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DC Field | Value | Language |
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dc.contributor.author | Yuan, Y | - |
dc.contributor.author | Jiang, X | - |
dc.contributor.author | Zhang, P | - |
dc.contributor.author | Lai, CS | - |
dc.date.accessioned | 2024-10-01T15:45:37Z | - |
dc.date.available | 2024-07-23 | - |
dc.date.available | 2024-10-01T15:45:37Z | - |
dc.date.issued | 2024-07-23 | - |
dc.identifier | ORCiD: Yujie Yuan https://orcid.org/0000-0002-5003-5872 | - |
dc.identifier | ORCiD: Chun Sing Lai https://orcid.org/0000-0002-4169-4438 | - |
dc.identifier | 109005 | - |
dc.identifier.citation | Yuan, Y. et al. (2024) 'Short-term high-speed rail passenger flow prediction by integrating ensemble empirical mode decomposition with multivariate grey support vector machine', Engineering Applications of Artificial Intelligence, 136, 109005, pp. 1 - 16. doi: 10.1016/j.engappai.2024.109005. | en_US |
dc.identifier.issn | 0952-1976 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29864 | - |
dc.description | Data availability: Data will be made available on request. | en_US |
dc.description | Corrigendum to “Short-term high-speed rail passenger flow prediction by integrating ensemble empirical mode decomposition with multivariate grey support vector machine” [Eng. Appl. Art. Intellig. 136PB (2024) 109005]. : The authors regret the incorrect acknowledgments in the published article. The revised acknowledgments section is as follows: Acknowledgements: This research is supported by the Fundamental Research Funds for the Central Universities, the Funds of the National Natural Science Foundation of China ( U2034208 ) and the Key Project of China State Railway Group Co., Ltd. ( N2023X034 ). The authors would like to apologise for any inconvenience caused. | - |
dc.description.abstract | Short-term prediction of high-speed rail (HSR) passenger flow provides a daily ridership estimation for the near future, which is critical to HSR planning and operational decision making. This paper proposes a new methodology that integrates ensemble empirical mode decomposition with multivariate support vector machines (EEMD-MSVM). There are four steps in this hybrid forecasting approach: (i) explore the correlation of multivariate HSR passenger flows at various stations based on archived data; (ii) decompose empirical modes of historical passenger flows for each HSR station, using EEMD to generate a number of intrinsic mode functions (IMFs) and a trend term; (iii) predict the IMF for each correlated station pair using MSVM; and (iv) reconstruct the refined IMF components to predict daily multivariate HSR passenger flows. The proposed EEMD-MSVM approach is demonstrated with multiple OD pairs along the Wuhan-Guangzhou HSR in China. Results from various origin-destination pairs, show that the EEMD-MSVM approach outperforms the existing ensemble empirical mode decomposition with grey support vector machine approach (EEMD-GSVM). With the multivariate approach, the mean absolute percentage error in demand prediction is reduced by 13.9%, 1.2%, 1.0%, 2.0%, and 2.7% and the mean absolute deviation is reduced by 78.8, 38.0, 4.4, 4.6, and 3.9, between these OD pairs respectively. Such increase in short-term demand prediction accuracy can significantly improve HSR service planning, operations, and revenue management in the real world. | en_US |
dc.description.sponsorship | This research is supported by the Fundamental Research Funds for the Central Universities, the Funds of the National Natural Science Foundation of China ( U2034208 ) and the Key Project of China State Railway Group Co., Ltd. ( N2023X034 ). | en_US |
dc.format.extent | 1 - 16 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Attribution Attribution – NonCommercial-NoDerivatives 4.0 International | - |
dc.rights | Copyright © 2024 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ (see: https://www.elsevier.com/about/policies/sharing). | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | high-speed rail | en_US |
dc.subject | passenger flow short-term forecasting | en_US |
dc.subject | ensemble empirical mode decomposition | en_US |
dc.subject | multivariate support vector machine | en_US |
dc.title | Short-term high-speed rail passenger flow prediction by integrating ensemble empirical mode decomposition with multivariate grey support vector machine | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.engappai.2024.109005 | - |
dc.relation.isPartOf | Engineering Applications of Artificial Intelligence | - |
pubs.publication-status | Published | - |
pubs.volume | 136 | - |
dc.identifier.eissn | 1873-6769 | - |
dc.rights.license | https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.en | - |
dc.rights.holder | Elsevier Ltd. | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Embargoed Research Papers |
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