  Reducing traffic accidents is a crucial global public safety concern.
Accident prediction is key to improving traffic safety, enabling proactive
measures to be taken before a crash occurs, and informing safety policies,
regulations, and targeted interventions. Despite numerous studies on accident
prediction over the past decades, many have limitations in terms of
generalizability, reproducibility, or feasibility for practical use due to
input data or problem formulation. To address existing shortcomings, we propose
CrashFormer, a multi-modal architecture that utilizes comprehensive (but
relatively easy to obtain) inputs such as the history of accidents, weather
information, map images, and demographic information. The model predicts the
future risk of accidents on a reasonably acceptable cadence (i.e., every six
hours) for a geographical location of 5.161 square kilometers. CrashFormer is
composed of five components: a sequential encoder to utilize historical
accidents and weather data, an image encoder to use map imagery data, a raw
data encoder to utilize demographic information, a feature fusion module for
aggregating the encoded features, and a classifier that accepts the aggregated
data and makes predictions accordingly. Results from extensive real-world
experiments in 10 major US cities show that CrashFormer outperforms
state-of-the-art sequential and non-sequential models by 1.8% in F1-score on
average when using ``sparse'' input data.
