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.

本文通过构建一个大规模、统一的数据集，总计 900 万条交通事故记录，并结合道路网络和交通量报告，评估了现有的深度学习方法在预测道路上事故发生的准确性。研究发现，图神经网络（如 GraphSAGE）能够准确预测道路上事故的数量，平均绝对误差低于 22%（相对于实际计数），并且对于是否会发生事故的预测准确率高达 87% 以上（通过平均州统计）。通过多任务学习考虑州际变异性和迁移学习将交通量与事故预测相结合，取得这些结果。消融研究凸显了道路图结构特征的重要性。最后，讨论了分析的意义并开发了一个使用该数据集的便捷包。