An Automatic Extraction Method of Coach Operation Information from Historical Trajectory Data
Quality of travel service for road transport relies heavily on richness of transport operation data. Currently, most types of data including coach operation data are collected by manual investigation which is time-consuming and labor-intensive, and this significantly hinders the realization of intel...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/3634942 |
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| _version_ | 1832548836758781952 |
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| author | Jun Li Qingqi Li Yan Zhu Yan Ma Yubin Xu Chao Xie |
| author_facet | Jun Li Qingqi Li Yan Zhu Yan Ma Yubin Xu Chao Xie |
| author_sort | Jun Li |
| collection | DOAJ |
| description | Quality of travel service for road transport relies heavily on richness of transport operation data. Currently, most types of data including coach operation data are collected by manual investigation which is time-consuming and labor-intensive, and this significantly hinders the realization of intelligent traffic information service. In view of the above problems, this paper is aimed at introducing a method of automatically extracting coach operation information using historical GPS trajectory data of massive coaches. The method first analyzes trajectory characteristics of coaches within stations and identifies the highly dense point clusters as coach stations using the DBSCAN clustering algorithm. Then the schedule information is obtained by conducting error adjustment on the actual arrival and departure time series of multiple shifts, and the name of coach station is queried from point of interest (POI) and geographical name database provided by online map. Finally, the regular driving route of coaches is extracted by an incremental trajectory merging method. The proposed method is applied in handling historical trajectory data in the Beijing-Tianjin-Hebei region in China, and experimental results show that the extraction accuracy is 84% and verify its effectiveness and feasibility. The proposed method makes use of data mining techniques to extract coach operation information from big trajectory data and saves a lot of labor work, time, and economic cost required by on-site investigation. |
| format | Article |
| id | doaj-art-a0098672d6da45708255166824c46af2 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-a0098672d6da45708255166824c46af22025-02-03T06:12:55ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/36349423634942An Automatic Extraction Method of Coach Operation Information from Historical Trajectory DataJun Li0Qingqi Li1Yan Zhu2Yan Ma3Yubin Xu4Chao Xie5College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaChina Academy of Civil Aviation Science and Technology, Beijing, ChinaChina Academy of Civil Aviation Science and Technology, Beijing, ChinaNational Engineering Laboratory for Transportation Safety and Emergency Informatics, China Transport Telecommunications & Information Center, Beijing, ChinaQuality of travel service for road transport relies heavily on richness of transport operation data. Currently, most types of data including coach operation data are collected by manual investigation which is time-consuming and labor-intensive, and this significantly hinders the realization of intelligent traffic information service. In view of the above problems, this paper is aimed at introducing a method of automatically extracting coach operation information using historical GPS trajectory data of massive coaches. The method first analyzes trajectory characteristics of coaches within stations and identifies the highly dense point clusters as coach stations using the DBSCAN clustering algorithm. Then the schedule information is obtained by conducting error adjustment on the actual arrival and departure time series of multiple shifts, and the name of coach station is queried from point of interest (POI) and geographical name database provided by online map. Finally, the regular driving route of coaches is extracted by an incremental trajectory merging method. The proposed method is applied in handling historical trajectory data in the Beijing-Tianjin-Hebei region in China, and experimental results show that the extraction accuracy is 84% and verify its effectiveness and feasibility. The proposed method makes use of data mining techniques to extract coach operation information from big trajectory data and saves a lot of labor work, time, and economic cost required by on-site investigation.http://dx.doi.org/10.1155/2019/3634942 |
| spellingShingle | Jun Li Qingqi Li Yan Zhu Yan Ma Yubin Xu Chao Xie An Automatic Extraction Method of Coach Operation Information from Historical Trajectory Data Journal of Advanced Transportation |
| title | An Automatic Extraction Method of Coach Operation Information from Historical Trajectory Data |
| title_full | An Automatic Extraction Method of Coach Operation Information from Historical Trajectory Data |
| title_fullStr | An Automatic Extraction Method of Coach Operation Information from Historical Trajectory Data |
| title_full_unstemmed | An Automatic Extraction Method of Coach Operation Information from Historical Trajectory Data |
| title_short | An Automatic Extraction Method of Coach Operation Information from Historical Trajectory Data |
| title_sort | automatic extraction method of coach operation information from historical trajectory data |
| url | http://dx.doi.org/10.1155/2019/3634942 |
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