Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models

Yu Tang (), Li Jin () and Kaan Ozbay ()
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Yu Tang: C2SMARTER Center, Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, New York 11201
Li Jin: University of Michigan-Shanghai Jiao Tong University Joint Institute and Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Kaan Ozbay: C2SMARTER Center, Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, New York 11201;

Transportation Science, 2024, vol. 58, issue 6, 1389-1402

Abstract: Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those of optimization-based methods. To this end, we combine the classical deep autoencoder, an unsupervised machine learning model consisting of one encoder and one decoder, with traffic flow models. Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic parameters given flow and speed measurements. We also introduce the denoising autoencoder into our method so that it can handle not only with normal data but also corrupted data with missing values. We verified our approach with a case study of Interstate 210 Eastbound in California. It turns out that our approach can achieve comparable performance to the-state-of-the-art calibration methods given normal data and outperform them given corrupted data with missing values.

Keywords: physics-informed machine learning; parameter identification; traffic flow models (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:58:y:2024:i:6:p:1389-1402

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