Online Traffic Density Estimation using Physics-Informed Neural Networks

• URL: http://arxiv.org/abs/2504.03483v1
• Date: Fri, 04 Apr 2025 14:41:22 GMT
• Title: Online Traffic Density Estimation using Physics-Informed Neural Networks
• Authors: Dennis Wilkman, Kateryna Morozovska, Karl Henrik Johansson, Matthieu Barreau,
• Abstract summary: In this paper, we introduce a methodology for online approximation of the traffic density using measurements from probe vehicles.<n>The proposed method continuously estimates the real-time traffic density in space and performs model identification with each new set of measurements.
• Score: 5.888531936968298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent works on the application of Physics-Informed Neural Networks to traffic density estimation have shown to be promising for future developments due to their robustness to model errors and noisy data. In this paper, we introduce a methodology for online approximation of the traffic density using measurements from probe vehicles in two settings: one using the Greenshield model and the other considering a high-fidelity traffic simulation. The proposed method continuously estimates the real-time traffic density in space and performs model identification with each new set of measurements. The density estimate is updated in almost real-time using gradient descent and adaptive weights. In the case of full model knowledge, the resulting algorithm has similar performance to the classical open-loop one. However, in the case of model mismatch, the iterative solution behaves as a closed-loop observer and outperforms the baseline method. Similarly, in the high-fidelity setting, the proposed algorithm correctly reproduces the traffic characteristics.

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