TAP (Traffic Accident Prediction data repository)

The Traffic Accident Prediction (TAP) data repository offers extensive coverage for 1,000 US cities (TAP-city) and 49 states (TAP-state), providing real-world road structure data that can be easily used for graph-based machine learning methods such as Graph Neural Networks. Additionally, it features multi-dimensional geospatial attributes, including angular and directional features, that are useful for analyzing transportation networks. The TAP repository has the potential to benefit the research community in various applications, including traffic crash prediction, road safety analysis, and traffic crash mitigation. The datasets can be accessed in the TAP-city and TAP-state directories.

For example, this repository can aid in traffic accident occurrence prediction and accident severity prediction. Binary labels are used to indicate whether a node contains at least one accident for the occurrence prediction task, while severity is represented by a number between 0 and 7 for the severity prediction task. A severity level of 0 denotes no accident, and 1 to 7 represents increasingly significant impacts on traffic.