Overview of traffic incident duration analysis and prediction

• Published in: European transport research review 2018-06, Vol.10 (2), p.1-13, Article 22
• Main Authors: Li, Ruimin, Pereira, Francisco C., Ben-Akiva, Moshe E.
• Format: Article
• Language: English
• Subjects: Automotive Engineering; Civil Engineering; Data mining; Engineering; Factor analysis; Hazard-based duration model; Incident duration analysis; Influence factors; Literature reviews; Original Paper; Regional/Spatial Science; Regression analysis; Regression models; Statistical analysis; Statistical tests; Traffic congestion; Traffic incident duration prediction; Traffic management; Traffic models; Transportation; Transportation research; Travel time
• ISSN: 1867-0717; 1866-8887
• DOI: 10.1186/s12544-018-0300-1

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.