Automatic classification of traffic incident's severity using machine learning approaches

Nguyen, H; Cai, C; Chen, F

Automatic classification of traffic incident's severity using machine learning approaches

Nguyen, H Cai, C Chen, F

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Publisher:: Institution of Engineering and Technology (IET)
Publication Type:: Journal Article
Citation:: IET Intelligent Transport Systems, 2017, 11, (10), pp. 615-623
Issue Date:: 2017-12-01

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Field	Value	Language
dc.contributor.author	Nguyen, H
dc.contributor.author	Cai, C
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date.accessioned	2022-08-22T03:52:15Z
dc.date.available	2022-08-22T03:52:15Z
dc.date.issued	2017-12-01
dc.identifier.citation	IET Intelligent Transport Systems, 2017, 11, (10), pp. 615-623
dc.identifier.issn	1751-956X
dc.identifier.issn	1751-9578
dc.identifier.uri	http://hdl.handle.net/10453/160673
dc.description.abstract	During daily work at a Transport Management Centre (TMC), the operators have to record and process a large volume of traffic information especially incident records. Their tasks involve manual classification of the data and then decide appropriate operations to clear the incidents on time. A real-time automatic decision support system can minimise an operator's responded time and hence reduce congestion. Besides standard descriptions (e.g. incident location, date, time, lanes affected), severity is an important criteria that operators have to evaluate based on all available information before any control commands can be issued. The NSW TMC and the research organisation Data61 in Sydney have collaborated to discover and visualise frequent patterns in historical incident response records, leading to the automatic classification of severity levels among past incidents using advanced machine learning, active learning and outlier detection techniques. The experiments were executed using 4 years TMC's incident logs from 2011 to 2014 which includes >40,000 records. The classification model achieved nearly 90% accuracy in five-fold cross-validation and is expected to help the TMC to improve its procedures, response plans, and resource allocations.
dc.language	English
dc.publisher	Institution of Engineering and Technology (IET)
dc.relation.ispartof	IET Intelligent Transport Systems
dc.relation.isbasedon	10.1049/iet-its.2017.0051
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Logistics & Transportation
dc.title	Automatic classification of traffic incident's severity using machine learning approaches
dc.type	Journal Article
utslib.citation.volume	11
utslib.location.activity	Melbourne, AUSTRALIA
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-22T03:52:12Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	10

Abstract:

During daily work at a Transport Management Centre (TMC), the operators have to record and process a large volume of traffic information especially incident records. Their tasks involve manual classification of the data and then decide appropriate operations to clear the incidents on time. A real-time automatic decision support system can minimise an operator's responded time and hence reduce congestion. Besides standard descriptions (e.g. incident location, date, time, lanes affected), severity is an important criteria that operators have to evaluate based on all available information before any control commands can be issued. The NSW TMC and the research organisation Data61 in Sydney have collaborated to discover and visualise frequent patterns in historical incident response records, leading to the automatic classification of severity levels among past incidents using advanced machine learning, active learning and outlier detection techniques. The experiments were executed using 4 years TMC's incident logs from 2011 to 2014 which includes >40,000 records. The classification model achieved nearly 90% accuracy in five-fold cross-validation and is expected to help the TMC to improve its procedures, response plans, and resource allocations.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/160673