Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling
Mobile phone data have become a critical data source for transportation research. While a cell-id trajectory was routinely reorganized by International Mobile Subscriber Identity (IMSI), it potentially allows to analyze transportation behaviors and social interaction of total population, with a full...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/1586010 |
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| author | Kemin Zhu Junli Liu Xianfeng Song Weifeng Wang Hao Chen |
| author_facet | Kemin Zhu Junli Liu Xianfeng Song Weifeng Wang Hao Chen |
| author_sort | Kemin Zhu |
| collection | DOAJ |
| description | Mobile phone data have become a critical data source for transportation research. While a cell-id trajectory was routinely reorganized by International Mobile Subscriber Identity (IMSI), it potentially allows to analyze transportation behaviors and social interaction of total population, with a full temporal coverage at low cost. However, cell-id trajectory is often sparse due to low reporting frequency and uncertainness of mobile holders’ position. So, the cell-id trajectory refinement has been recognized as challenging work to further facilitate trajectory data mining. This paper presents a comprehensive approach to identify cell-id trajectories of public service vehicles (PSVs) from large volume of trajectories and further refines these cell-id trajectories by a heuristic global optimization approach. The modified longest common subsequence (LCSS) method is used to match a cell-id trajectory and a public transportation route (PTR) and correspondingly calculates their similarities for determining whether the trajectory is PSV mode or not. Taking full advantages of the nature of a PSV tends to move on the PTR in uniform motion to meet a prescript visit to stops, a heuristic global optimization approach is deployed to build a spatiotemporal model of a PSV motion, which estimates new locations of cell-id trajectories on the PTR. The approach was finally tested using Beijing cellular network signaling datasets. The precision of PSV trajectory detection is 90%, and the recall is 88%. Evaluated by our GNSS-logged trajectories, the mean absolute error (MAE) of refined PSV trajectories is 144.5 m and the standard deviation (St. Dev) is 81.8 m. It shows a significant improvement in comparison of traditional interpolation methods. |
| format | Article |
| id | doaj-art-c3b7de41a970494c9e76de73ab0e5063 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c3b7de41a970494c9e76de73ab0e50632025-02-03T01:28:52ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/15860101586010Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal ModellingKemin Zhu0Junli Liu1Xianfeng Song2Weifeng Wang3Hao Chen4College of Resources and Environment, Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Resources and Environment, Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Resources and Environment, Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Resources and Environment, Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Urban and Environmental Sciences, Huaiyin Normal University, Huai’an 223001, Jiangsu, ChinaMobile phone data have become a critical data source for transportation research. While a cell-id trajectory was routinely reorganized by International Mobile Subscriber Identity (IMSI), it potentially allows to analyze transportation behaviors and social interaction of total population, with a full temporal coverage at low cost. However, cell-id trajectory is often sparse due to low reporting frequency and uncertainness of mobile holders’ position. So, the cell-id trajectory refinement has been recognized as challenging work to further facilitate trajectory data mining. This paper presents a comprehensive approach to identify cell-id trajectories of public service vehicles (PSVs) from large volume of trajectories and further refines these cell-id trajectories by a heuristic global optimization approach. The modified longest common subsequence (LCSS) method is used to match a cell-id trajectory and a public transportation route (PTR) and correspondingly calculates their similarities for determining whether the trajectory is PSV mode or not. Taking full advantages of the nature of a PSV tends to move on the PTR in uniform motion to meet a prescript visit to stops, a heuristic global optimization approach is deployed to build a spatiotemporal model of a PSV motion, which estimates new locations of cell-id trajectories on the PTR. The approach was finally tested using Beijing cellular network signaling datasets. The precision of PSV trajectory detection is 90%, and the recall is 88%. Evaluated by our GNSS-logged trajectories, the mean absolute error (MAE) of refined PSV trajectories is 144.5 m and the standard deviation (St. Dev) is 81.8 m. It shows a significant improvement in comparison of traditional interpolation methods.http://dx.doi.org/10.1155/2021/1586010 |
| spellingShingle | Kemin Zhu Junli Liu Xianfeng Song Weifeng Wang Hao Chen Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling Journal of Advanced Transportation |
| title | Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling |
| title_full | Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling |
| title_fullStr | Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling |
| title_full_unstemmed | Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling |
| title_short | Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling |
| title_sort | refining sparse cell id trajectory of public service vehicles by spatiotemporal modelling |
| url | http://dx.doi.org/10.1155/2021/1586010 |
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