A new nondominated sorting genetic algorithm based on the regression line for fuzzy traffic signal optimization problem

Abstract

Traffic jam is a daily problem in nearly all major cities in the world and continues to increase with population and economic growth of urban areas. Traffic lights, as one of the key components at intersections, play an important role in control of traffic flow. Hence, study and research on phase synchronization and time optimization of the traffic lights could be an important step to avoid creating congestion and rejection queues in a urban network. Here, we describe the application of NSGA-II, a multi-objective evolutionary algorithm, to optimize both vehicle and pedestrian delays in an individual intersection. In this paper, we improve NSGA-II algorithm based on the regression line to find a Pareto-optimal solution or a restrictive set of Pareto-optimal solutions based on our solution approaches to the problem, named PDNSGA (Non-dominated Sorting Genetic Algorithm based on Perpendicular Distance). The high speed of the proposed algorithm and its quick convergence makes it desirable for large scheduling with a large number of phases. It is demonstrated that our proposed algorithm (PDNSGA) gives better outputs than those of Moga, NSGA-II, and WBGA in traffic signal optimization problem, statistically .