Zhang, Chao (Google Research), Li, Yechen (Google Research), Arora, Neha (Google Research), Pierce, Damien (Google), Osorio, Carolina (Google Research, HEC Montreal)

Traffic Simulations: Multi-City Calibration of Metropolitan Highway Networks

Scheduled for presentation during the Invited Session "Large-scale Smart Mobility" (FrAT2), Friday, September 27, 2024, 10:50−11:10, Salon 5

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 7, 2024

Abstract

This paper proposes an approach to perform travel demand calibration for high-resolution stochastic urban 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.