Competing risk mixture model and text analysis for sequential incident duration prediction |
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| Institution: | 1. Intelligent Transportation Systems Laboratory, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 0219, USA;2. Singapore-MIT Alliance for Research and Technology, 1 CREATE Way, #09-02 CREATE Tower, Singapore 138602, Singapore;3. Edmund K. Turner Professor of Department of Civil and Environmental Engineering, MIT. Room 1-181, 77 Massachusetts Avenue, Cambridge, MA 02139, USA;4. Institute of Transportation Engineering, Department of Civil Engineering, Tsinghua University, Beijing 100084, China;1. Center for Biomedical Technology, Universidad Politécnica de Madrid, Spain;2. Facultad de Informática, Universidad Politécnica de Madrid, Spain;1. NEXTRANS Center, Purdue University, West Lafayette, IN 47906, USA;2. Goergen Institute for Data Science, University of Rochester, Rochester, NY 14627, USA;3. Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA;4. Department of Civil Engineering and Engineering Mechanics, The University of Arizona, AZ 85721, USA;5. Department of Electrical and Computer Engineering, Ohio State University, OH 43210, USA;6. Department of Civil, Structural and Environmental Engineering, University at Buffalo, the State University of New York, Buffalo, NY 14260, USA;1. School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia;2. Gulf Coast Research Center for Evacuation and Transportation Resiliency, Louisiana State University, Baton Rouge, LA 74803, United States;1. Department of Civil and Environmental Engineering, South Dakota State University, Crothers Engineering Hall 132, Box 2219, Brookings, SD 57007, United States;2. Department of Civil and Environmental Engineering, The Pennsylvania State University, 231 Sackett Building, University Park, PA 16802, United States;1. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;2. Department of Civil and Environmental Engineering, National University of Singapore, 117576, Singapore |
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| Abstract: | Predicting the duration of traffic incidents sequentially during the incident clearance period is helpful in deploying efficient measures and minimizing traffic congestion related to such incidents. This study proposes a competing risk mixture hazard-based model to analyze the effect of various factors on traffic incident duration and predict the duration sequentially. First, topic modeling, a text analysis technique, is used to process the textual features of the traffic incident to extract time-dependent topics. Given four specific clearance methods and the uncertainty of these methods when used during traffic incidents, the proposed mixture model uses the multinomial logistic model and parametric hazard-based model to assess the influence of covariates on the probability of clearance methods and on the duration of the incident. Subsequently, the performance of estimated mixture model in sequentially predicting the incident duration is compared with that of the non-mixture model. The prediction results show that the presented mixture model outperforms the non-mixture model. |
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| Keywords: | Traffic incident management Incident duration prediction Competing risk mixture model Topic modeling |
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