Optimizing Adaptive Signalized Intersections: A Cooperative Game Theory Approach

Abstract

Adaptive signalized intersections can significantly enhance the traffic characteristics of transportation networks. This research aims to utilize a cooperative game theory approach, Shapley value, to optimize the green times of signalized intersections in a network. This is achieved through simulating the current traffic characteristic of a network, then applying Shapley value to calculate the optimized green times of phases in the intersection based on their marginal contribution to the overall network performance. The proposed model is developed and simulated on PTV VISSIM. The optimization module is coded via Python through the integrated COM interface. The proposed approach is developed and tested on a case study in Cairo, Egypt, presenting its ability to optimize the overall traffic characteristics of a network. The model’s outcome is compared to optimized fixed time signal. The proposed approach improved the overall network performance, rather than achieving local optimal solutions at subsets of the network.