The simulation of traffic flow on networks requires knowledge on the behavior across traffic intersections. For macroscopic models based on hyperbolic conservation laws, there exist nowadays many ad‐hoc models describing this behavior. Based on real‐world car trajectory data we propose a new class of data‐driven models with the requirements of being consistent to networked hyperbolic traffic flow models. To this end, the new models combine artificial neural networks with a parametrization of the solution space to the half‐Riemann problem at the junction. A method for deriving density and flux corresponding to the traffic close to the junction for data‐driven models is presented. The model parameters are fitted to obtain suitable boundary conditions for macroscopic first‐ and second‐order traffic flow models. The prediction of various models are compared considering also existing coupling rules at the junction. Numerical results imposing the data‐fitted coupling models on a traffic network are presented exhibiting accurate predictions of the new models.

中文翻译：

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路口交通流的数据驱动模型

网络交通流的模拟需要了解交通路口的行为。对于基于双曲守恒定律的宏观模型，现在存在许多描述这种行为的临时模型。基于现实世界的汽车轨迹数据，我们提出了一类新的数据驱动模型，要求与网络双曲线交通流模型保持一致。为此，新模型将人工神经网络与交界处半黎曼问题的解空间参数化相结合。提出了一种为数据驱动模型导出与路口附近交通相对应的密度和通量的方法。拟合模型参数以获得宏观一阶和二阶交通流模型的合适边界条件。还考虑了连接处现有的耦合规则，对各种模型的预测进行了比较。将数据拟合耦合模型应用于交通网络的数值结果展示了新模型的准确预测。