Online Traffic Density Estimation using Physics-Informed Neural Networks
This work addresses real-time traffic monitoring for urban planning and management, presenting an incremental improvement by adapting existing Physics-Informed Neural Networks to online settings.
The paper tackles real-time traffic density estimation using probe vehicle measurements, introducing a method that updates estimates with gradient descent and adaptive weights, outperforming baseline methods under model mismatch and accurately reproducing traffic characteristics in high-fidelity simulations.
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.