Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/90599
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | en_US |
dc.creator | Zhang, J | en_US |
dc.creator | Che, H | en_US |
dc.creator | Chen, F | en_US |
dc.creator | Ma, W | en_US |
dc.creator | He, Z | en_US |
dc.date.accessioned | 2021-08-04T01:52:04Z | - |
dc.date.available | 2021-08-04T01:52:04Z | - |
dc.identifier.issn | 0968-090X | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/90599 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | en_US |
dc.rights | © 2020 Elsevier Ltd. All rights reserved. | en_US |
dc.rights | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Zhang, J., Che, H., Chen, F., Ma, W., & He, Z. (2021). Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method. Transportation Research Part C: Emerging Technologies, 124, 102928 is available at https://dx.doi.org/10.1016/j.trc.2020.102928. | en_US |
dc.subject | Channel-wise attention | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Short-term origin-destination prediction | en_US |
dc.subject | Split CNN | en_US |
dc.subject | Urban rail transit | en_US |
dc.title | Short-term origin-destination demand prediction in urban rail transit systems : a channel-wise attentive split-convolutional neural network method | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 124 | en_US |
dc.identifier.doi | 10.1016/j.trc.2020.102928 | en_US |
dcterms.abstract | Short-term origin–destination (OD) flow prediction in urban rail transit (URT) plays a crucial role in smart and real-time URT operation and management. Different from other short-term traffic forecasting methods, the short-term OD flow prediction possesses three unique characteristics: (1) data availability: real-time OD flow is not available during the prediction; (2) data dimensionality: the dimension of the OD flow is much higher than the cardinality of transportation networks; (3) data sparsity: URT OD flow is spatiotemporally sparse. There is a great need to develop novel OD flow forecasting method that explicitly considers the unique characteristics of the URT system. To this end, a channel-wise attentive split–convolutional neural network (CAS-CNN) is proposed. The proposed model consists of many novel components such as the channel-wise attention mechanism and split CNN. In particular, an inflow/outflow-gated mechanism is innovatively introduced to address the data availability issue. We further originally propose a masked loss function to solve the data dimensionality and data sparsity issues. The model interpretability is also discussed in detail. The CAS–CNN model is tested on two large-scale real-world datasets from Beijing Subway, and it outperforms the rest of benchmarking methods. The proposed model contributes to the development of short-term OD flow prediction, and it also lays the foundations of real-time URT operation and management. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Transportation research. Part C, Emerging technologies, Mar. 2021, v. 124, 102928 | en_US |
dcterms.isPartOf | Transportation research. Part C, Emerging technologies | en_US |
dcterms.issued | 2021-03 | - |
dc.identifier.scopus | 2-s2.0-85099197443 | - |
dc.identifier.artn | 102928 | en_US |
dc.description.validate | 202108 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a0988-n02 | - |
dc.identifier.SubFormID | 2364 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | P0033933 | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhang_Short-Term_Origin-Destination_Rail.pdf | Pre-Published version | 2.49 MB | Adobe PDF | View/Open |
Page views
79
Last Week
1
1
Last month
Citations as of May 12, 2024
Downloads
269
Citations as of May 12, 2024
SCOPUSTM
Citations
69
Citations as of May 16, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.