A novel trajectory similarity–based approach for location prediction
Location prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people’s future whereabouts hard to predict, although studies have proved that human mobility shows strong re...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2016-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147716678426 |
| _version_ | 1797710918184861696 |
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| author | Zelei Liu Liang Hu Chunyi Wu Yan Ding Jia Zhao |
| author_facet | Zelei Liu Liang Hu Chunyi Wu Yan Ding Jia Zhao |
| author_sort | Zelei Liu |
| collection | DOAJ |
| description | Location prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people’s future whereabouts hard to predict, although studies have proved that human mobility shows strong regularity. Most previous works, in general, tend to discover an association between a user’s social relations in real world and variances in trajectory and then utilize this association to model the user’s mobility which is used for location prediction. However, these methods normally require some specific data, which make them hard to be migrated to other platforms. Moreover, by focusing on social relations, these methods neglect the potential value of the associations among strangers’ trajectory. Based on this argument, this article has proposed a novel location prediction approach trajectory similarity–based location prediction. It applies the social contagion theory and introduces a novel similarity computing-based trajectory method along with the trajectory sampling, which is achieved by covering algorithm to accelerate the process of computing similarity. Experiment results on real dataset show that trajectory similarity–based location prediction achieves higher accuracy and stability comparing to the state-of-the-art approaches. |
| first_indexed | 2024-03-12T06:58:16Z |
| format | Article |
| id | doaj.art-9fb37ac289ea4b5daf9b2a8f0dada2c5 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T06:58:16Z |
| publishDate | 2016-11-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-9fb37ac289ea4b5daf9b2a8f0dada2c52023-09-02T23:53:23ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772016-11-011210.1177/1550147716678426A novel trajectory similarity–based approach for location predictionZelei Liu0Liang Hu1Chunyi Wu2Yan Ding3Jia Zhao4College of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaHigh-Assurance Software Laboratory, INESC TEC & University of Minho, Braga, PortugalLocation prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people’s future whereabouts hard to predict, although studies have proved that human mobility shows strong regularity. Most previous works, in general, tend to discover an association between a user’s social relations in real world and variances in trajectory and then utilize this association to model the user’s mobility which is used for location prediction. However, these methods normally require some specific data, which make them hard to be migrated to other platforms. Moreover, by focusing on social relations, these methods neglect the potential value of the associations among strangers’ trajectory. Based on this argument, this article has proposed a novel location prediction approach trajectory similarity–based location prediction. It applies the social contagion theory and introduces a novel similarity computing-based trajectory method along with the trajectory sampling, which is achieved by covering algorithm to accelerate the process of computing similarity. Experiment results on real dataset show that trajectory similarity–based location prediction achieves higher accuracy and stability comparing to the state-of-the-art approaches.https://doi.org/10.1177/1550147716678426 |
| spellingShingle | Zelei Liu Liang Hu Chunyi Wu Yan Ding Jia Zhao A novel trajectory similarity–based approach for location prediction International Journal of Distributed Sensor Networks |
| title | A novel trajectory similarity–based approach for location prediction |
| title_full | A novel trajectory similarity–based approach for location prediction |
| title_fullStr | A novel trajectory similarity–based approach for location prediction |
| title_full_unstemmed | A novel trajectory similarity–based approach for location prediction |
| title_short | A novel trajectory similarity–based approach for location prediction |
| title_sort | novel trajectory similarity based approach for location prediction |
| url | https://doi.org/10.1177/1550147716678426 |
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