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TripCube: A Trip-oriented vehicle trajectory data indexing structure

Article dans une revue avec comité de lecture
Author
XU, Tao
300771 East China Normal University [Shangaï] [ECNU]
527773 Key Laboratory of Big Data Analysis and Processing
ZHANG, Xihui
527773 Key Laboratory of Big Data Analysis and Processing
ccCLARAMUNT, Christophe
13094 Institut de Recherche de l'Ecole Navale [IRENAV]
XIANG, LI
300771 East China Normal University [Shangaï] [ECNU]

URI
http://hdl.handle.net/10985/15038
DOI
10.1016/j.compenvurbsys.2017.08.005
Date
2018
Journal
Computers, Environment and Urban Systems

Abstract

With the dramatic development of location-based services, a large amount of vehicle trajectory data are available and applied to different areas,while there are still many research challenges left, one of thembeing data access issues. Most of existing tree-shape indexing schemes cannot facilitate maintenance and management of very large vehicle trajectory data. Howto retrieve vehicle trajectory information efficiently requiresmore efforts. Accordingly, this paper presents a trip-oriented data indexing scheme, named TripCube, for massive vehicle trajectory data. Its principle is to represent vehicle trajectory data as trip information records and develop a three-dimensional cube-shape indexing structure to achieve trip-oriented trajectory data retrieval. In particular, the approach is implemented and applied to vehicle trajectory data in the city of Shanghai including N100 million locational records per day collected from about 13,000 taxis. TripCube is compared to two existing trajectory data indexing structures in our experiments, and the result exhibits that TripCube outperforms others.

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