用时空图神经网络识别交通拥堵前兆，让预测结果可解释。
Discovering the Precursors of Traffic Breakdowns Using Spatiotemporal Graph Attribution Networks
- 结合时空图网络与谢尔利值，挖掘拥堵前的异常信号。
- 在州际24号公路数据上发现道路拓扑和急刹车是主因。
- 适合交通管理、智能驾驶系统研发者参考。
理解并预测交通崩溃的前兆对于提升道路安全和交通流管理至关重要。本文提出一种新方法，将时空图神经网络（ST-GNNs）与谢尔利值（Shapley values）相结合，以识别并解释交通崩溃的前兆。通过将谢尔利解释方法扩展至时空场景，所提方法弥合了黑箱神经网络预测与可解释原因之间的鸿沟。我们在州际24号公路（Interstate-24）数据集上验证该方法，发现道路拓扑结构和突发性制动是导致交通崩溃的主要因素。
原文摘要 · Abstract (English)
Understanding and predicting the precursors of traffic breakdowns is critical for improving road safety and traffic flow management. This paper presents a novel approach combining spatiotemporal graph neural networks (ST-GNNs) with Shapley values to identify and interpret traffic breakdown precursors. By extending Shapley explanation methods to a spatiotemporal setting, our proposed method bridges the gap between black-box neural network predictions and interpretable causes. We demonstrate the method on the Interstate-24 data, and identify that road topology and abrupt braking are major factors that lead to traffic breakdowns.
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