无传感器区域交通预测新模型，靠外部信息补足数据空白
Generalising Traffic Forecasting to Regions without Traffic Observations
- 用物理规律和外部信号（如天气）弥补无观测数据的缺陷
- 在多个真实数据集上显著降低预测误差，泛化能力更强
- 适合交通监测覆盖不足但需精准预测的区域使用
交通预测对智能交通系统至关重要，但依赖传感器持续采集数据。由于部署与维护成本高，许多地区未配备传感器，导致现有模型难以泛化。本文提出GenCast模型，核心思路是利用外部知识弥补缺失观测，提升模型泛化能力。通过引入物理信息神经网络，将物理规律融入学习过程以约束模型；设计外部信号学习模块，挖掘交通状态与天气等外部因素的相关性；并构建空间分组模块，过滤影响泛化的局部特征。大量实验证明，GenCast在多个真实数据集上均能持续降低预测误差。
原文摘要 · Abstract (English)
Traffic forecasting is essential for intelligent transportation systems. Accurate forecasting relies on continuous observations collected by traffic sensors. However, due to high deployment and maintenance costs, not all regions are equipped with such sensors. This paper aims to forecast for regions without traffic sensors, where the lack of historical traffic observations challenges the generalisability of existing models. We propose a model named GenCast, the core idea of which is to exploit external knowledge to compensate for the missing observations and to enhance generalisation. We integrate physics-informed neural networks into GenCast, enabling physical principles to regularise the learning process. We introduce an external signal learning module to explore correlations between traffic states and external signals such as weather conditions, further improving model generalisability. Additionally, we design a spatial grouping module to filter localised features that hinder model generalisability. Extensive experiments show that GenCast consistently reduces forecasting errors on multiple real-world datasets.
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