Traffic Incident Duration Prediction: A Systematic Review of Techniques

Grigorev, A; Mihaita, A-S; Chen, F

Traffic Incident Duration Prediction: A Systematic Review of Techniques

Grigorev, A Mihaita, A-S Chen, F

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: JOURNAL OF ADVANCED TRANSPORTATION, 2024, 2024, (1)
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Grigorev, A
dc.contributor.author	Mihaita, A-S
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.editor	Truong, L
dc.date.accessioned	2025-02-04T03:04:55Z
dc.date.available	2025-02-04T03:04:55Z
dc.date.issued	2024
dc.identifier.citation	JOURNAL OF ADVANCED TRANSPORTATION, 2024, 2024, (1)
dc.identifier.issn	0197-6729
dc.identifier.issn	2042-3195
dc.identifier.uri	http://hdl.handle.net/10453/184916
dc.description.abstract	<jats:p>This systematic literature review investigates the application of machine learning (ML) techniques for predicting traffic incident durations, a crucial component of intelligent transportation systems (ITSs) aimed at mitigating congestion and enhancing environmental sustainability. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) methodology, we systematically analyze literature that overviews models for incident duration prediction. Our review identifies that while traditional ML models like XGBoost and Random Forest are prevalent, significant potential exists for advanced methodologies such as bilevel and hybrid frameworks. Key challenges identified include the following: data quality issues, model interpretability, and the complexities associated with high‐dimensional datasets. Future research directions proposed include the following: (1) development of data fusion models that integrate heterogeneous datasets of incident reports for enhanced predictive modeling; (2) utilization of natural language processing (NLP) to extract contextual information from textual incident reports; and (3) implementation of advanced ML pipelines that incorporate anomaly detection, hyperparameter optimization, and sophisticated feature selection techniques. The findings underscore the transformative potential of advanced ML methodologies in traffic incident management, contributing to the development of safer, more efficient, and environmentally sustainable transportation systems.</jats:p>
dc.language	English
dc.publisher	WILEY
dc.relation	http://purl.org/au-research/grants/arc/LP180100114
dc.relation.ispartof	JOURNAL OF ADVANCED TRANSPORTATION
dc.relation.isbasedon	10.1155/atr/3748345
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0102 Applied Mathematics, 0905 Civil Engineering, 1507 Transportation and Freight Services
dc.subject.classification	Logistics & Transportation
dc.subject.classification	3509 Transportation, logistics and supply chains
dc.subject.classification	4005 Civil engineering
dc.title	Traffic Incident Duration Prediction: A Systematic Review of Techniques
dc.type	Journal Article
utslib.citation.volume	2024
utslib.for	0102 Applied Mathematics
utslib.for	0905 Civil Engineering
utslib.for	1507 Transportation and Freight Services
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Business
pubs.organisational-group	University of Technology Sydney/Faculty of Business/Marketing Discipline
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-02-04T03:04:53Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	2024
utslib.citation.issue	1

Abstract:

This systematic literature review investigates the application of machine learning (ML) techniques for predicting traffic incident durations, a crucial component of intelligent transportation systems (ITSs) aimed at mitigating congestion and enhancing environmental sustainability. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) methodology, we systematically analyze literature that overviews models for incident duration prediction. Our review identifies that while traditional ML models like XGBoost and Random Forest are prevalent, significant potential exists for advanced methodologies such as bilevel and hybrid frameworks. Key challenges identified include the following: data quality issues, model interpretability, and the complexities associated with high‐dimensional datasets. Future research directions proposed include the following: (1) development of data fusion models that integrate heterogeneous datasets of incident reports for enhanced predictive modeling; (2) utilization of natural language processing (NLP) to extract contextual information from textual incident reports; and (3) implementation of advanced ML pipelines that incorporate anomaly detection, hyperparameter optimization, and sophisticated feature selection techniques. The findings underscore the transformative potential of advanced ML methodologies in traffic incident management, contributing to the development of safer, more efficient, and environmentally sustainable transportation systems.

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http://hdl.handle.net/10453/184916