  We consider the problem of traffic accident analysis on a road network based
on road network connections and traffic volume. Previous works have designed
various deep-learning methods using historical records to predict traffic
accident occurrences. However, there is a lack of consensus on how accurate
existing methods are, and a fundamental issue is the lack of public accident
datasets for comprehensive evaluations. This paper constructs a large-scale,
unified dataset of traffic accident records from official reports of various
states in the US, totaling 9 million records, accompanied by road networks and
traffic volume reports. Using this new dataset, we evaluate existing
deep-learning methods for predicting the occurrence of accidents on road
networks. Our main finding is that graph neural networks such as GraphSAGE can
accurately predict the number of accidents on roads with less than 22% mean
absolute error (relative to the actual count) and whether an accident will
occur or not with over 87% AUROC, averaged over states. We achieve these
results by using multitask learning to account for cross-state variabilities
(e.g., availability of accident labels) and transfer learning to combine
traffic volume with accident prediction. Ablation studies highlight the
importance of road graph-structural features, amongst other features. Lastly,
we discuss the implications of the analysis and develop a package for easily
using our new dataset.
