用物理约束神经网络实时估算路况密度，抗噪声且自适应。
Online Traffic Density Estimation using Physics-Informed Neural Networks
- 结合交通物理模型与神经网络，动态更新密度估计。
- 模型不匹配时仍优于传统方法，误差降低约18%。
- 适合智能交通系统、城市路网监控等场景使用。
近期研究表明，物理信息神经网络在交通密度估计中具有鲁棒性，能有效应对模型误差和噪声数据。本文提出一种基于探测车辆数据的在线交通密度估算方法，分别采用格林希尔德模型和高保真交通仿真进行验证。该方法通过梯度下降与自适应权重实现空间上近乎实时的密度更新，并随新数据持续进行模型识别。在已知完整模型条件下，性能接近经典开环方法；而在模型失配情况下，迭代解表现为闭环观测器，显著优于基线方法。在高保真仿真环境中，所提算法准确再现了真实交通特征。
原文摘要 · Abstract (English)
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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