Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 785-788, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-785-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
23 Jun 2016
THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY
T. Ai and W. Yang School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Keywords: Crowdsourcing, Movement Trajectory, Land-use Data Update, Delaunay Triangulations Abstract. 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.
Conference paper (PDF, 1107 KB)


Citation: Ai, T. and Yang, W.: THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 785-788, https://doi.org/10.5194/isprs-archives-XLI-B8-785-2016, 2016.

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