车头时距
卡车
计算机科学
非线性系统
理论(学习稳定性)
控制理论(社会学)
流量(计算机网络)
灵敏度(控制系统)
模拟
汽车工程
控制(管理)
人工智能
工程类
物理
机器学习
量子力学
计算机安全
电子工程
作者
Dongfang Ma,Yueyi Han,Fengzhong Qu,Sheng Jin
出处
期刊:Chinese Physics B
[IOP Publishing]
日期:2020-10-22
卷期号:30 (3): 034501-034501
被引量:14
标识
DOI:10.1088/1674-1056/abc3b3
摘要
The car-following behavior can be influenced by its driver’s backward-looking effect. Especially in traffic congestion, if vehicles adjust the headway by considering backward-looking effect, the stability of traffic flow can be enhanced. A model of car-following behavior considering backward-looking effect was built using visual information as a stimulus. The critical stability conditions were derived by linear and nonlinear stability analyses. The results of parameter sensitivity analysis indicate that the stability of traffic flow was enhanced by considering the backward-looking effect. The spatiotemporal evolution of traffic flow of different truck ratios and varying degrees of backward-looking effect was determined by numerical simulation. This study lays a foundation for exploring the complex feature of car-following behavior and making the intelligent network vehicles control rules more consistent with human driver habits.
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