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Springer Nature Switzerland
Reference10 articles.
1. Chen, G., Viana, A.C., Fiore, M., Sarraute, C.: Complete trajectory reconstruction from sparse mobile phone data. EPJ Data Sci. 8(1), 1–24 (2019). https://doi.org/10.1140/epjds/s13688-019-0206-8
2. Chen, W., Ji, M., Wang, J.: T-dbscan: a spatiotemporal density clustering for GPS trajectory segmentation. Int. J. Online Eng. (iJOE) 10, 19 (2014). https://doi.org/10.3991/ijoe.v10i6.3881
3. Choi, S., Yeo, H., Kim, J.: Network-wide vehicle trajectory prediction in urban traffic networks using deep learning. Transport. Res. Rec. J. Transport. Res. Board 2672, 036119811879473 (2018). https://doi.org/10.1177/0361198118794735
4. De Groeve, J., et al.: Extracting spatio-temporal patterns in animal trajectories: an ecological application of sequence analysis methods. Methods Ecol. Evol. 7(3), 369–379 (2016). https://doi.org/10.1111/2041-210X.12453, https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12453
5. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. KDD’96, pp. 226–231. AAAI Press (1996)
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