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Road safety is a major global public health concern. Effective traffic crashprediction can play a critical role in reducing road traffic accidents.However, Existing machine learning approaches tend to focus on predictingtraffic accidents in isolation, without considering the potential relationshipsbetween different accident locations within road networks. To incorporate graphstructure information, graph-based approaches such as Graph Neural Networks(GNNs) can be naturally applied. However, applying GNNs to the accidentprediction problem faces challenges due to the lack of suitablegraph-structured traffic accident datasets. To bridge this gap, we haveconstructed a real-world graph-based Traffic Accident Prediction (TAP) datarepository, along with two representative tasks: accident occurrence predictionand accident severity prediction. With nationwide coverage, real-world networktopology, and rich geospatial features, this data repository can be used for avariety of traffic-related tasks. We further comprehensively evaluate elevenstate-of-the-art GNN variants and two non-graph-based machine learning methodsusing the created datasets. Significantly facilitated by the proposed data, wedevelop a novel Traffic Accident Vulnerability Estimation via Linkage (TRAVEL)model, which is designed to capture angular and directional information fromroad networks. We demonstrate that the proposed model consistently outperformsthe baselines. The data and code are available on GitHub(https://github.com/baixianghuang/travel).

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