Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94217
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Logistics and Maritime Studies | en_US |
dc.creator | Feng, S | en_US |
dc.creator | Ke, J | en_US |
dc.creator | Yang, H | en_US |
dc.creator | Ye, J | en_US |
dc.date.accessioned | 2022-08-11T01:08:42Z | - |
dc.date.available | 2022-08-11T01:08:42Z | - |
dc.identifier.issn | 1524-9050 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/94217 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Feng, S., Ke, J., Ya.ng, H., & Ye, J. (2021). A multi-task matrix factorized graph neural network for co-prediction of zone-based and od-based ride-hailing demand. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5704-5716 is available at https://doi.org/10.1109/TITS.2021.3056415 | en_US |
dc.subject | Deep multi-task learning | en_US |
dc.subject | Matrix factorization | en_US |
dc.subject | Mixture-model graph convolutional network | en_US |
dc.subject | OD-based prediction | en_US |
dc.subject | Ride-hailing | en_US |
dc.title | A multi-task matrix factorized graph neural network for co-prediction of zone-based and OD-based ride-hailing demand | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 5704 | en_US |
dc.identifier.epage | 5716 | en_US |
dc.identifier.volume | 23 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.doi | 10.1109/TITS.2021.3056415 | en_US |
dcterms.abstract | Ride-hailing service has witnessed a dramatic growth over the past decade but meanwhile raised various challenging issues, one of which is how to provide a timely and accurate short-term prediction of supply and demand. While the predictions for zone-based demand have been extensively studied, much less efforts have been paid to the predictions for origin-destination (OD) based demand (namely, demand originating from one zone to another). However, OD-based demand prediction is even more important and worth further explorations, since it provides more elaborate trip information in the near future as reference for fine-grained operations, such as the routing and matching of shared ride-hailing services that pick up and drop off two or more passengers in each ride. Simultaneous prediction of both zone-based and OD-based demand can be an interesting and practical problem for the ride-hailing platforms. To address the issue, we propose a multi-task matrix factorized graph neural network (MT-MF-GCN), which consists of two major components: (1) a GCN (graph convolutional network) basic module that captures the spatial correlations among zones via mixture-model graph convolutional (MGC) network, and (2) a matrix factorization module for multi-task predictions of zone-based and OD-based demand. By evaluations on the real-world on-demand data in Manhattan and Haikou, we show that the proposed model outperforms the state-of-the-art baseline methods in both zone- and OD-based predictions. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on intelligent transportation systems, June 2022, v. 23, no. 6, p. 5704-5716 | en_US |
dcterms.isPartOf | IEEE transactions on intelligent transportation systems | en_US |
dcterms.issued | 2022-06 | - |
dc.identifier.scopus | 2-s2.0-85102705138 | - |
dc.identifier.eissn | 1558-0016 | en_US |
dc.description.validate | 202208 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | LMS-0057 | - |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 55064224 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Ke_Multi-Task_Matrix_Factorized.pdf | Pre-Published version | 2.9 MB | Adobe PDF | View/Open |
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