2017
DOI: 10.1016/j.trb.2017.03.010
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Scalable space-time trajectory cube for path-finding: A study using big taxi trajectory data

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Cited by 49 publications
(28 citation statements)
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“…Step 1 is data pre-processing. After the basic process of transforming coordinates, removing abnormal trajectories and map matching, the taxi trajectories used in this study included 75.94 million GPS points with an average sampling interval of 40 s. Then, the trajectory data were decomposed into individual trips which began with a pick-up point (PUP) and ended with a drop-off point (DOP) [ 47 ]. Meanwhile, the minimum geographic unit available to the public (which we can obtain) is the sub-district.…”
Section: Case Studymentioning
confidence: 99%
“…Step 1 is data pre-processing. After the basic process of transforming coordinates, removing abnormal trajectories and map matching, the taxi trajectories used in this study included 75.94 million GPS points with an average sampling interval of 40 s. Then, the trajectory data were decomposed into individual trips which began with a pick-up point (PUP) and ended with a drop-off point (DOP) [ 47 ]. Meanwhile, the minimum geographic unit available to the public (which we can obtain) is the sub-district.…”
Section: Case Studymentioning
confidence: 99%
“…Definition 2 (Trajectory): As defined in a previous study [28], a trajectory is a sequence of GPS records collected when a taxi moves. It includes the occupied (delivery time) and empty statuses (cruising time).…”
Section: Methodology a Algorithm And Variablesmentioning
confidence: 99%
“…The first application is the movement-behavior analysis of moving objects, for example, individuals (González et al, 2008;Ando and Suzuki, 2011;Gao et al, 2013;Renso et al, 2013;Song et al, 2014) and groups (Zheng et al, 2014;Li et al, 2013;Gupta et al, 2013;McGuire et al, 2014;Liu et al, 2016). The second application is path recommendation from historical trajectories (Luo et al, 2013;Dai et al, 2015;Yang et al, 2017;Zheng et al, 2018). The third application is location prediction for tourists (Lee et al, 2016;Yu et al, 2017a), navigators (Li et al, 2016;Besse et al, 2018), and driverless vehicles .…”
Section: Related Workmentioning
confidence: 99%