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GTraclus: a novel algorithm for local trajectory clustering on GPUs

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Abstract

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 \(24\times\) faster execution time when compared against MC-Traclus.

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Acknowledgements

This work is supported in part by the National Science Foundation under Grant Nos. 1302439 and 1302423.

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Authors and Affiliations

  1. Department of Computer Science, University of Minnesota Duluth, Duluth, MN, USA

    Hamza Mustafa & Eleazar Leal

  2. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Clark Barrus & Le Gruenwald

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  1. Hamza Mustafa
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  3. Eleazar Leal
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Correspondence to Eleazar Leal.

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Mustafa, H., Barrus, C., Leal, E. et al. GTraclus: a novel algorithm for local trajectory clustering on GPUs. Distrib Parallel Databases 41, 467–488 (2023). https://doi.org/10.1007/s10619-023-07429-x

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