A Prediction Method for Destination Based on the Semantic Transfer Model
With the widespread use of the mobile devices, destination prediction has become an important issue for location-based services (LBSs). Most existing methods are based on various Markov chain models, in which the predicted destinations are trained by historical trajectories. A problem among most of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8721085/ |
| _version_ | 1818666051067445248 |
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| author | Qilong Han Dan Lu Kejia Zhang Xiaojiang Du Mohsen Guizani |
| author_facet | Qilong Han Dan Lu Kejia Zhang Xiaojiang Du Mohsen Guizani |
| author_sort | Qilong Han |
| collection | DOAJ |
| description | With the widespread use of the mobile devices, destination prediction has become an important issue for location-based services (LBSs). Most existing methods are based on various Markov chain models, in which the predicted destinations are trained by historical trajectories. A problem among most of these follow-up works is that they blindly rely on the Markov process, ignoring the geographical distribution and the time property of the trajectories. In this paper, we propose a novel destination prediction algorithm, called STTL, based on the time property of the partial trajectory, along with the semantic transfer probability model trained in advance. We have conducted extensive experiments on the Shanghai Taxi dataset. The experimental results show that the STTL outperforms other state-of-the-art approaches. |
| first_indexed | 2024-12-17T05:58:23Z |
| format | Article |
| id | doaj.art-35f0fa1307d04a5e82edc996054c4024 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:58:23Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-35f0fa1307d04a5e82edc996054c40242022-12-21T22:00:57ZengIEEEIEEE Access2169-35362019-01-017737567376310.1109/ACCESS.2019.29185948721085A Prediction Method for Destination Based on the Semantic Transfer ModelQilong Han0Dan Lu1https://orcid.org/0000-0001-6410-8422Kejia Zhang2Xiaojiang Du3Mohsen Guizani4https://orcid.org/0000-0002-8972-8094College of Computer Science and Technology, Harbin Engineering University, Harbin, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin, ChinaDepartment of Computer and Information Sciences, Temple University, Philadelphia, PA, USADepartment of Electrical and Computer Engineering, University of Idaho, Moscow, ID, USAWith the widespread use of the mobile devices, destination prediction has become an important issue for location-based services (LBSs). Most existing methods are based on various Markov chain models, in which the predicted destinations are trained by historical trajectories. A problem among most of these follow-up works is that they blindly rely on the Markov process, ignoring the geographical distribution and the time property of the trajectories. In this paper, we propose a novel destination prediction algorithm, called STTL, based on the time property of the partial trajectory, along with the semantic transfer probability model trained in advance. We have conducted extensive experiments on the Shanghai Taxi dataset. The experimental results show that the STTL outperforms other state-of-the-art approaches.https://ieeexplore.ieee.org/document/8721085/Destination predictiontime-propertysemantic transfer probabilityhistorical trajectories |
| spellingShingle | Qilong Han Dan Lu Kejia Zhang Xiaojiang Du Mohsen Guizani A Prediction Method for Destination Based on the Semantic Transfer Model IEEE Access Destination prediction time-property semantic transfer probability historical trajectories |
| title | A Prediction Method for Destination Based on the Semantic Transfer Model |
| title_full | A Prediction Method for Destination Based on the Semantic Transfer Model |
| title_fullStr | A Prediction Method for Destination Based on the Semantic Transfer Model |
| title_full_unstemmed | A Prediction Method for Destination Based on the Semantic Transfer Model |
| title_short | A Prediction Method for Destination Based on the Semantic Transfer Model |
| title_sort | prediction method for destination based on the semantic transfer model |
| topic | Destination prediction time-property semantic transfer probability historical trajectories |
| url | https://ieeexplore.ieee.org/document/8721085/ |
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