Meta-Heuristics for Bi-Objective Urban Traffic Light Scheduling Problems

Abstract

This paper addresses a bi-objective urban traffic light scheduling problem (UTLSP), which requires minimizing both the total network-wise delay time of all vehicles and total delay time of all pedestrians within a given finite-time window. First, a centralized model is employed to describe the 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 based on the two objectives. Second, metaheuristics, such as harmony search (HS) and artificial bee colony (ABC), are implemented to solve the UTLSP. Based on the characteristics of the UTLSP, a local search operator is utilized to improve the search performance of the developed optimization algorithms. Finally, experiments are carried out based on the real traffic data in Jurong area of Singapore. The HS, ABC, and their variants with the local search operator are evaluated in 19 case studies with different scales and time windows. To the best of our knowledge, this paper is the first of its kind to solve bi-objective traffic light scheduling problems in the literature. To demonstrate the effectiveness of the proposed algorithms in dealing with bi-objective optimization in traffic light scheduling, they are compared to the classical non-dominated sorting genetic algorithm II (NSGAII) with and without the local search operation. The comparisons indicate that our algorithms outperform the NSGAII algorithm with and without the local search operator for solving the UTLSP.