Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development

Zhang, J; Gong, Y; Zhibin, L; Wei, L; Jinfeng, Y

Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development

Zhang, J

Gong, Y Zhibin, L Wei, L Jinfeng, Y

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Publisher:: Published by AAAI Press, Palo Alto, California USA
Publication Type:: Conference Proceeding
Citation:: ISSN 2374-3468, 2020, 34, (04), pp. 4020-4027
Issue Date:: 2020-04-03

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Field	Value	Language
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541
dc.contributor.author	Gong, Y
dc.contributor.author	Zhibin, L
dc.contributor.author	Wei, L
dc.contributor.author	Jinfeng, Y
dc.date	2020-02-07
dc.date.accessioned	2020-10-09T04:00:38Z
dc.date.available	2020-10-09T04:00:38Z
dc.date.issued	2020-04-03
dc.identifier.citation	ISSN 2374-3468, 2020, 34, (04), pp. 4020-4027
dc.identifier.isbn	9781577358350
dc.identifier.issn	2159-5399
dc.identifier.issn	2374-3468
dc.identifier.uri	http://hdl.handle.net/10453/143172
dc.description.abstract	Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.
dc.language	en
dc.publisher	Published by AAAI Press, Palo Alto, California USA
dc.relation.ispartof	ISSN 2374-3468
dc.relation.ispartof	Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20)
dc.relation.isbasedon	10.1609/aaai.v34i04.5819
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
dc.type	Conference Proceeding
utslib.citation.volume	34
utslib.location.activity	New York USA
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2020-10-09T04:00:35Z
pubs.finish-date	2020-02-12
pubs.issue	04
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2020-02-07
pubs.volume	34
utslib.citation.issue	04
dc.location	USA

Abstract:

Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.

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http://hdl.handle.net/10453/143172