Dynamic scheduling of rail replacement bus timetables

Yan, Z; Traish, J; Li, R; Lu, J

Dynamic scheduling of rail replacement bus timetables

Yan, Z

Traish, J Li, R Lu, J

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Publisher:: WORLD SCIENTIFIC
Publication Type:: Conference Proceeding
Citation:: Developments of Artificial Intelligence Technologies in Computation and Robotics, 2020, pp. 505-512
Issue Date:: 2020-10

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Field	Value	Language
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100
dc.contributor.author	Traish, J
dc.contributor.author	Li, R
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.date	2020-08-18
dc.date.accessioned	2021-04-22T02:45:34Z
dc.date.available	2021-04-22T02:45:34Z
dc.date.issued	2020-10
dc.identifier.citation	Developments of Artificial Intelligence Technologies in Computation and Robotics, 2020, pp. 505-512
dc.identifier.isbn	9789811223327
dc.identifier.uri	http://hdl.handle.net/10453/148278
dc.description.abstract	Disrupted urban rail services are routinely experienced by rail passengers throughout the world. To minimize the impacts to passengers, transport providers will temporarily run rail replacement bus services that use buses to replace passenger trains. This paper presents a novel data-driven bussing optimization system that can efficiently, reliably, and dynamically determine the replacement bus timetables. The system first infers travel behaviour of train passengers via data mining, then it formulates the route selection problem as a multi-objective optimization problem which is solved in a meta-heuristic way. Finally, a demand-driven scheduling approach is developed to generate the replacement bus timetable. This data-driven bussing optimization system is jointly developed and implemented by University of Technology Sydney and Sydney Trains. Deployment results show that the system not only saved cost for the transport operators, but also significantly improved customer experience in New South Wales, Australia.
dc.language	en
dc.publisher	WORLD SCIENTIFIC
dc.relation.ispartof	Developments of Artificial Intelligence Technologies in Computation and Robotics
dc.relation.ispartof	International FLINS Conference
dc.relation.isbasedon	10.1142/9789811223334_0061
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Dynamic scheduling of rail replacement bus timetables
dc.type	Conference Proceeding
utslib.location.activity	Cologne, Germany
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-22T02:45:33Z
pubs.finish-date	2020-08-21
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2020-08-18
dc.location	USA

Abstract:

Disrupted urban rail services are routinely experienced by rail passengers throughout the world. To minimize the impacts to passengers, transport providers will temporarily run rail replacement bus services that use buses to replace passenger trains. This paper presents a novel data-driven bussing optimization system that can efficiently, reliably, and dynamically determine the replacement bus timetables. The system first infers travel behaviour of train passengers via data mining, then it formulates the route selection problem as a multi-objective optimization problem which is solved in a meta-heuristic way. Finally, a demand-driven scheduling approach is developed to generate the replacement bus timetable. This data-driven bussing optimization system is jointly developed and implemented by University of Technology Sydney and Sydney Trains. Deployment results show that the system not only saved cost for the transport operators, but also significantly improved customer experience in New South Wales, Australia.

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http://hdl.handle.net/10453/148278