WU Z Q, FAN Z B, GUO J X,et al.Study on coordinated control of urban trunk traffic based on cellular automata simulation[J].Journal of Henan Polytechnic University(Natural Science) ,2025,44(5):62-71.

DOI:10.16186/j.cnki.1673-9787.2024070071

Received: 2024/07/19

Revised: 2025/03/26

Published:2025/07/23

Study on coordinated control of urban trunk traffic based on cellular automata simulation

Wu Zhiqiang1, Fan Zhibo2,3, Guo Jingxing1, Zhang Xifan4, Wang Yanhong5

1.College of Computer Science， Henan Polytechnic University， Jiaozuo  454000， Henan， China；2.Graduate School， China Academy of Engineering Physics， Beijing  100088， China；3.Institute of Applied Physics and Computational Mathematics Beijing， Beijing  100088， China；4.School of Mathematics and Information Science， Henan Polytechnic University， Jiaozuo  454000， Henan， China；5.College of Mechanical and Electrical Engineering， Henan Agricultural University， Zhengzhou  450002， Henan， China

Abstract: Objectives To explore the impact of the coordinated control scheme for continuous intersections in the urban trunk traffic system on cyclic traffic congestion and avoid the interference of unreasonable traffic control on travel， Conduct research on coordinated control of urban arterial traffic based on cellular automaton simulation.  Methods the trunk traffic system of three continuous intersections was taken as the research scenario. The traffic flow cellular automaton NaSch model was applied， and open boundary conditions were adopted. The traffic operations under different traffic control parameters were simulated， and the effects of the vehicle-entering probability at the traffic system entrance， the signal cycle lengths， green-light ratios， green-time differences of each intersection， and the lengths of each road section on the traffic states of the three continuous road sections were analyzed.  Results The results showed that， in the trunk traffic system of three continuous intersections， an increase in the vehicle-entering probability had a more significant impact on the upstream road section. When it was controlled below 0.6， the number of stagnant vehicles and the probability of traffic jams were reduced. A shorter signal cycle length at the upstream intersection relieved the traffic pressure on the downstream road section， while the downstream had little influence on the upstream. An excessively large green-light ratio at the upstream intersection affected the traffic state of the adjacent downstream road section. A signal cycle of 90 s and a green-light ratio of 0.6 were found to be more suitable； when the green-light ratio was lower than 0.5， the probability of vehicles stopping and waiting at the intersection increased significantly. The green-time difference at the upstream intersection appropriately alleviated the congestion at the downstream intersection. An increase in the length of the road section led to an increase in the number of queuing vehicles on this section， but had little impact on other road sections.  Conclusions In the urban trunk traffic system of three continuous intersections， by reducing the vehicle-entering probability and reasonably regulating parameters such as the signal cycle lengths， green-light ratios， and green-time differences， traffic queuing at intersections was reduced， and congestion was alleviated. This study provided a reference for urban trunk traffic design and signal timing optimization of continuous intersections.

Key words:urban trunk traffic;consecutive intersections;coordinated control;cellular automata;traffic simulation