摘要
在V2X网联环境中,公交系统能够获取全局的动态信息,以相邻公交车站之间的道路为场景开展网联公交的通行策略研究。以绿信比之差为主要参数,构建公交快速通行的数学模型;遗传算法与免疫思想相结合,设计亲和度,选取优秀抗体,提出一种混合遗传算子;改进自适应交叉、变异概率,提出一种基于免疫思想的公交通行策略。仿真结果表明:对比固定相位时长,采用遗传算法和基于免疫思想的通行策略均可较大程度地减少公交车的运行时间、等待时间和停车次数等重要指标;对比遗传算法的通行策略,基于免疫思想的通行策略绿信比变化降低了约8.8%,收敛速度提升了约26.8%,改进的策略减少了陷入局部最优的风险,能实现网联公交的快速通行,提升公交系统的运行效率。
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.
作者
李操
郑睿
马小陆
丁梓琼
仲俊屹
张升
齐晶晶
Li Cao;Zheng Rui;Ma Xiaolu;Ding Ziqiong;Zhong Junyi;Zhang Sheng;Qi Jingjing(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;School of Electrical and Information Engineering,Anhui University of Technology,Maanshan 243000,China;Anhui Dar Intelligent Control System Co.Ltd,Wuhu 241002,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2024年第2期449-462,共14页
Journal of System Simulation
基金
安徽省科技重大专项(202003a05020028)
安徽省重点研究与开发计划(202004a0502001)
安徽省自然科学基金(1908085MF216)
安徽省高校优秀青年支持计划(gxyq202002)。
