inTformer: A Time-Embedded Attention-Based Transformer for Crash Likelihood Prediction at Intersections Using Connected Vehicle Data

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

Transformer-Conformer Ensemble for Crash Prediction Using Connected Vehicle Trajectory Data

Crash prediction is one of the important elements of real time traffic management strategies. Previous studies have demonstrated the use of infrastructure-based detector data and UAV video to predict a crash in the near future. The main limitation of such data is limited coverage. In this work, we have used connected vehicle trajectory data that can have wide coverage as well as provide insight into the trajectory that might lead to a crash. The trajectory data was provided by Wejo which collects data from the manufacturer and was spaced at 3 seconds. GPS locations and their associated time series features such as speed, acceleration and yaw rate were used to feed into an ensembled Transformer and Conformer model. A voting classifier was used to obtain the output of the final model which achieved a recall of 76% and the false alarm rate of 30%. This study showed how connected vehicle trajectory data can aid in getting insight into crashes. While most previous studies focus on using aggregated data to estimate crashes, the proposed work shows that trajectory data mining can also provide competitive results