用神经算子构建闭环交通密度估计模型，提升实时精度与鲁棒性。
Closed-Loop Neural Operator-Based Observer of Traffic Density
- 基于傅里叶神经算子学习交通流宏观动态规律。
- 闭环设计使误差在噪声下仍保持有界，优于开环预测。
- 适合交通监控、智能驾驶等需实时高精度密度估计场景。
针对路侧固定传感器稀疏测量下的交通密度估计问题，本文采用傅里叶神经算子从高保真数据中学习宏观交通流动力学。推理时，算子作为开环预测器驱动交通演化；为实现闭环，将开环算子与修正算子结合，融合预测密度与传感器稀疏观测。基于SUMO软件的仿真表明，相比开环观测器，所提闭环观测器具备抗噪声能力强、误差最终有界的经典闭环特性。该方法展示了融合学习物理规律与实时校正的优势，为构建精确、高效且可解释的数据驱动观测器开辟了新路径。
原文摘要 · Abstract (English)
We consider the problem of traffic density estimation with sparse measurements from stationary roadside sensors. Our approach uses Fourier neural operators to learn macroscopic traffic flow dynamics from high-fidelity data. During inference, the operator functions as an open-loop predictor of traffic evolution. To close the loop, we couple the open-loop operator with a correction operator that combines the predicted density with sparse measurements from the sensors. Simulations with the SUMO software indicate that, compared to open-loop observers, the proposed closed-loop observer exhibits classical closed-loop properties such as robustness to noise and ultimate boundedness of the error. This shows the advantages of combining learned physics with real-time corrections, and opens avenues for accurate, efficient, and interpretable data-driven observers.
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