Overview of traffic incident duration analysis and prediction
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...
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer-Verlag
2018
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| Online Access: | http://hdl.handle.net/1721.1/116290 https://orcid.org/0000-0002-9635-9987 |
| _version_ | 1826194997446180864 |
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| author | Li, Ruimin Pereira, Francisco C. Ben-Akiva, Moshe E |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Li, Ruimin Pereira, Francisco C. Ben-Akiva, Moshe E |
| author_sort | Li, Ruimin |
| collection | MIT |
| description | 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 |
| first_indexed | 2024-09-23T10:05:28Z |
| format | Article |
| id | mit-1721.1/116290 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:05:28Z |
| publishDate | 2018 |
| publisher | Springer-Verlag |
| record_format | dspace |
| spelling | mit-1721.1/1162902022-09-30T18:52:31Z Overview of traffic incident duration analysis and prediction Li, Ruimin Pereira, Francisco C. Ben-Akiva, Moshe E Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Ben-Akiva, Moshe E 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 2018-06-13T18:55:26Z 2018-06-13T18:55:26Z 2018-05 2017-11 2018-06-01T11:40:31Z Article http://purl.org/eprint/type/JournalArticle 1867-0717 1866-8887 http://hdl.handle.net/1721.1/116290 Li, Ruimin et al. "Overview of traffic incident duration analysis and prediction." European Transport Research Review 10 (May 2018): 22 © 2018 The Author(s) https://orcid.org/0000-0002-9635-9987 en https://doi.org/10.1186/s12544-018-0300-1 European Transport Research Review Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ The Author(s). application/pdf Springer-Verlag Springer International Publishing |
| spellingShingle | Li, Ruimin Pereira, Francisco C. Ben-Akiva, Moshe E Overview of traffic incident duration analysis and prediction |
| title | Overview of traffic incident duration analysis and prediction |
| title_full | Overview of traffic incident duration analysis and prediction |
| title_fullStr | Overview of traffic incident duration analysis and prediction |
| title_full_unstemmed | Overview of traffic incident duration analysis and prediction |
| title_short | Overview of traffic incident duration analysis and prediction |
| title_sort | overview of traffic incident duration analysis and prediction |
| url | http://hdl.handle.net/1721.1/116290 https://orcid.org/0000-0002-9635-9987 |
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