  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.
