This paper leverages macroscopic models and multi-source spatiotemporal data collected from automatic traffic counters and probe vehicles to accurately estimate traffic flow and travel time in links where these measurements are unavailable. This problem is critical in transportation planning applications where the sensor coverage is low and the planned interventions have network-wide impacts. The proposed model, named the Macroscopic Traffic Estimator (MaTE), can perform network-wide estimations of traffic flow and travel time only using the set of observed measurements of these quantities. Because MaTE is grounded in macroscopic flow theory, all parameters and variables are interpretable. The estimated traffic flow satisfies fundamental flow conservation constraints and exhibits an increasing monotonic relationship with the estimated travel time. Using logit-based stochastic traffic assignment as the principle for routing flow behavior makes the model fully differentiable with respect to the model parameters. This property facilitates the application of computational graphs to learn parameters from vast amounts of spatiotemporal data. We also integrate neural networks and polynomial kernel functions to capture link flow interactions and enrich the mapping of traffic flows into travel times. MaTE also adds a destination choice model and a trip generation model that uses historical data on the number of trips generated by location. Experiments on synthetic data show that the model can accurately estimate travel time and traffic flow in out-of-sample links. Results obtained using real-world multi-source data from a large-scale transportation network suggest that MaTE outperforms data-driven benchmarks, especially in travel time estimation. The estimated parameters of MaTE are also informative about the hourly change in travel demand and supply characteristics of the transportation network.

翻译：本文利用宏观模型以及从自动交通计数器和探测车辆收集的多源时空数据，精确估计无测量数据路段的交通流量和出行时间。该问题在传感器覆盖率低且规划干预措施具有网络范围影响的交通规划应用中至关重要。所提出的模型名为宏观交通估计器（MaTE），仅使用这些量的观测测量值即可进行网络范围的交通流量和出行时间估计。由于MaTE基于宏观交通流理论，所有参数和变量均具有可解释性。估计的交通流量满足基本流量守恒约束，并与估计的出行时间呈单调递增关系。采用基于逻辑模型的随机交通分配作为路径流行为原理，使模型对参数完全可微。这一特性便于应用计算图从大量时空数据中学习参数。我们还集成了神经网络和多项式核函数，以捕捉路段流量交互作用，并丰富交通流量到出行时间的映射关系。MaTE还增加了目的地选择模型和基于位置历史出行次数数据的出行生成模型。合成数据实验表明，该模型能准确估计样本外路段的出行时间和交通流量。基于大规模交通网络真实多源数据的结果显示，MaTE优于数据驱动基准模型，尤其在出行时间估计方面表现更佳。MaTE估计的参数还能揭示交通网络出行需求与供给特征的逐时变化规律。