1. Y. Zheng, "Trajectory data mining: an overview," ACM Trans. Intell. Syst. Technol., vol. 6, no. 3, pp. 1-41, 2015. [DOI:10.1145/2743025]

2. Z. Fan, A. Arai, X. Song, A. Witayangkurn, H. Kanasugi, and R. Shibasaki, "A collaborative filtering approach to citywide human mobility completion from sparse call records," IJCAI Int. Jt. Conf. Artif. Intell., vol. 2016-Janua, pp. 2500-2506, 2016.

3. D. Hecker, H. Stange, C. Körner, and M. May, "Sample Bias due to Missing Data in Mobility Surveys," in 2010 IEEE International Conference on Data Mining Workshops, 2010, pp. 241-248. doi: 10.1109/ICDMW.2010.162. [DOI:10.1109/ICDMW.2010.162]

4. J. A. Long, "Kinematic interpolation of movement data," Int. J. Geogr. Inf. Sci., vol. 30, no. 5, pp. 854-868, 2016, doi: 10.1080/13658816.2015.1081909. [DOI:10.1080/13658816.2015.1081909]

5. M. Li, S. Gao, F. Lu, and H. Zhang, "Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data," Comput. Environ. Urban Syst., vol. 77, p. 101346, 2019, doi: https://doi.org/10.1016/j.compenvurbsys.2019.101346 [DOI:10.1016/j.compenvurbsys.2019.101346.]

6. M. Liang, R. W. Liu, Q. Zhong, J. Liu, and J. Zhang, "Neural Network-Based Automatic Reconstruction of Missing Vessel Trajectory Data," in 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), 2019, pp. 426-430. doi: 10.1109/ICBDA.2019.8713215. [DOI:10.1109/ICBDA.2019.8713215]

7. S. Pathak, M. He, S. Malinchik, and S. Sobolevsky, "Pattern Ensembling for Spatial Trajectory Reconstruction," Jan. 2021, [Online]. Available: http://arxiv.org/abs/2101.09844

8. Y. Tao et al., "A comparative analysis of trajectory similarity measures," GIScience Remote Sens., vol. 58, no. 5, pp. 643-669, 2021, doi: 10.1080/15481603.2021.1908927. [DOI:10.1080/15481603.2021.1908927]

9. M. Sharif and A. A. Alesheikh, "A Review on the Process of Point Objects' Movement and Their Trajectory Similarity Measurement Approacheا," issge-gej, vol. 7, no. 1, pp. 41-54, Mar. 2016.

10. M. Sharif and A. A. Alesheikh, "Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method," GIScience Remote Sens., vol. 54, no. 3, pp. 426-452, May 2017, doi: 10.1080/15481603.2017.1278644. [DOI:10.1080/15481603.2017.1278644]

11. D. Bucher et al., "Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App," no. Ict4s, pp. 89-98, 2016, doi: 10.2991/ict4s-16.2016.11. [DOI:10.2991/ict4s-16.2016.11]

12. K. Corder, U. Ekelund, R. M. Steele, N. J. Wareham, and S. Brage, "Assessment of physical activity in youth," J. Appl. Physiol., vol. 105, no. 3, pp. 977-987, 2008, doi: 10.1152/japplphysiol.00094.2008. [DOI:10.1152/japplphysiol.00094.2008]

13. B. W. Wheeler, A. R. Cooper, A. S. Page, and R. Jago, "Greenspace and children's physical activity: A GPS/GIS analysis of the PEACH project," Prev. Med. (Baltim)., vol. 51, no. 2, pp. 148-152, Aug. 2010, doi: 10.1016/J.YPMED.2010.06.001. [DOI:10.1016/j.ypmed.2010.06.001]

14. D. Seng, Q. Zhang, X. Zhang, G. Chen, and X. Chen, "Spatiotemporal prediction of air quality based on LSTM neural network," Alexandria Eng. J., vol. 60, no. 2, pp. 2021-2032, 2021, doi: https://doi.org/10.1016/j.aej.2020.12.009 [DOI:10.1016/j.aej.2020.12.009.]

15. D. Alizadeh, A. A. Alesheikh, and M. Sharif, "Vessel Trajectory Prediction Using Historical Automatic Identification System Data," J. Navig., vol. 74, no. 1, pp. 156-174, 2021, doi: 10.1017/S0373463320000442. [DOI:10.1017/S0373463320000442]