# Discovering the Precursors of Traffic Breakdowns Using Spatiotemporal Graph Attribution Networks

arXiv: https://arxiv.org/abs/2504.17109 · HTML: https://mlsanity.com/paper/2504.17109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [GraphSVX: Shapley Value Explanations for Graph Neural Networks](https://mlsanity.com/paper/2104.10482/) (2021)
- [I-24 MOTION: An instrument for freeway traffic science](https://mlsanity.com/paper/2301.11198/) (2023)
- [Virtual trajectories for I-24 MOTION: data and tools](https://mlsanity.com/paper/2311.10888/) (2023)

## Abstract (arXiv)

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.