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 24X faster execution time when compared against MC-Traclus.
|Original language||English (US)|
|Title of host publication||Proceedings - 2021 IEEE 37th International Conference on Data Engineering Workshops, ICDEW 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Apr 1 2021|
|Event||37th IEEE International Conference on Data Engineering Workshops, ICDEW 2021 - Virtual, Chania, Greece|
Duration: Apr 19 2021 → Apr 22 2021
|Name||Proceedings - 2021 IEEE 37th International Conference on Data Engineering Workshops, ICDEW 2021|
|Conference||37th IEEE International Conference on Data Engineering Workshops, ICDEW 2021|
|Period||4/19/21 → 4/22/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work is supported in part by the National Science Foundation under Grant No. 1302439 and 1302423.
© 2021 IEEE.
- spatio-Temporal data
- trajectory clustering