Improving the Accuracy and Efficiency of Online Calibration for Simulation-based Dynamic Traffic Assignment

TL;DR

This paper enhances online calibration for simulation-based dynamic traffic assignment by improving EKF accuracy and scalability through state augmentation and graph-coloring techniques, demonstrated via synthetic and real-world tests.

Contribution

It introduces a novel approach combining state augmentation and graph-coloring to improve EKF-based online calibration for large, congested traffic networks.

Findings

Improved prediction accuracy in traffic state estimation.

Enhanced computational efficiency for large-scale networks.

Successful validation through synthetic and real-world case studies.

Abstract

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.