller, achieved using a Nash bargaining game-theoretic framework, that operates a flexible phasing sequence to adapt to dynamic changes in traffic demand levels. The Nash bargaining algorithm is used to optimize 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 algorithm was implemented in the INTEGRATION microscopic traffic assignment and simulation software and tested on two sample networks. The proposed control approach was compared to the operation of an optimum fixed-time coordinated plan, an actuated controller, a centralized adaptive phase split controller, a decentralized phase split and cycle length controller, and a fully coordinated adaptive phase split, cycle length, and offset optimization controller to evaluate its performance. Testing was initially conducted on an isolated intersection, showing a 77% reduction in queue length, a a 64% reduction in vehicle delay, and a 17% reduction in vehicle emission levels. In addition, the algorithm was tested on an arterial network producing statistically significant reductions in total delay ranging between 36% and 67% and vehicle emission reductions ranging between 6% and 13%.