TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks

Abstract

Road safety is a major global public health concern, and effective prediction of traffic accidents at a fine-grained spatial scale plays a critical role in reducing roadway deaths and serious injuries. However, previous studies have either overlooked implicit spatial correlations or inadequately simulated road structures due to the lack of graph-structured datasets. To bridge this gap, we introduce a graph-based Traffic Accident Prediction (TAP) data repository, along with two representative tasks: accident occurrence and severity prediction. With its real-world graph structures, comprehensive geographical coverage, and rich geospatial features, this repository has considerable potential to facilitate various traffic-related tasks. We extensively evaluate eleven Graph Neural Network (GNN) baselines using the constructed datasets. We also develop a novel GNN-based model, which can capture additional angular and directional information from road networks. We demonstrate that the proposed model consistently outperforms the baselines. The data and code are available at https://github.com/baixianghuang/travel.