NSQGA-Based Optimization of Traffic Signal in Isolated Intersection with Multiple Objectives

In this chapter, a novel multi-objective optimization algorithm is investigated to deal with the issue of signal timing in an isolated traffic intersection aiming at releasing traffic congestion, reducing travel delay, maximizing the traffic flow, and minimizing pollution. The throughput maximum, stop times, and delay time of motorized traffic and non-motorized traffic are selected as the objectives of the optimization problem, and quantum computing is integrated with the genetic algorithm to obtain optimized traffic signal timing plan to upgrade the performance of intersection with faster convergence and higher accuracy. A numerical simulation study is conducted on MATLAB in this research work as a case study with a Non-dominated Sorting Quantum Genetic Algorithm (NSQGA), and the simulation results show that the proposed NSQGA algorithm performed superior to the conventional NSGA-II algorithm in effectively coordinating the traffic signal timing plan for an isolated intersection to improve the traffic capacity, efficiency, and safety of traffic system.