Citation

Li R, Pereira FC, Ben-Akiva ME. Eur. Transp. Res. Rev. 2018; 10(2): e22.

Copyright

(Copyright © 2018, European Conference of Transport Research Institutes, Publisher Holtzbrinck Springer Nature Publishing Group)

DOI

10.1186/s12544-018-0300-1

PMID

unavailable

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.

Language: en

Keywords

Data mining; Hazard-based duration model; Incident duration analysis; Influence factors; Traffic incident duration prediction