We propose and study a data-driven framework for identifying traffic congestion functions (numerical relationships between observations of traffic variables) at global scale and segment-level granularity. In contrast to methods that estimate a separate set of parameters for each roadway, ours learns a single black-box function over all roadways in a metropolitan area. First, we pool traffic data from all segments into one dataset, combining static attributes with dynamic time-dependent features. Second, we train a feed-forward neural network on this dataset, which we can then use on any segment in the area. We evaluate how well our framework identifies congestion functions on observed segments and how it generalizes to unobserved segments and predicts segment attributes on a large dataset covering multiple cities worldwide. For identification error on observed segments, our single data-driven congestion function compares favorably to segment-specific model-based functions on highway roads, but has room to improve on arterial roads. For generalization, our approach shows strong performance across cities and road types: both on unobserved segments in the same city and on zero-shot transfer learning between cities. Finally, for predicting segment attributes, we find that our approach can approximate critical densities for individual segments using their static properties.

翻译：本文提出并研究了一种数据驱动的框架，用于在全球尺度和路段级粒度上识别交通拥堵函数（即交通变量观测值之间的数值关系）。与为每个路段单独估计一组参数的方法不同，我们的方法学习一个覆盖大都市区所有道路的单一黑箱函数。首先，我们将所有路段的交通数据汇集到一个数据集中，结合静态属性与动态时变特征。其次，我们在此数据集上训练一个前馈神经网络，随后可将其应用于该区域内的任何路段。我们评估了该框架在已观测路段上识别拥堵函数的效果，及其在未观测路段上的泛化能力与路段属性预测能力，所用大型数据集覆盖全球多个城市。在已观测路段的识别误差方面，我们单一的数据驱动拥堵函数在高速公路上的表现优于基于模型的、针对特定路段的函数，但在主干道上仍有改进空间。在泛化能力方面，我们的方法在不同城市和道路类型上均表现出色：无论是在同一城市的未观测路段上，还是在城市间的零样本迁移学习中。最后，在预测路段属性方面，我们发现该方法可以利用路段的静态属性近似估计其临界密度。