Histogram-based Feature Extraction for GPS Trajectory Clustering

Authors

  • Chi Nguyen Ho Chi Minh City University of Transport image/svg+xml
  • Thao Dinh Department of Information Technology and Resources and Environment Data
  • Van-Hau Nguyen Hung Yen University of Technology and Education image/svg+xml
  • Nhat Phuong Tran Queen's University Belfast image/svg+xml
  • Anh Le Ho Chi Minh City University of Transport image/svg+xml

DOI:

https://doi.org/10.4108/eai.13-7-2018.162796

Keywords:

trajectory clustering, histogram, data clustering, GPS

Abstract

Clustering trajectories from GPS data is a crucial task for developing applications in intelligent transportation systems. Most existing approaches perform clustering on raw data consisting of series of GPS positions of moving objects over time. Such approaches are not suitable for classifying moving behaviours of vehicles, e.g., how to distinguish between a trajectory of a taxi and a trajectory of a private car. In this paper, we focus on the problem of clustering trajectories of vehicles having the same moving behaviours. Our approach is based on histogram-based feature extraction to model moving behaviours of objects and utilizes traditional clustering algorithms to group trajectories. We perform experiments on real datasets and obtain better results than existing approaches.

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Published

17-01-2020

How to Cite

Nguyen, C. ., Dinh, T. ., Nguyen, V.-H. ., Phuong Tran, N. ., & Le, A. . (2020). Histogram-based Feature Extraction for GPS Trajectory Clustering. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 7(22), e3. https://doi.org/10.4108/eai.13-7-2018.162796