A major shortcoming of the conventional car-following models is that these models only consider the current spacing and speeds of the target vehicle and its immediate leading vehicle, without taking into account prior driving actions, even for those from the same driver. In other words, the numerous prior experiences have no influence in predicting vehicular movements for the next time step. In this research, we propose a machine-learning-based data-driven methodology that is able to take advantage of the high-resolution historical traffic data in the current data-rich era, to predict vehicular movements in an accurate manner with high computational efficiency. The proposed car-following model has a simple model structure based on a fixed-radius near neighbors (FRNN) search algorithm and it can be applied to high-resolution, real-time vehicle movement prediction, modeling, and control. A comprehensive performance comparison is also conducted among the proposed car-following model, another similar data-driven model, and two conventional formula-based models. The results indicate that the FRNN algorithm-based car-following model is superior to all other three models in terms of prediction accuracy and is more computationally efficient compared to its data-driven-based counterpart. Some extensive applications of the proposed car-following model are also discussed at the end of this article.

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关于先前经验对跟车模型的影响：模型开发、计算效率、比较分析和广泛应用

传统跟车模型的一个主要缺点是这些模型只考虑目标车辆及其直接领先车辆的当前间距和速度，而没有考虑先前的驾驶行为，即使是来自同一驾驶员的行为。换句话说，大量的先前经验对预测下一个时间步长的车辆运动没有影响。在这项研究中，我们提出了一种基于机器学习的数据驱动方法，该方法能够利用当前数据丰富时代的高分辨率历史交通数据，以高计算效率准确地预测车辆运动. 所提出的跟车模型具有基于固定半径近邻 (FRNN) 搜索算法的简单模型结构，可应用于高分辨率、实时车辆运动预测、建模和控制。还对所提出的跟车模型、另一个类似的数据驱动模型和两个传统的基于公式的模型进行了综合性能比较。结果表明，基于 FRNN 算法的跟车模型在预测精度方面优于所有其他三种模型，并且与其基于数据驱动的模型相比计算效率更高。本文末尾还讨论了所提出的跟车模型的一些广泛应用。结果表明，基于 FRNN 算法的跟车模型在预测精度方面优于所有其他三种模型，并且与其基于数据驱动的模型相比计算效率更高。本文末尾还讨论了所提出的跟车模型的一些广泛应用。结果表明，基于 FRNN 算法的跟车模型在预测精度方面优于所有其他三种模型，并且与其基于数据驱动的模型相比计算效率更高。本文末尾还讨论了所提出的跟车模型的一些广泛应用。