  The real-time crash likelihood prediction model is an essential component of
the proactive traffic safety management system. Over the years, numerous
studies have attempted to construct a crash likelihood prediction model in
order to enhance traffic safety, but mostly on freeways. In the majority of the
existing studies, researchers have primarily employed a deep learning-based
framework to identify crash potential. Lately, Transformer has emerged as a
potential deep neural network that fundamentally operates through
attention-based mechanisms. Transformer has several functional benefits over
extant deep learning models such as LSTM, CNN, etc. Firstly, Transformer can
readily handle long-term dependencies in a data sequence. Secondly,
Transformers can parallelly process all elements in a data sequence during
training. Finally, a Transformer does not have the vanishing gradient issue.
Realizing the immense possibility of Transformers, this paper proposes
inTersection-Transformer (inTformer), a time-embedded attention-based
Transformer model that can effectively predict intersection crash likelihood in
real-time. The proposed model was evaluated using connected vehicle data
extracted from Signal Analytics Platform. Acknowledging the complex traffic
operation mechanism at intersection, this study developed zone-specific models
by dividing the intersection region into two distinct zones:
within-intersection and approach zone. The best inTformer models in
'within-intersection,' and 'approach' zone achieved a sensitivity of 73%, and
70%, respectively. The zone-level models were also compared to earlier studies
on crash likelihood prediction at intersections and with several established
deep learning models trained on the same connected vehicle dataset.
