Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models

• URL: http://arxiv.org/abs/2307.06267v1
• Date: Wed, 12 Jul 2023 16:11:57 GMT
• Title: Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models
• Authors: Yu Tang, Li Jin, Kaan Ozbay
• Abstract summary: Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. We propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those of optimization-based methods.
• Score: 7.422267768764612
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed base 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 handles not only with normal data but also with corrupted data with missing values. We verified our approach with a case study of I-210 E in California.

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