THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY
This study tries to explore the question of transport land-use change detection by large volume of vehicle trajectory data, presenting a method based on Deluanay triangulation. The whole method includes three steps. The first one is to pre-process the vehicle trajectory data including the point anom...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B8/785/2016/isprs-archives-XLI-B8-785-2016.pdf |
| _version_ | 1828764299343953920 |
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| author | T. Ai W. Yang |
| author_facet | T. Ai W. Yang |
| author_sort | T. Ai |
| collection | DOAJ |
| description | This study tries to explore the question of transport land-use change detection by large volume of vehicle trajectory data, presenting a method based on Deluanay triangulation. The whole method includes three steps. The first one is to pre-process the vehicle trajectory data including the point anomaly removing and the conversion of trajectory point to track line. Secondly, construct Deluanay triangulation within the vehicle trajectory line to detect neighborhood relation. Considering the case that some of the trajectory segments are too long, we use a interpolation measure to add more points for the improved triangulation. Thirdly, extract the transport road by cutting short triangle edge and organizing the polygon topology. We have conducted the experiment of transport land-use change discovery using the data of taxi track in Beijing City. We extract not only the transport land-use area but also the semantic information such as the transformation speed, the traffic jam distribution, the main vehicle movement direction and others. Compared with the existed transport network data, such as OpenStreet Map, our method is proved to be quick and accurate. |
| first_indexed | 2024-12-11T02:19:16Z |
| format | Article |
| id | doaj.art-e9fced990283418798c0b130b1019d7b |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-11T02:19:16Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-e9fced990283418798c0b130b1019d7b2022-12-22T01:24:07ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342016-06-01XLI-B878578810.5194/isprs-archives-XLI-B8-785-2016THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORYT. Ai0W. Yang1School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaThis study tries to explore the question of transport land-use change detection by large volume of vehicle trajectory data, presenting a method based on Deluanay triangulation. The whole method includes three steps. The first one is to pre-process the vehicle trajectory data including the point anomaly removing and the conversion of trajectory point to track line. Secondly, construct Deluanay triangulation within the vehicle trajectory line to detect neighborhood relation. Considering the case that some of the trajectory segments are too long, we use a interpolation measure to add more points for the improved triangulation. Thirdly, extract the transport road by cutting short triangle edge and organizing the polygon topology. We have conducted the experiment of transport land-use change discovery using the data of taxi track in Beijing City. We extract not only the transport land-use area but also the semantic information such as the transformation speed, the traffic jam distribution, the main vehicle movement direction and others. Compared with the existed transport network data, such as OpenStreet Map, our method is proved to be quick and accurate.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B8/785/2016/isprs-archives-XLI-B8-785-2016.pdf |
| spellingShingle | T. Ai W. Yang THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY |
| title_full | THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY |
| title_fullStr | THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY |
| title_full_unstemmed | THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY |
| title_short | THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY |
| title_sort | detection of transport land use data using crowdsourcing taxi trajectory |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B8/785/2016/isprs-archives-XLI-B8-785-2016.pdf |
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