Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94209
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Logistics and Maritime Studies | en_US |
dc.creator | Ke, J | en_US |
dc.creator | Feng, S | en_US |
dc.creator | Zhu, Z | en_US |
dc.creator | Yang, H | en_US |
dc.creator | Ye, J | en_US |
dc.date.accessioned | 2022-08-11T01:08:36Z | - |
dc.date.available | 2022-08-11T01:08:36Z | - |
dc.identifier.issn | 0968-090X | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/94209 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | en_US |
dc.rights | © 2021 Elsevier Ltd. All rights reserved. | en_US |
dc.rights | © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.rights | The following publication Ke, J., Feng, S., Zhu, Z., Yang, H., & Ye, J. (2021). Joint predictions of multi-modal ride-hailing demands: A deep multi-task multi-graph learning-based approach. Transportation Research Part C: Emerging Technologies, 127, 103063 is available at https://doi.org/10.1016/j.trc.2021.103063. | en_US |
dc.subject | Deep multi-task learning | en_US |
dc.subject | Demand prediction | en_US |
dc.subject | Multi-graph convolutional network | en_US |
dc.subject | Ride-hailing | en_US |
dc.title | Joint predictions of multi-modal ride-hailing demands : a deep multi-task multi-graph learning-based approach | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 127 | en_US |
dc.identifier.doi | 10.1016/j.trc.2021.103063 | en_US |
dcterms.abstract | Ride-hailing platforms generally provide various service options to customers, such as solo ride services, shared ride services, etc. It is generally expected that demands for different service modes are correlated, and the prediction of demand for one service mode can benefit from historical observations of demands for other service modes. Moreover, an accurate joint prediction of demands for multiple service modes can help the platforms better allocate and dispatch vehicle resources. Although there is a large stream of literature on ride-hailing demand predictions for one specific service mode, few efforts have been paid towards joint predictions of ride-hailing demands for multiple service modes. To address this issue, we propose a deep multi-task multi-graph learning approach, which combines two components: (1) multiple multi-graph convolutional (MGC) networks for predicting demands for different service modes, and (2) multi-task learning modules that enable knowledge sharing across multiple MGC networks. More specifically, two multi-task learning structures are established. The first one is the regularized cross-task learning, which builds cross-task connections among the inputs and outputs of multiple MGC networks. The second one is the multi-linear relationship learning, which imposes a prior tensor normal distribution on the weights of various MGC networks. Although there are no concrete bridges between different MGC networks, the weights of these networks are constrained by each other and subject to a common prior distribution. Evaluated with the for-hire-vehicle datasets in Manhattan, we show that our proposed approach outperforms the benchmark algorithms in prediction accuracy for different ride-hailing modes. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Transportation research. Part C, Emerging technologies, June 2021, v. 127, 103063 | en_US |
dcterms.isPartOf | Transportation research. Part C, Emerging technologies | en_US |
dcterms.issued | 2021-06 | - |
dc.identifier.scopus | 2-s2.0-85103776909 | - |
dc.identifier.artn | 103063 | en_US |
dc.description.validate | 202208 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | LMS-0034 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | NSFC/RGC Joint Research grant; Hong Kong University of Science and Technology - DiDi Chuxing (HKUST-DiDi) Joint Laboratory | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 55063993 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Ke_Joint_Predictions_Multi-Modal.pdf | Pre-Published version | 1.47 MB | Adobe PDF | View/Open |
Page views
49
Last Week
1
1
Last month
Citations as of May 12, 2024
Downloads
43
Citations as of May 12, 2024
SCOPUSTM
Citations
60
Citations as of May 17, 2024
WEB OF SCIENCETM
Citations
46
Citations as of May 16, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.