The vehicle nudging behaviour suggests that a vehicle in the traffic flow may induce a ‘pushing effect’ to its preceding vehicle. In other words, while the traditional vehicle-following behaviour results in look-ahead interaction, the nudging behaviour may result in look-behind interaction: the combination of the two effects would result in bidirectional inter-vehicle interactions. Unfortunately, all reported numerical examples and traffic simulators indicating that nudging may improve the traffic flow with artificially engineered nudging behaviour. It is still unclear if such behaviour really occurs and is crucial in our roads. To address this question, this work proposes “modular” nudging models, meaning that the model is able to describe both the look-ahead-only scenario (with only vehicle-following behaviour) and the look-ahead-and-behind scenario (with both vehicle-following and nudging behaviour). We apply this modular philosophy to traditional models (optimal velocity model, intelligent driver model) and to a physics-inspired neural network model. By using the NGSIM real-world traffic data sets, the models suggest that the nudging effect plays a smaller and smaller role as the model accuracy improves.

中文翻译：

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模块化助推模型：现实世界交通数据集的制定和识别

车辆的轻推行为表明交通流中的车辆可能对其前车产生“推动效应”。换句话说，传统的车辆跟随行为会导致前视交互，而轻推行为可能会导致后视交互：两种效果的结合将导致双向车辆间交互。不幸的是，所有报告的数值示例和交通模拟器都表明微推可以通过人为设计的微推行为来改善交通流量。目前尚不清楚这种行为是否真的发生并且在我们的道路上是否至关重要。为了解决这个问题，这项工作提出了“模块化”助推模型，这意味着该模型能够描述仅前瞻场景（仅具有车辆跟随行为）和前瞻和后向场景（具有跟随车辆和轻推行为）。我们将这种模块化理念应用于传统模型（最佳速度模型、智能驾驶员模型）和受物理启发的神经网络模型。通过使用 NGSIM 真实交通数据集，模型表明，随着模型精度的提高，助推效应所起的作用越来越小。