Vehicles Trajectories Analysis Using Piecewise-Segment Dynamic Time Warping (PSDTW)
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-70917-4_21
Reference17 articles.
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2. Arfa, R., Yusof, R., Shabanzadeh, P.: Novel trajectory clustering method based on distance dependent Chinese restaurant process. PeerJ Comput. Sci. 2019(8), 1–5 (2019). https://doi.org/10.7717/peerj-cs.206
3. Atev, S., Miller, G., Papanikolopoulos, N.P.: Clustering of vehicle trajectories. IEEE Trans. Intell. Transp. Syst. 11(3), 647–657 (2010). https://doi.org/10.1109/TITS.2010.2048101
4. Feng, L., Zhao, X., Liu, Y., Yao, Y., Jin, B.: A similarity measure of jumping dynamic time warping. In: Proceedings of 2010 7th International Conference Fuzzy System Knowledge Discovery, FSKD 2010, vol. 4, no. Fskd, pp. 1677–1681 (2010). https://doi.org/10.1109/FSKD.2010.5569383
5. Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285–289 (2000). https://doi.org/10.1145/347090.347153
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