Network-wide crowd flow prediction of Sydney trains via customized online non-negative matrix factorization

Gong, Y; Liu, W; Li, Z; Zheng, Y; Zhang, J; Kirsch, C

Network-wide crowd flow prediction of Sydney trains via customized online non-negative matrix factorization

Gong, Y

Liu, W

Li, Z

Zheng, Y Zhang, J

Kirsch, C

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Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2018, pp. 1243 - 1252
Issue Date:: 2018-10-17

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Field	Value	Language
dc.contributor.author	Gong, Y https://orcid.org/0000-0003-3948-4471	en_US
dc.contributor.author	Liu, W https://orcid.org/0000-0002-3003-1313	en_US
dc.contributor.author	Li, Z https://orcid.org/0000-0002-4226-267X	en_US
dc.contributor.author	Zheng, Y	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Kirsch, C	en_US
dc.date.issued	2018-10-17	en_US
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2018, pp. 1243 - 1252	en_US
dc.identifier.isbn	9781450360142	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128531
dc.description.abstract	© 2018 Copyright held by the owner/author(s). Crowd Flow Prediction (CFP) is one major challenge in the intelligent transportation systems of the Sydney Trains Network. However, most advanced CFP methods only focus on entrance and exit flows at the major stations or a few subway lines, neglecting Crowd Flow Distribution (CFD) forecasting problem across the entire city network. CFD prediction plays an irreplaceable role in metro management as a tool that can help authorities plan route schedules and avoid congestion. In this paper, we propose three online non-negative matrix factorization (ONMF) models. ONMF-AO incorporates an Average Optimization strategy that adapts to stable passenger flows. ONMF-MR captures the Most Recent trends to achieve better performance when sudden changes in crowd flow occur. The Hybrid model, ONMF-H, integrates both ONMF-AO and ONMF-MR to exploit the strengths of each model in different scenarios and enhance the models' applicability to real-world situations. Given a series of CFD snapshots, both models learn the latent attributes of the train stations and, therefore, are able to capture transition patterns from one timestamp to the next by combining historic guidance. Intensive experiments on a large-scale, real-world dataset containing transactional data demonstrate the superiority of our ONMF models.	en_US
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings	en_US
dc.relation.isbasedon	10.1145/3269206.3271757	en_US
dc.title	Network-wide crowd flow prediction of Sydney trains via customized online non-negative matrix factorization	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	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 Computer Science
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/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 Copyright held by the owner/author(s). Crowd Flow Prediction (CFP) is one major challenge in the intelligent transportation systems of the Sydney Trains Network. However, most advanced CFP methods only focus on entrance and exit flows at the major stations or a few subway lines, neglecting Crowd Flow Distribution (CFD) forecasting problem across the entire city network. CFD prediction plays an irreplaceable role in metro management as a tool that can help authorities plan route schedules and avoid congestion. In this paper, we propose three online non-negative matrix factorization (ONMF) models. ONMF-AO incorporates an Average Optimization strategy that adapts to stable passenger flows. ONMF-MR captures the Most Recent trends to achieve better performance when sudden changes in crowd flow occur. The Hybrid model, ONMF-H, integrates both ONMF-AO and ONMF-MR to exploit the strengths of each model in different scenarios and enhance the models' applicability to real-world situations. Given a series of CFD snapshots, both models learn the latent attributes of the train stations and, therefore, are able to capture transition patterns from one timestamp to the next by combining historic guidance. Intensive experiments on a large-scale, real-world dataset containing transactional data demonstrate the superiority of our ONMF models.

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http://hdl.handle.net/10453/128531