This paper presents a novel isolated traffic signal control algorithm based on a game-theoretic optimization framework. The algorithm models a signalized intersection considering four phases, where each phase is modeled as a player in a game in which the players cooperate to reach a mutual agreement. The Nash bargaining solution is applied to obtain the optimal control strategy, considering a variable phasing sequence and free cycle length. The system is implemented and evaluated in the INTEGRATION microscopic traffic assignment and simulation software. The proposed algorithm is compared to an optimum fixed-time plan and an actuated control algorithm to evaluate the performance of the proposed Nash bargaining approach for different traffic demand levels. The simulation results demonstrate that the proposed Nash bargaining control algorithm outperforms the fixed-time and actuated control algorithms for the various traffic conditions. The benefits are observed in improvements in the stopped delay, queue length, travel time, average vehicle speed, system throughput, fuel consumption, and emission levels. Specifically, the simulation results show a reduction in the average travel time ranging from 37% to 65%, a reduction in the total delay ranging from 41% to 64%, a reduction in the queue length ranging from 58% to 77% and a reduction in the emission levels ranging from 6% to 17%.
['Hossam M. Abdelghaffar', 'Hao Yang', 'Hesham Rakha']
Isolated traffic signal control using a game theoretic framework