ITSC 2024 Paper Abstract

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Paper FrAT2.2

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

Keywords Simulation and Modeling, Theory and Models for Optimization and Control, Network Modeling

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.

 

 

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