| Summary: | 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.
|
|---|