ABSTRACT Connected vehicles (CVs) are considered to have the potential to significantly improve traffic flow stability. Although several studies have been devoted to modelling car-following behaviour in a connected environment, most model formulations are based on assumptions without empirical observations. Therefore, this paper utilizes data from field experiments to explore the dynamics of CVs. Data mining analysis shows that the driver is more responsive to velocity differences with safety messages. According to the data analysis results, we present a modified car-following model based on the intelligent driver model (IDM). Then, the parameters of our modified IDM are calibrated. It is shown that the modified IDM is able to reproduce the observed experimental data better than the original IDM. Next, we conduct a linear stability analysis of the modified IDM to explore the properties of the model. Finally, simulation experiments are conducted to verify the theoretical analysis.

中文翻译：

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用于评估 V2V 消息对交通动态影响的跟驰模型

摘要 联网车辆（CV）被认为具有显着提高交通流量稳定性的潜力。尽管有几项研究致力于对互联环境中的跟车行为进行建模，但大多数模型公式都基于没有经验观察的假设。因此，本文利用来自现场实验的数据来探索 CV 的动力学。数据挖掘分析表明，驾驶员对安全信息的速度差异更敏感。根据数据分析结果，我们提出了一种基于智能驾驶员模型（IDM）的改进型跟驰模型。然后，校准我们修改后的 IDM 的参数。结果表明，修改后的 IDM 能够比原始 IDM 更好地再现观察到的实验数据。下一个，我们对修改后的 IDM 进行线性稳定性分析，以探索模型的特性。最后通过仿真实验对理论分析进行验证。