  Reducing traffic accidents is an important public safety challenge,
therefore, accident analysis and prediction has been a topic of much research
over the past few decades. Using small-scale datasets with limited coverage,
being dependent on extensive set of data, and being not applicable for
real-time purposes are the important shortcomings of the existing studies. To
address these challenges, we propose a new solution for real-time traffic
accident prediction using easy-to-obtain, but sparse data. Our solution relies
on a deep-neural-network model (which we have named DAP, for Deep Accident
Prediction); which utilizes a variety of data attributes such as traffic
events, weather data, points-of-interest, and time. DAP incorporates multiple
components including a recurrent (for time-sensitive data), a fully connected
(for time-insensitive data), and a trainable embedding component (to capture
spatial heterogeneity). To fill the data gap, 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. By employing the US-Accidents dataset and through an extensive
set of experiments across several large cities, we have evaluated our proposal
against several baselines. Our analysis and results show significant
improvements to predict rare accident events. Further, we have shown the impact
of traffic information, time, and points-of-interest data for real-time
accident prediction.
