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.
@article{arxiv.2504.17109,
title = {Discovering the Precursors of Traffic Breakdowns Using Spatiotemporal Graph Attribution Networks},
author = {Zhaobin Mo and Xiangyi Liao and Dominik A. Karbowski and Yanbing Wang},
journal= {arXiv preprint arXiv:2504.17109},
year = {2025}
}