Overview of traffic incident duration analysis and prediction
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Author(s)
Li, Ruimin
Pereira, Francisco C.
Ben-Akiva, Moshe E
Date Issued
May 2018
Journal
European Transport Research Review
Publisher
Springer-Verlag
Citation
Li, Ruimin et al. "Overview of traffic incident duration analysis and prediction." European Transport Research Review 10 (May 2018): 22 © 2018 The Author(s)
Version
Final published version
Abstract
Introduction
Non-recurrent congestion caused by traffic incident is difficult to predict but should be dealt with in a timely and effective manner to reduce its influence on road capacity reduction and enormous travel time loss. Influence factor analysis and reasonable prediction of traffic incident duration are important in traffic incident management to predict incident impacts and aid in the implementation of appropriate traffic operation strategies. The objective of this study is to conduct a thorough review and discusses the research evolution, mainly including the different phases of incident duration, data resources, and the various methods that are applied in the traffic incident duration influence factor analysis and duration time prediction.
Methods
In order to achieve the goal of this study, we presented a systematic review of traffic incident duration time estimation and prediction methods developed based on various data resource, methodologies etc.
Results
based on the previous studies, we analyse (i) Data resources and characteristics: different traffic incident time phases, data set size, incident types, duration time distribution, available data resources, significant influence factors and unobserved heterogeneity and randomness, (ii) traffic incident duration analysis methods, mainly including hazard-based duration model and regression and statistical tests, (iii) traffic incident duration prediction methods and evaluation of prediction accuracy.
Conclusions
After a comprehensive review of literature, this study identifies and analyses future challenges and what can be achieved in the future to estimate and predict the traffic incident duration time. Keywords: Incident duration analysis; Traffic incident duration prediction; Hazard-based duration model; Data mining; Influence factors
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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DOI of Published Version
https://doi.org/10.1186/s12544-018-0300-1
