Dynamic multi-objective optimization for mixed traffic flow based on partial least squares prediction model

• Main Authors: Juan Chen, Zhengxuan Xue, Dongxiao Han
• Format: Article
• Language: English
• Published: SAGE Publishing 2019-10-01
• Series: Journal of Algorithms & Computational Technology
• Online Access: https://doi.org/10.1177/1748302619873589

A dynamic multi-objective genetic algorithm based on partial least squares prediction model (DNSGA-II-PLS) is presented in this paper to solve the mix traffic flow multi-objective timing optimization problem with time-varying traffic demand. Take motor vehicle delay, non-motor vehicle delay, and pedestrian delay as objectives to solve the problem. Make comparison with three improved dynamic multi-objective genetic algorithms based on prediction strategy: dynamic multi-objective evolutionary algorithm based on simple prediction (DNSGA-II-PREM), autonomous regression dynamic multi-objective evolutionary algorithm (DNSGA-II-AR), and mutation-based dynamic multi-objective evolutionary algorithm (DNSGA-II-MUT) under four kinds of test functions. The results show that compared with the other three algorithms, the DNSGA-II-PLS algorithm proposed in this paper shows better performance in convergence and distribution, and this algorithm has less complexity. Finally, taking the intersections of Taiping North Road and Zhujiang Road in Nanjing as the research object, the performance of the algorithm is tested under the simulation environment. The results show that compared with the DNSGA-II–AR, which has best performance among three compared algorithms, the proposed DNSGA-II-PLS algorithm can effectively reduce the delay of motor vehicles by 6.7%, non-motor vehicles by –2.8%, and pedestrian waiting time by 20.5%.