Neuron-Network-Based Mixture Probability Model for Passenger Walking Time Distribution Estimation

Fang, H; Chen, CH; Chen, D; Hwang, FJ

Neuron-Network-Based Mixture Probability Model for Passenger Walking Time Distribution Estimation

Fang, H Chen, CH Chen, D Hwang, FJ

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Publisher:: Institute of Electronics, Information and Communications Engineers (IEICE)
Publication Type:: Journal Article
Citation:: IEICE Transactions on Information and Systems, 2022, E105D, (5), pp. 1112-1115
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Fang, H
dc.contributor.author	Chen, CH
dc.contributor.author	Chen, D
dc.contributor.author	Hwang, FJ
dc.date.accessioned	2023-05-11T00:47:38Z
dc.date.available	2023-05-11T00:47:38Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEICE Transactions on Information and Systems, 2022, E105D, (5), pp. 1112-1115
dc.identifier.issn	0916-8532
dc.identifier.issn	1745-1361
dc.identifier.uri	http://hdl.handle.net/10453/170314
dc.description.abstract	Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10−4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.
dc.language	en
dc.publisher	Institute of Electronics, Information and Communications Engineers (IEICE)
dc.relation.ispartof	IEICE Transactions on Information and Systems
dc.relation.isbasedon	10.1587/transinf.2021EDL8096
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0806 Information Systems, 0906 Electrical and Electronic Engineering, 1801 Law
dc.subject.classification	Information Systems
dc.title	Neuron-Network-Based Mixture Probability Model for Passenger Walking Time Distribution Estimation
dc.type	Journal Article
utslib.citation.volume	E105D
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1801 Law
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access	*
dc.date.updated	2023-05-11T00:47:37Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	E105D
utslib.citation.issue	5

Abstract:

Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10−4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.

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http://hdl.handle.net/10453/170314