Analysis of Stochasticity and Heterogeneity of Car-Following Behavior Based on Data-Driven Modeling
Analysis of Stochasticity and Heterogeneity of Car-Following Behavior Based on Data-Driven Modeling
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DOI：
10.1177/03611981231169279
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发表时间：
2023-05
影响因子：
1.7
通讯作者：
Y. Shiomi;Guopeng Li-;V. Knoop
中科院分区：
文献类型：
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作者：
Y. Shiomi;Guopeng Li-;V. Knoop
高速公路上的交通动力学是随机的，因为在感知和操作的司机以及异质性之间和内部的异质性的错误。这种随机性在跟驰模型中通常用随机项来表示，为了便于数学处理，该随机项被假定为遵循正态分布。然而，这一假设的有效性尚未得到研究。在这项研究中，我们专注于一个随机项的分布形状的汽车跟随模型，预测一个时间步后的加速度。基于日本高速公路上的车辆轨迹数据，首先使用数据驱动方法开发了车辆跟驰模型，其中应用了长短期记忆（LSTM）网络。在这个LSTM网络中，加速度值被离散化，模型参数用焦点损失函数训练。预测分布的模态，标准差（SD），和IA相对于交通状态之间的关系，然后检查。调查结果表明：1）所开发的模型可以准确地预测加速度; 2）概率分布倾向于在合并点周围以及在停止和行进波的开始和沿着处具有大的SD和多模态;以及3）驾驶行为可以基于驾驶员在概率分布内采取的百分位值的变化而被分类在四个聚类中的一个中。所提出的模型和见解有助于改进微观仿真模型时，考虑新的交通管理措施。
Traffic dynamics on freeways are stochastic in nature because of errors in perception and operation of drivers as well as the heterogeneity between and within drivers. This stochasticity is often represented in car-following models by a stochastic term, which is assumed to follow a normal distribution for the convenience of mathematical processing. However, the validity of this assumption has not been studied yet. In this study, we focused on the shape of the distribution of a stochastic term in the car-following model that predicts an acceleration after a time step. Based on vehicle trajectory data on a freeway in Japan, a car-following model is first developed by using data-driven methodology in which long short-term memory (LSTM) network is applied. In this LSTM network, the acceleration value is discretized and the model parameters are trained with the focal loss function. The relationship between the predicted distributions’ modality, standard deviation (SD), and I A with respect to traffic states is then examined. The findings demonstrate that: 1) the developed model can accurately predict the accelerations; 2) a probabilistic distribution tends to have a large SD and multimodality around a merging point and at the beginning of and along stop-and-go waves; and 3) driving behavior can be classed in one of four clusters based on the variation of the percentile value that a driver takes within the probability distribution. The proposed model and the insights are helpful for improving microscopic simulation models when considering new traffic management measures.