An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data
With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are...
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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/23/6938 |
| _version_ | 1797545772443500544 |
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| author | Tao Wu Zhixuan Zeng Jianxin Qin Longgang Xiang Yiliang Wan |
| author_facet | Tao Wu Zhixuan Zeng Jianxin Qin Longgang Xiang Yiliang Wan |
| author_sort | Tao Wu |
| collection | DOAJ |
| description | With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are deficient in dynamic scalability and the correlations between environmental constraints and personal choices have not been investigated. This paper proposes an improved HMM-based (hidden Markov model) method for planning personalized routes with crowd sourcing spatiotemporal data. It tries to integrate the dynamic public preferences, the individual interests and the physical road network space in the same spatiotemporal framework, ensuring that reasonable routes will be generated. A novel dual-layer mapping structure has been proposed to bridge the gap from brief individual preferences to specific entries of POIs (points-of-interest) inside realistic road networks. A case study on Changsha city has proven that the proposed method can not only flexibly plan people’s travel routes under different spatiotemporal backgrounds but also is close to people’s natural selection by the perception of the group. |
| first_indexed | 2024-03-10T14:19:44Z |
| format | Article |
| id | doaj.art-ba26a6a17fa54eec8d322108d6f04b8e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:19:44Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ba26a6a17fa54eec8d322108d6f04b8e2023-11-20T23:29:28ZengMDPI AGSensors1424-82202020-12-012023693810.3390/s20236938An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal DataTao Wu0Zhixuan Zeng1Jianxin Qin2Longgang Xiang3Yiliang Wan4Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, ChinaState Key Laboratory of LIESMARS, Wuhan University, Wuhan 430079, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, ChinaWith the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are deficient in dynamic scalability and the correlations between environmental constraints and personal choices have not been investigated. This paper proposes an improved HMM-based (hidden Markov model) method for planning personalized routes with crowd sourcing spatiotemporal data. It tries to integrate the dynamic public preferences, the individual interests and the physical road network space in the same spatiotemporal framework, ensuring that reasonable routes will be generated. A novel dual-layer mapping structure has been proposed to bridge the gap from brief individual preferences to specific entries of POIs (points-of-interest) inside realistic road networks. A case study on Changsha city has proven that the proposed method can not only flexibly plan people’s travel routes under different spatiotemporal backgrounds but also is close to people’s natural selection by the perception of the group.https://www.mdpi.com/1424-8220/20/23/6938hidden Markov modelroute planningcrowd sourcing spatiotemporal data |
| spellingShingle | Tao Wu Zhixuan Zeng Jianxin Qin Longgang Xiang Yiliang Wan An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data Sensors hidden Markov model route planning crowd sourcing spatiotemporal data |
| title | An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data |
| title_full | An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data |
| title_fullStr | An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data |
| title_full_unstemmed | An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data |
| title_short | An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data |
| title_sort | improved hmm based approach for planning individual routes using crowd sourcing spatiotemporal data |
| topic | hidden Markov model route planning crowd sourcing spatiotemporal data |
| url | https://www.mdpi.com/1424-8220/20/23/6938 |
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