A meta-heuristic with ensemble of local search operators for urban traffic light optimization

This study addresses a bi-objective urban traffic light optimization problem in a scheduling framework, which aims to minimize both the total network-wise delay time of all vehicles and total delay time of all pedestrians within a given finite time window. Firstly, a centralized model is employed to describe the urban traffic light scheduling problem (UTLSP), where the cost functions and constraints of the two objectives are presented. A non-domination strategy based metric is used to compare and rank solutions for the two objectives. Secondly, an effective metaheuristic, artificial bee colony (ABC), is implemented to solve the UTLSP. An ensemble of local search operators is developed to improve the local search performance of ABC. Finally, experiments are carried out based on the real traffic data in Jurong area of Singapore. The ABC are evaluated in 8 cases with different scales and time windows. The proposed ABC is compared to NSGAII, which is a classical multi-objective optimization algorithm. The comparisons and discussions indicate that the proposed algorithm outperforms the NSGAII algorithm for solving the UTLSP.