车头时距
弹道
加速度
流量(计算机网络)
随机建模
计算机科学
微观交通流模型
休克(循环)
流量(数学)
标准差
模拟
控制理论(社会学)
数学
统计
交通生成模型
实时计算
物理
控制(管理)
人工智能
医学
计算机安全
经典力学
天文
内科学
几何学
作者
Linheng Li,Shuo Li,Jing Gan,Xu Qu,Bin Ran
标识
DOI:10.1080/21680566.2023.2299993
摘要
This study investigates the impact of Chinese drivers' stochastic behaviour on local car-following situations using localized trajectory data. An extended stochastic car-following model (S-IDM) is established, which considers both internal and external stochasticity. External stochasticity is characterized by different driver types, while internal stochasticity is characterized by the standard deviation of acceleration under different headway and velocity differences for the same driver. The proposed model shows advantages in terms of single-vehicle simulation accuracy and traffic shock reproduction ability, compared to traditional and existing car-following models. The model can also be extended to the evolution analysis of mixed traffic flow models, where reducing the stochasticity of human-driven vehicles is critical for optimizing and controlling traffic flow.
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