用移动探针数据实时估算交通流并量化不确定性。
ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors
- 基于动态探针与边界数据的深度算子网络框架。
- 可处理稀疏不规则数据，准确捕捉拥堵传播等复杂现象。
- 适合需要实时、鲁棒交通管理的智慧城市场景。
精准的交通流估计与预测对高效交通系统管理至关重要，尤其在城市化加剧背景下。传统静态传感器空间覆盖有限，而探针车辆虽数据丰富却呈稀疏不规则分布。本文提出ON-Traffic框架，结合深度算子网络与滚动时域学习机制，利用移动探针与下游边界输入实现交通状态的在线时空估计及不确定性量化。在数值与仿真数据集上的评估表明，该模型能有效处理稀疏不规则输入，适应时间偏移场景，并生成校准良好的不确定性估计。结果证实其可捕捉冲击波与拥堵传播等复杂交通现象，且对噪声与传感器丢失具有强鲁棒性。该研究推动了在线自适应交通管理系统的发展。
原文摘要 · Abstract (English)
Accurate traffic flow estimation and prediction are critical for the efficient management of transportation systems, particularly under increasing urbanization. Traditional methods relying on static sensors often suffer from limited spatial coverage, while probe vehicles provide richer, albeit sparse and irregular data. This work introduces ON-Traffic, a novel deep operator Network and a receding horizon learning-based framework tailored for online estimation of spatio-temporal traffic state along with quantified uncertainty by using measurements from moving probe vehicles and downstream boundary inputs. Our framework is evaluated in both numerical and simulation datasets, showcasing its ability to handle irregular, sparse input data, adapt to time-shifted scenarios, and provide well-calibrated uncertainty estimates. The results demonstrate that the model captures complex traffic phenomena, including shockwaves and congestion propagation, while maintaining robustness to noise and sensor dropout. These advancements present a significant step toward online, adaptive traffic management systems.
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