Bus Traffic Strategy Based on Immune Theory in Network Environment

doi:10.16182/j.issn1004731x.joss.22-1133

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.

Key words: V2X, bus system, traffic strategy, immune theory, hybrid genetic operator

CLC Number:

TP18

Li Cao, Zheng Rui, Ma Xiaolu, Ding Ziqiong, Zhong Junyi, Zhang Sheng, Qi Jingjing. Bus Traffic Strategy Based on Immune Theory in Network Environment[J]. Journal of System Simulation, 2024, 36(2): 449-462.

Figures/Tables 12

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方法	决策点相位	原相位时长/s	配时后相位时长	通行时间/s	停车次数	路口等待总时长/s	绿信比差的绝对值总和	迭代次数
固定相位时长	(1, 4, 1)	(20, 20, 30, 20) (30, 25, 30, 25) (28, 25, 28, 25)		241.4	2	29.1
遗传算法的通行策略	(1, 4, 1)	(20, 20, 30, 20) (30, 25, 30, 25) (28, 25, 28, 25)	(39, 18, 18, 17) (30, 20, 25, 18) (25, 21, 24, 20)	211.3	2	3.5	0.737	38
基于免疫思想的通行策略	(1, 4, 1)	(20, 20, 30, 20) (30, 25, 30, 25) (28, 25, 28, 25)	(38, 17, 18, 17) (30, 24, 29, 24) (25, 21, 24, 23)	216.8	1	1.2	0.675	29
固定相位时长	(2, 1, 2)	(30, 25, 40, 25) (35, 20, 30, 25) (28, 25, 28, 25)		335.0	3	127.0
遗传算法的通行策略	(2, 1, 2)	(30, 25, 40, 25) (35, 20, 30, 25) (28, 25, 28, 25)	(21, 20, 27, 23) (33, 29, 37, 27) (29, 20, 30, 22)	218.6	2	6.1	0.284	31
基于免疫思想的通行策略	(2, 1, 2)	(30, 25, 40, 25) (35, 20, 30, 25) (28, 25, 28, 25)	(25, 21, 29, 21) (30, 28, 39, 26) (33, 24, 29, 24)	209.1	2	4.4	0.247	24
固定相位时长	(4, 2, 4)	(30, 35, 30, 35) (35, 25, 35, 25) (28, 20, 28, 20)		333.6	1	91.9
遗传算法的通行策略	(4, 2, 4)	(30, 35, 30, 35) (35, 25, 35, 25) (28, 20, 28, 20)	(42, 30, 31, 37) (40, 27, 40, 39) (20, 20, 21, 21)	210.7	0	1.8	0.384	38
基于免疫思想的通行策略	(4, 2, 4)	(30, 35, 30, 35) (35, 25, 35, 25) (28, 20, 28, 20)	(39, 29, 35, 39) (42, 27, 34, 36) (21, 21, 21, 21)	202.0	0	0	0.377	24

方法	决策点相位	原相位时长/s	配时后相位时长	通行时间/s	停车次数	路口等待总时长/s	绿信比差的绝对值总和	迭代次数
固定相位时长	(3)	(20, 25, 20, 30)		149.7	1	71.8
遗传算法的通行策略	(3)	(20, 25, 20, 30)	(23, 29, 24, 34)	75.5	0	0	0.016	23
基于免疫思想的通行策略	(3)	(20, 25, 20, 30)	(23, 27, 22, 33)	75.5	0	0	0.014	18
固定相位时长	(3, 3)	(25, 20, 25, 20) (35, 30, 25, 30)		203.8	1	61.9
遗传算法的通行策略	(3, 3)	(25, 20, 25, 20) (35, 30, 25, 30)	(31, 25, 29, 26) (26, 20, 27, 20)	151.7	1	3.4	0.062	36
基于免疫思想的通行策略	(3, 3)	(25, 20, 25, 20) (35, 30, 25, 30)	(29, 24, 29, 25) (24, 21, 24, 21)	150.1	1	1.9	0.059	27
固定相位时长	(4, 1)	(35, 30, 35, 35) (30, 26, 28, 20)		286.8	2	143.7
遗传算法的通行策略	(4, 1)	(35, 30, 35, 35) (30, 26, 28, 20)	(40, 34, 38, 35) (36, 31, 35, 35)	147.1	0	0	0.057	29
基于免疫思想的通行策略	(4, 1)	(35, 30, 35, 35) (30, 26, 28, 20)	(39, 33, 37, 35) (35, 30, 34, 34)	147.1	0	0	0.050	20

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