Real-Time Crash Likelihood Prediction Using Temporal Attention–Based Deep Learning and Trajectory Fusion
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 7
Abstract
A crucial component of the proactive traffic safety management system is the real-time crash likelihood prediction model, which takes real-time traffic data as input and predicts the crash likelihood for the next 5+ min. This study aims to investigate the application of trajectory fusion to crash likelihood prediction and improve the predictive accuracy of the deep learning crash likelihood prediction model using the temporal attention mechanism. Two trajectory data were integrated using data fusion techniques. Specifically, trajectory data from Lynx buses and the Lytx fleet were collected using the automatic vehicle locator (AVL) and Lytx DriveCam, respectively. A deep learning model was developed for predicting real-time crash likelihood using features extracted from trajectory data. The proposed model contained a temporal attention–based long short-term memory (TA-LSTM) and a convolutional neural network (CNN). Temporal attention was introduced to capture temporal correlations between time-series data. Experimental results suggested that temporal attention could significantly improve the model’s performance on crash likelihood prediction. The proposed model outperformed other benchmark models in terms of sensitivity and false alarm rate. Moreover, trajectory fusion improved the predictive accuracy of the proposed model, which indicated the importance of having data from different types of vehicles for developing real-time crash likelihood prediction models.
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Data Availability Statement
Some or all data, models, or codes used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This research was supported by the Advanced Transportation and Congestion Management Technologies Deployment Program (ATCMTD) of the Federal Highway Administration (FHWA) and FDOT. The authors are grateful for the assistance of Jeremy Dilmore, FDOT Transportation System Management and Operations (TSM&O) Engineer. The authors would like to thank Lynx, Lytx, and Orange County for providing the data. All results and opinions are those of the authors only and do not reflect the opinion or position of the FHWA and FDOT.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 7 • July 2022
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© 2022 American Society of Civil Engineers.
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Received: Apr 16, 2021
Accepted: Mar 10, 2022
Published online: Apr 29, 2022
Published in print: Jul 1, 2022
Discussion open until: Sep 29, 2022
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