GTraclus: a novel algorithm for local trajectory clustering on GPUs

Hamza Mustafa, Clark Barrus, Eleazar Leal, Le Gruenwald

Research output: Contribution to journalArticlepeer-review


Due to the high availability of location-based sensors like GPS, it has been possible to collect large amounts of spatio-temporal data in the form of trajectories, each of which is a sequence of spatial locations that a moving object occupies in space as time progresses. Many applications, such as intelligent transportation systems and urban planning, can benefit from clustering the trajectories of cars in each locality of a city in order to learn about traffic behavior in each neighborhood. However, the immense and ever-increasing volume of trajectory data and the concept drift present in city traffic constitute scalability challenges that have not been addressed. In order to fill this gap, we propose the first GPU algorithm for local trajectory clustering, called GTraclus. We present a parallelized trajectory partitioning algorithm which simplifies trajectories into line segments using the Minimum Description Length (MDL) principle. We evaluated our proposed algorithm using two large real-life trajectory datasets and compared it against a multicore CPU version, which we call MC-Traclus, of the popular trajectory clustering algorithm, Traclus; our experiments showed that GTraclus had on average up to 24 × faster execution time when compared against MC-Traclus.

Original languageEnglish (US)
Pages (from-to)467-488
Number of pages22
JournalDistributed and Parallel Databases
Issue number3
StatePublished - Sep 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


  • Clustering
  • GPU
  • Parallel computing
  • Spatial data
  • Trajectory


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