Road safety is a major global public health concern. Effective traffic crash
prediction can play a critical role in reducing road traffic accidents.
However, Existing machine learning approaches tend to focus on predicting
traffic accidents in isolation, without considering the potential relationships
between different accident locations within road networks. To incorporate graph
structure information, graph-based approaches such as Graph Neural Networks
(GNNs) can be naturally applied. However, applying GNNs to the accident
prediction problem faces challenges due to the lack of suitable
graph-structured traffic accident datasets. To bridge this gap, we have
constructed a real-world graph-based Traffic Accident Prediction (TAP) data
repository, along with two representative tasks: accident occurrence prediction
and accident severity prediction. With nationwide coverage, real-world network
topology, and rich geospatial features, this data repository can be used for a
variety of traffic-related tasks. We further comprehensively evaluate eleven
state-of-the-art GNN variants and two non-graph-based machine learning methods
using the created datasets. Significantly facilitated by the proposed data, we
develop a novel Traffic Accident Vulnerability Estimation via Linkage (TRAVEL)
model, which is designed to capture angular and directional information from
road networks. We demonstrate that the proposed model consistently outperforms
the baselines. The data and code are available on GitHub
(this https URL).

本研究旨在构建一个基于图形结构的交通事故预测数据仓库并评估不同机器学习方法的效果，通过将图形神经网络等特殊的机器学习算法应用于交通事故预测问题中，本研究展示了交通事故预测与道路网络的关系。