Refueling Stop Activity Detection and Gas Station Extraction Using Crowdsourcing Vehicle Trajectory Data

doi:10.11947/j.AGCS.2017.20160657

Abstract

Abstract: In view of the deficiencies of current surveying methods of gas station, an approach is proposed to extract gas station from vehicle traces. Firstly, the spatial-temporal characteristics of individual and collective refueling behavior of trajectory is analyzed from aspects of movement features and geometric patterns. Secondly, based on Stop/Move model, the velocity sequence linear clustering algorithm is proposed to extract refueling stop tracks. Finally, using the methods including Delaunay triangulation, Fourier shape recognition and semantic constraints to identify and extract gas station. An experiment using 7 days taxi GPS traces in Beijing verified the novel method. The experimental results of 482 gas stations are extracted and the correct rate achieves to 93.1%.

Key words: vehicle trajectory, gas station, refueling activity, Stop/Move model, semantics enrichment

CLC Number:

P208

YANG Wei, AI Tinghua. Refueling Stop Activity Detection and Gas Station Extraction Using Crowdsourcing Vehicle Trajectory Data[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(7): 918-927.

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