Semantic graph construction for 3D geospatial data of multi-versions

Zhou, Xingxing; Shao, Zhenfeng; Zeng, Wei; Li, Jun

doi:10.1016/j.ijleo.2013.09.057

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Multi-source mobility trajectory datasets generated by mobile phones, networks, and vehicle navigation, contain rich information about human activities. More and more researchers are working on application based on location information, including socio-economic development and wellbeing [2], human individual mobility characteristics [3,4], traffic analysis [5,6,7], travel guides [8,9,10,11], user behavior modelling [12], cross domain recommendation [13], identifying and predicting lifestyles [14], construction for 3D geospatial data [15],and so on.…”

Section: Introductionmentioning

confidence: 99%

User Identification across Asynchronous Mobility Trajectories

Wang

et al. 2019

Sensors

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With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories. This method first finds TOP-N regions whose trajectory points are most frequently distributed to reduce the computational complexity. Based on this, we discuss three methods of trajectory similarity metrics for matching tracks belonging to the same user in two datasets. We conducted extensive experiments on two real GPS trajectory datasets GeoLife and Cabspotting and comprehensively discussed the experimental results. Experimentally, our method is substantially effective and efficiency for user identification.

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