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

TL;DR

This paper introduces TAP, a comprehensive graph-structured dataset for traffic accident prediction, and proposes the TRAVEL model that leverages this data to improve accident vulnerability estimation.

Contribution

The paper creates a large-scale, real-world traffic accident dataset with graph structure and geospatial features, and develops the TRAVEL model utilizing this data for better prediction.

Findings

TRAVEL outperforms baseline models in accident prediction tasks.

The dataset enables effective evaluation of GNN variants.

Graph structure information improves accident prediction accuracy.

Abstract

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 (https://github.com/baixianghuang/travel).