Traffic Simulations: Multi-City Calibration of Metropolitan Highway Networks

TL;DR

This paper introduces a novel travel demand calibration method for high-resolution traffic simulators, effectively scaling across multiple metropolitan highway networks and outperforming existing algorithms in accuracy and efficiency.

Contribution

It presents a scalable calibration approach using path-level travel times, demonstrating significant improvements over SPSA in real-world multi-city case studies.

Findings

Enhanced fit to field data by 43.5% on average

Successfully calibrated 54 scenarios across 6 networks

Achieved more efficient calibration with fewer simulation calls

Abstract

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.