Early Forecast of Traffic Accident Impact Based on a Single-Snapshot Observation

Kaiqun Fu, Zhiqian Chen

Abstract

Predicting and quantifying the impact of traffic accidents is necessary and critical to Intelligent Transport Systems (ITS). As a state-of-the-art technique in graph learning, current graph neural networks heavily rely on graph Fourier transform, assuming homophily among the neighborhood. However, the homophily assumption makes it challenging to characterize abrupt signals such as traffic accidents. Our paper proposes an abrupt graph wavelet network (AGWN) to model traffic accidents and predict their time durations using only one single snapshot.

Guangyu Meng, Qisheng Jiang, Kaiqun Fu, Beiyu Lin, Chang-Tien Lu, Zhiqian Chen: Early Forecast of Traffic Accident Impact Based on a Single-Snapshot Observation, AAAI 2022: 13015-13016