| 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.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 | embargoed 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 | Not applicable | 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 |
| dc.date.embargo | 2023.03.31 | en_US |
| Appears in Collections: | Journal/Magazine Article | |
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