  Recently, the problem of traffic accident risk forecasting has been getting
the attention of the intelligent transportation systems community due to its
significant impact on traffic clearance. This problem is commonly tackled in
the literature by using data-driven approaches that model the spatial and
temporal incident impact, since they were shown to be crucial for the traffic
accident risk forecasting problem. To achieve this, most approaches build
different architectures to capture the spatio-temporal correlations features,
making them inefficient for large traffic accident datasets. Thus, in this
work, we are proposing a novel unified framework, namely a contextual vision
transformer, that can be trained in an end-to-end approach which can
effectively reason about the spatial and temporal aspects of the problem while
providing accurate traffic accident risk predictions. We evaluate and compare
the performance of our proposed methodology against baseline approaches from
the literature across two large-scale traffic accident datasets from two
different geographical locations. The results have shown a significant
improvement with roughly 2\% in RMSE score in comparison to previous
state-of-art works (SoTA) in the literature. Moreover, our proposed approach
has outperformed the SoTA technique over the two datasets while only requiring
23x fewer computational requirements.
