Journal of System Simulation
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.
DOI
10.16182/j.issn1004731x.joss.22-1133
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