This paper proposes an approach to perform travel demand calibration for high-resolution stochastic traffic simulators. It employs abundant travel times at the path-level, departing from the standard practice of resorting to scarce segment-level sensor counts. The proposed approach is shown to tackle high-dimensional instances in a sample-efficient way. For the first time, case studies on 6 metropolitan highway networks are carried out, considering a total of 54 calibration scenarios. This is the first work to show the ability of a calibration algorithm to systematically scale across networks. Compared to the state-of-the-art simultaneous perturbation stochastic approximation (SPSA) algorithm, the proposed approach enhances fit to field data by an average 43.5% with a maximum improvement of 80.0%, and does so within fewer simulation calls.
@article{arxiv.2501.04783,
title = {Traffic Simulations: Multi-City Calibration of Metropolitan Highway Networks},
author = {Chao Zhang and Yechen Li and Neha Arora and Damien Pierce and Carolina Osorio},
journal= {arXiv preprint arXiv:2501.04783},
year = {2025}
}
Comments
Published on the 27th IEEE International Conference on Intelligent Transportation Systems (ITSC) (2024)