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■ Discovering Corridors From GPS Trajectories

Zygouras, N., & Gunopulos, D. (2017, November). Discovering Corridors From GPS Trajectories. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (p. 61). ACM.
In this work a pipelined approach is proposed for detecting a set of frequently accessed paths, named as corridors, from a vast collection of trajectories. Initially we applied a well known topic modelling technique to detect frequent sets of locations and then we derived frequent paths at these locations. Our initial experimental results demonstrate the ability of our approach to summarize a large collection of trajectories to a few number of frequently accessed paths. The detection of such corridors abstracts the complex trajectories and returns the major movement patterns.

Abstract.

The increasing pervasiveness of GPS-enabled devices results in the collection of massive trajectories datasets. The vast amount of the generated location data is particularly difficult to be processed, interpreted and analyzed, due to its complexity. Nevertheless, in many cases a considerable number of moving objects share common paths and their whole trajectory can be decomposed as a sequence of such commonly accessed paths, referred as corridors. In this paper we formulate the problem of corridor discovery using GPS data that represent user trajectories. We initiate research for developing an algorithm to solve this problem efficiently and we present initial experimental results that demonstrate our approach.

Bibtex Entry.

@inproceedings{DBLP:conf/gis/ZygourasG17,
  author    = {Nikolaos Zygouras and
               Dimitrios Gunopulos},
  title     = {Discovering Corridors From {GPS} Trajectories},
  booktitle = {Proceedings of the 25th {ACM} {SIGSPATIAL} International Conference
               on Advances in Geographic Information Systems, {GIS} 2017, Redondo
               Beach, CA, USA, November 7-10, 2017},
  pages     = {61:1--61:4},
  year      = {2017},
  url       = {http://doi.acm.org/10.1145/3139958.3139994},
  doi       = {10.1145/3139958.3139994},
  timestamp = {Thu, 01 Mar 2018 16:41:44 +0100},
}