Development of a maximum likelihood regression tree-based model for predicting subway incident delay

Weng, J; Zheng, Y; Qu, X; Yan, X

Development of a maximum likelihood regression tree-based model for predicting subway incident delay

Weng, J Zheng, Y Qu, X

Yan, X

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Publication Type:: Journal Article
Citation:: Transportation Research Part C: Emerging Technologies, 2015, 57 pp. 30 - 41
Issue Date:: 2015-08-01

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Field	Value	Language
dc.contributor.author	Weng, J	en_US
dc.contributor.author	Zheng, Y	en_US
dc.contributor.author	Qu, X https://orcid.org/0000-0003-0973-3756	en_US
dc.contributor.author	Yan, X	en_US
dc.date.issued	2015-08-01	en_US
dc.identifier.citation	Transportation Research Part C: Emerging Technologies, 2015, 57 pp. 30 - 41	en_US
dc.identifier.issn	0968-090X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119163
dc.description.abstract	© 2015 Elsevier Ltd. This study aims to develop a maximum likelihood regression tree-based model to predict subway incident delays, which are major negative impacts caused by subway incidents from the commuter's perspective. Using the Hong Kong subway incident data from 2005 and 2009, a tree comprising 10 terminal nodes is selected to predict subway incident delays in a case study. An accelerated failure time (AFT) analysis is conducted separately for each terminal node. The goodness-of-fit results show that our developed model outperforms the traditional AFT models with fixed and random effects because it can overcome the heterogeneity problem and over-fitting effects. The developed model is beneficial for subway engineers looking to propose effective strategies for reducing subway incident delays, especially in super-large-sized cities with huge public travel demand.	en_US
dc.relation.ispartof	Transportation Research Part C: Emerging Technologies	en_US
dc.relation.isbasedon	10.1016/j.trc.2015.06.003	en_US
dc.subject.classification	Logistics & Transportation	en_US
dc.title	Development of a maximum likelihood regression tree-based model for predicting subway incident delay	en_US
dc.type	Journal Article
utslib.citation.volume	57	en_US
utslib.for	090507 Transport Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	57	en_US

Abstract:

© 2015 Elsevier Ltd. This study aims to develop a maximum likelihood regression tree-based model to predict subway incident delays, which are major negative impacts caused by subway incidents from the commuter's perspective. Using the Hong Kong subway incident data from 2005 and 2009, a tree comprising 10 terminal nodes is selected to predict subway incident delays in a case study. An accelerated failure time (AFT) analysis is conducted separately for each terminal node. The goodness-of-fit results show that our developed model outperforms the traditional AFT models with fixed and random effects because it can overcome the heterogeneity problem and over-fitting effects. The developed model is beneficial for subway engineers looking to propose effective strategies for reducing subway incident delays, especially in super-large-sized cities with huge public travel demand.

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

http://hdl.handle.net/10453/119163