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 Long Short-Term Memory (LSTM), Convolution
Neural Network (CNN), etc. Firstly, Transformer can readily handle long-term
dependencies in a data sequence. Secondly, Transformer can parallelly process
all elements in a data sequence during training. Finally, Transformer does not
have the vanishing gradient issue. Realizing the immense possibility of
Transformer, 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 INRIX's Signal Analytics Platform.
The data was parallelly formatted and stacked at different timesteps to develop
nine inTformer models. The best inTformer model achieved a sensitivity of 73%.
This model was 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. In every scenario, this inTformer
outperformed the benchmark models confirming the viability of the proposed
inTformer architecture.Comment: 29 pages, 7 figures, 9 table