Bus Traffic Strategy Based on Immune Theory in Network Environment

Authors

Cao Li, School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot (Anhui Normal University), Wuhu 241002, ChinaFollow
Rui Zheng, School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot (Anhui Normal University), Wuhu 241002, ChinaFollow
Xiaolu Ma, School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243000, China
Ziqiong Ding, School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot (Anhui Normal University), Wuhu 241002, China
Junyi Zhong, School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot (Anhui Normal University), Wuhu 241002, China
Sheng Zhang, School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot (Anhui Normal University), Wuhu 241002, China
Jingjing Qi, Anhui Dar Intelligent Control System Co. Ltd, Wuhu 241002, China

Abstract

Abstract: In V2X network environment, the bus system can obtain dynamic global information and the bus traffic strategy is carried out based on the road scene between adjacent bus stops. The mathematical model of bus rapid traffic is constructed with the difference of green time ratio as the main parameter. A hybrid genetic operator is proposed on the basis of the combination of genetic algorithm and immune theory, the design of affinity, the selection of excellent antibodies. A bus traffic strategy based on immune theory is proposed on the basis of the improvement of adaptive crossover and mutation probability. The simulation results show that compared with the fixed phase duration, the running, waiting, and parking times can be significantly reduced by using the genetic algorithm and immune theory. Compared with the genetic algorithms for traffic strategy, the change of the green time ratio of traffic strategy based on the immune theory is reduced by about 8.8% on average, and the convergence speed is increased by about 26.8% on average. Tthe improved strategy can reduce the risk of falling into local optimum, which can realize the rapid traffic of the bus and improve the operation efficiency.

Recommended Citation

Li, Cao; Zheng, Rui; Ma, Xiaolu; Ding, Ziqiong; Zhong, Junyi; Zhang, Sheng; and Qi, Jingjing (2024) "Bus Traffic Strategy Based on Immune Theory in Network Environment," Journal of System Simulation: Vol. 36: Iss. 2, Article 14.
DOI: 10.16182/j.issn1004731x.joss.22-1133
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss2/14

First Page

449

Last Page

462

CLC

TP18

Recommended Citation

Li Cao, Zheng Rui, Ma Xiaolu, et al. Bus Traffic Strategy Based on Immune Theory in Network Environment[J]. Journal of System Simulation, 2024, 36(2): 449-462.

Corresponding Author

Zheng Rui

DOI

10.16182/j.issn1004731x.joss.22-1133