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原文传递 Analyzing Freeway Traffic Incident Clearance Time Using a Deep Survival Model

题名：	Analyzing Freeway Traffic Incident Clearance Time Using a Deep Survival Model
正文语种：	eng
作者：	Yajie Zou;Wanbing Han;Yue Zhang;Jinjun Tang;Xinzhi Zhong
作者单位：	Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji Univ. Shanghai 201804 China;Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji Univ. Shanghai 201804 China;Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji Univ. Shanghai 201804 China;School of Traffic & Transportation Engineering Central South Univ. Changsha 410075 China;Dept. of Civil and Environmental Engineering Univ. of Wisconsin-Madison Madison WI 53706
关键词：	Freeway traffic incident; Clearance time; DeepSurv model; Influential factor analysis
摘要：	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.
出版年：	2023
期刊名称：	Journal of Transportation Engineering
卷：	149
期：	10
页码：	04023101.1-04023101.9