This paper presents the formulation of a family of two-regime car-following models where both free-flow and congestion regimes obey statistically independent random processes. This formulation generalizes previous efforts based on Brownian and geometric Brownian acceleration processes, each reproducing a different feature of traffic instabilities. The probability density of vehicle positions turns out to be analytical in our model, and therefore parameters can be estimated using maximum likelihood. This allows us to test a wide variety of hypotheses using statistical inference methods, such as the homogeneity of the driver/vehicle population and the statistical significance of the impacts of roadway geometry. Using data from two car-following experiments, we find that (i) model parameters are similar across repeated experiments within the same dataset but different across datasets, (ii) the acceleration error process is closer to a Brownian motion, and (iii) drivers press the gas pedal harder than usual when they come to an upgrade segment. Additionally, we explain the cause of traffic oscillations traveling downstream, which were observed both in the field data and in our simulations. The model is flexible so that newer vehicle technologies can be incorporated to test such hypotheses as differences in the car-following parameters of automated and regular vehicles, when data becomes available.

本文介绍了两种制度的汽车跟随模型的模型，其中自由流动和拥挤情况都服从统计独立的随机过程。此公式概括了基于布朗和几何布朗加速过程的先前工作，每个过程都重现了交通不稳定的不同特征。在我们的模型中，车辆位置的概率密度证明是可分析的，因此可以使用最大似然来估计参数。这使我们能够使用统计推断方法来检验各种假设，例如驾驶员/车辆人口的同质性以及道路几何形状影响的统计显着性。利用来自两次汽车跟踪实验的数据，我们发现（i）模型参数在相同数据集中的重复实验中相似，但在数据集之间不同；（ii）加速误差过程更接近布朗运动，并且（iii）驾驶员在踩油门踏板时比平时更用力进入升级阶段。此外，我们解释了向下游传播的交通振荡的原因，这在现场数据和模拟中都可以观察到。该模型具有灵活性，因此，当数据可用时，可以采用较新的车辆技术来测试诸如自动和常规车辆的跟车参数差异之类的假设。另外，我们解释了向下游传播的交通振荡的原因，这在现场数据和模拟中都可以观察到。该模型具有灵活性，因此在数据可用时，可以采用更新的车辆技术来测试诸如自动和常规车辆的跟车参数差异之类的假设。此外，我们解释了向下游传播的交通振荡的原因，这在现场数据和模拟中都可以观察到。该模型具有灵活性，因此在数据可用时，可以采用更新的车辆技术来测试诸如自动和常规车辆的跟车参数差异之类的假设。