Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control. The efficacy of these systems rests on the ability to generate accurate estimates and predictions of traffic states, which necessitates online calibration. A widely used solution approach for online calibration is the Extended Kalman Filter (EKF), which – although appealing in its flexibility to incorporate any class of parameters and measurements – poses several challenges with regard to calibration accuracy and scalability, especially in congested situations for large-scale networks. This paper addresses these issues in turn so as to improve the accuracy and efficiency of EKF-based online calibration approaches for large and congested networks. First, the concept of state augmentation is revisited to handle violations of the Markovian assumption typically implicit in online applications of the EKF. Second, a method based on graph-coloring is proposed to operationalize the partitioned finite-difference approach that enhances scalability of the gradient computations.

Several synthetic experiments and a real world case study demonstrate that application of the proposed approaches yields improvements in terms of both prediction accuracy and computational performance. The work has applications in real-world deployments of simulation-based dynamic traffic assignment systems.

基于仿真的动态交通分配模型在实时交通管理和控制中具有重要的应用。这些系统的功效取决于生成交通状态的准确估计和预测的能力，这需要进行在线校准。广泛使用的在线校准解决方案方法是扩展卡尔曼滤波器（EKF），尽管其可灵活地融合任何类型的参数和测量，但它在校准精度和可扩展性方面带来了若干挑战，尤其是在大型情况下的拥挤情况下规模的网络。本文依次解决了这些问题，以提高基于EKF的大型和拥塞网络在线校准方法的准确性和效率。第一的，重新讨论了状态增强的概念，以处理违反EKF在线应用程序中通常隐含的马尔可夫假设的情况。其次，提出了一种基于图着色的方法来实现分区有限差分方法，从而提高了梯度计算的可扩展性。

几个综合实验和一个实际案例研究表明，所提出方法的应用在预测准确性和计算性能方面均产生了改进。该作品在基于仿真的动态交通分配系统的实际部署中具有应用程序。