A Countrywide Traffic Accident Dataset

TL;DR

This paper introduces US-Accidents, a comprehensive, large-scale, publicly available dataset of 2.25 million traffic accidents across the US, including detailed contextual information to advance accident analysis and prediction research.

Contribution

The paper presents a new extensive dataset with rich contextual attributes, addressing limitations of previous small-scale or private datasets for traffic accident analysis.

Findings

Insights into spatiotemporal accident patterns

Rich contextual data enables better predictive modeling

Dataset availability facilitates broader research efforts

Abstract

Reducing traffic accidents is an important public safety challenge. However, the majority of studies on traffic accident analysis and prediction have used small-scale datasets with limited coverage, which limits their impact and applicability; and existing large-scale datasets are either private, old, or do not include important contextual information such as environmental stimuli (weather, points-of-interest, etc.). In order to help the research community address these shortcomings we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. US-Accidents currently contains data about $2.25$ million instances of traffic accidents that took place within the contiguous United States, and over the last three years. Each accident record consists of a variety of intrinsic and contextual attributes such as location, time, natural language description, weather, period-of-day, and points-of-interest. We present this dataset in this paper, along with a wide range of insights gleaned from this dataset with respect to the spatiotemporal characteristics of accidents. The dataset is publicly available at https://smoosavi.org/datasets/us_accidents.