[19] F. Liu, D. Wang, and Z.-Q. Xu. Privacy-preserving travel time prediction with uncertainty usinggps trace data.IEEE Transactions on Mobile Computing, pages 1-1,2021.
[20] Q. Liu, S. Wu, L. Wang, and T. Tan. Predicting the next location: A recurrent model with spatial and temporal contexts. In Thirtieth AAAI conference on artificial intelligence, 2016.
[21] F. J. Massey. The kolmogorov-smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253):68-78, 1951.
[22] L. Moreira-Matias, J. Gama, M. Ferreira, J. Mendes-Moreira, and L. Damas. Predicting taxi-passenger demand using streaming data. IEEE Transactions on Intelligent Transportation Systems, 14(3):1393-1402, 2013.
[23] H. Ren, S. Ruan, Y. Li, J. Bao, C. Meng, R. Li, and Y. Zheng. Mtrajrec: Map-constrained trajectory recovery via seq2seq multi-task learning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 1410-1419, 2021.
[24] K. Richly. A survey on trajectory data management for hybrid transactional and analytical workloads. In 2018 IEEE International Conference on Big Data (Big Data), pages 562-569, 2018.
[25] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241. Springer, 2015.
[26] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015.
[27] J. Song, C. Meng, and S. Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.
[28] X. Song, H. Kanasugi, and R. Shibasaki. Deepransport: Prediction and simulation of human mobility and transportation mode at a citywide level. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pages 2618-2624, 2016.
[29] T. Vincenty. Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Survey Review, 23(176):88-93, 1975.
[30] H. Wang, X. Tang, Y.-H. Kuo, D. Kifer, and Z. Li. A simple baseline for travel time estimation using large-scale trip data. ACM Transactions on Intelligent Systems and Technology, 10(2):1-22, 2019.
[31] J. Wang, N. Wu, X. Lu, W. X. Zhao, and K. Feng. Deep trajectory recovery with fine-grained calibration using kalman filter. IEEE Transactions on Knowledge and Data Engineering, 33(3):921-934, 2019.
[32] S. Wang, Z. Bao, J. S. Culpepper, and G. Cong. A survey on trajectory data management, analytics, and learning. ACM Computing Surveys (CSUR), 54(2):1-36, 2021.
[33] Z. Wang, K. Fu, and J. Ye. Learning to estimate the travel time. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, page 858-866, London, United Kingdom, 2018. Association for Computing Machinery.
[34] Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, and C. Zhang. Connecting the dots: Multivariate time series forecasting with graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 753-763, 2020.
[35] Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang. Graph wavenet for deep spatial-temporal graph modeling. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI'19, page 1907-1913, 2019.
[36] C. Yang and G. Gidófalvi. Fast map matching, an algorithm integrating hidden markov model with precomputation. International Journal of Geographical Information Science, 32(3):547-570, 2018.