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- Publisher Website: 10.1109/TITS.2021.3056415
- Scopus: eid_2-s2.0-85102705138
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Article: A Multi-Task Matrix Factorized Graph Neural Network for Co-Prediction of Zone-Based and OD-Based Ride-Hailing Demand
Title | A Multi-Task Matrix Factorized Graph Neural Network for Co-Prediction of Zone-Based and OD-Based Ride-Hailing Demand |
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Authors | |
Keywords | Ride-hailing OD-based prediction Mixture-model graph convolutional network Matrix factorization Deep multi-task learning |
Issue Date | 2021 |
Citation | IEEE Transactions on Intelligent Transportation Systems, 2021 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/308846 |
ISSN | 2023 Impact Factor: 7.9 2023 SCImago Journal Rankings: 2.580 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Feng, Siyuan | - |
dc.contributor.author | Ke, Jintao | - |
dc.contributor.author | Yang, Hai | - |
dc.contributor.author | Ye, Jieping | - |
dc.date.accessioned | 2021-12-08T07:50:15Z | - |
dc.date.available | 2021-12-08T07:50:15Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Transactions on Intelligent Transportation Systems, 2021 | - |
dc.identifier.issn | 1524-9050 | - |
dc.identifier.uri | http://hdl.handle.net/10722/308846 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Intelligent Transportation Systems | - |
dc.subject | Ride-hailing | - |
dc.subject | OD-based prediction | - |
dc.subject | Mixture-model graph convolutional network | - |
dc.subject | Matrix factorization | - |
dc.subject | Deep multi-task learning | - |
dc.title | A Multi-Task Matrix Factorized Graph Neural Network for Co-Prediction of Zone-Based and OD-Based Ride-Hailing Demand | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TITS.2021.3056415 | - |
dc.identifier.scopus | eid_2-s2.0-85102705138 | - |
dc.identifier.eissn | 1558-0016 | - |
dc.identifier.isi | WOS:000733512300001 | - |