On the Use of Abundant Road Speed Data for Travel Demand Calibration of Urban Traffic Simulators

TL;DR

This paper presents a compute-efficient, physics-based surrogate modeling approach for calibrating urban travel demand using abundant road speed data, significantly improving performance and efficiency in traffic simulation calibration.

Contribution

It introduces a novel surrogate model-based optimization method for travel demand calibration that leverages road speed data, enhancing efficiency and accuracy over existing methods.

Findings

Outperforms benchmark by 84.4% in speed accuracy

Achieves 72.2% improvement in counts accuracy

Yields high-quality solutions with few simulation evaluations

Abstract

This work develops a compute-efficient algorithm to tackle a fundamental problem in transportation: that of urban travel demand estimation. It focuses on the calibration of origin-destination travel demand input parameters for high-resolution traffic simulation models. It considers the use of abundant traffic road speed data. The travel demand calibration problem is formulated as a continuous, high-dimensional, simulation-based optimization (SO) problem with bound constraints. There is a lack of compute efficient algorithms to tackle this problem. We propose the use of an SO algorithm that relies on an efficient, analytical, differentiable, physics-based traffic model, known as a metamodel or surrogate model. We formulate a metamodel that enables the use of road speed data. Tests are performed on a Salt Lake City network. We study how the amount of data, as well as the congestion levels, impact both in-sample and out-of-sample performance. The proposed method outperforms the benchmark for both in-sample and out-of-sample performance by 84.4% and 72.2% in terms of speeds and counts, respectively. Most importantly, the proposed method yields the highest compute efficiency, identifying solutions with good performance within few simulation function evaluations (i.e., with small samples).