Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework to estimate these demand flows in an online context. Specifically, we propose to explicitly include trip-chaining behavior within the state-space formulation, which is solved using the well-established Kalman Filtering technique. While existing works already consider structural information and recursive behavior within the online demand estimation problem, this information has been always considered at the OD level. In this study, we introduce this structural information by explicitly representing trip-chaining within the estimation framework. The advantage is twofold. First, all trips belonging to the same tour can be jointly calibrated. Second, given the estimation during a certain time interval, a prediction of the structural deviation over the whole day can be obtained without the need to run additional simulations. The effectiveness of the proposed methodology is demonstrated first on a toy network and then on a large real-world network. Results show that the model improves the prediction performance with respect to a conventional Kalman Filtering approach. We also show that, on the basis of the estimation of the morning commute, the model can be used to predict the evening commute without need of running additional simulations.

与时间有关的始发目的地（OD）需求流是动态交通分配（DTA）系统和实时交通管理的基本输入。这项工作介绍了一种新颖的状态空间框架，可以在线估计这些需求流量。具体来说，我们建议在状态空间公式中明确包含跳链行为，这可以使用公认的卡尔曼滤波技术解决。尽管现有作品已经在在线需求估计问题中考虑了结构信息和递归行为，但始终在OD级别考虑此信息。在这项研究中，我们通过在估计框架内明确表示旅行链来介绍此结构信息。优点是双重的。首先，可以对属于同一行程的所有行程进行联合校准。第二，如果在一定的时间间隔内进行估算，则无需进行额外的模拟即可获得全天结构偏差的预测。首先在玩具网络上展示了所提出方法的有效性，然后在大型现实网络上展示了该方法的有效性。结果表明，相对于传统的卡尔曼滤波方法，该模型提高了预测性能。我们还表明，基于对早上通勤的估计，该模型可以用于预测晚上通勤，而无需运行其他模拟。首先在玩具网络上展示了所提出方法的有效性，然后在大型现实网络上展示了该方法的有效性。结果表明，相对于传统的卡尔曼滤波方法，该模型提高了预测性能。我们还表明，基于对早上通勤的估计，该模型可以用于预测晚上通勤，而无需运行其他模拟。首先在玩具网络上展示了所提出方法的有效性，然后在大型现实网络上展示了该方法的有效性。结果表明，相对于传统的卡尔曼滤波方法，该模型提高了预测性能。我们还表明，基于对早上通勤的估计，该模型可以用于预测晚上通勤，而无需运行其他模拟。