- Title
- Vehicle trajectory clustering based on dynamic representation learning of internet of vehicles
- Creator
- Wang, Wei; Xia, Feng; Nie, Hansong; Chen, Zhikui; Gong, Zhiguo
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/177919
- Identifier
- vital:15366
- Identifier
-
https://doi.org/10.1109/TITS.2020.2995856
- Identifier
- ISBN:1524-9050 (ISSN)
- Abstract
- With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods. © 2000-2011 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 6 (2021), p. 3567-3576
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2020 IEEE
- Rights
- Open Access
- Subject
- 0801 Artificial Intelligence and Image Processing; 0905 Civil Engineering; 1507 Transportation and Freight Services; Internet of Vehicles; Network representation learning; Vehicle trajectory clustering
- Full Text
- Reviewed
- Funder
- This work is funded by National Key R&D Program of China (2019YFB1600700), The Science and Technology Development Fund, Macau SAR (SKL-IOTSC-2018-2020, FDCT/0045/2019/A1, FDCT/007/2016/AFJ), Guangzhou Science and Technology Innovation and Development Commission (EF005/FST-GZG/2019/GSTIC), Research Committee of University of Macau (MYRG2017-00212-FST, MYRG2018-00129-FST), and China Postdoctoral Science Foundation (2019M651115).
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