Analyzing Freeway Traffic Incident Clearance Time Using a Deep Survival Model
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 10
Abstract
Accurately predicting freeway incident clearance time and analyzing influential factors are essential for developing effective traffic incident management strategies. Existing approaches for analyzing traffic incident clearance time include statistical models and machine learning models. Whereas the statistical approach is able to quantify the impact of influential factors on incident clearance time, it often yields unsatisfactory levels of the prediction accuracy. Conversely, the machine learning approach lacks model interpretability but can generate accurate predictions. To combine the advantages of both approaches, a survival analysis model based on deep neural network (DeepSurv) is applied to predict the traffic incident clearance time. We used the SHapley Additive exPlanations (SHAP) method to interpret the modeling results of the DeepSurv model and analyze the impact of influential factors on traffic incident clearance time. Results show that the DeepSurv model outperforms statistical models (i.e., Cox proportional hazard, accelerated failure time and quantile regressions) and traditional machine learning models (i.e., support vector machine, random forest, and extreme gradient boosting algorithm) in terms of prediction performance. The analysis results indicate that response time, incident type (collision), lane closure type (all travel lanes blocked, total closure), involvement of fire and traffic control are significant influential factors affecting traffic incident clearance time. Our findings indicate that the proposed DeepSurv model is a more effective approach to predict traffic incident clearance time.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
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Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 10 • October 2023
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© 2023 American Society of Civil Engineers.
History
Received: Aug 19, 2022
Accepted: Jun 1, 2023
Published online: Aug 4, 2023
Published in print: Oct 1, 2023
Discussion open until: Jan 4, 2024
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