Discovering periodic frequent travel patterns of individual metro passengers considering different time granularities and station attributes

Cao, 2007, Discovery of periodic patterns in spatiotemporal sequences, IEEE Trans. Knowl. Data Eng., 19, 453, 10.1109/TKDE.2007.1002 Ebadi, 2017, Constructing activity–mobility trajectories of college students based on smart card transaction data, Int. J. Transp. Sci. Technol., 6, 316, 10.1016/j.ijtst.2017.08.003 Fournier-Viger, P., Lin, C.W., Duong, Q.H., et al., 2017. PFPM: discovering periodic frequent patterns with novel periodicity measures. In: Proceedings of the 2nd Czech-China Scientific Conference 2016. IntechOpen. Fournier-Viger, P., Chi, T.T., Wu, Y., et al., 2021. Finding Periodic Patterns in Multiple Sequences. Periodic Pattern Mining. Springer, Singapore, pp. 81-103. Fournier-Viger, P., Wu, Y., Dinh, D.T., et al., 2021. Discovering periodic high utility itemsets in a discrete sequence. Periodic Pattern Mining. Springer, Singapore, pp. 133-151. Fournier-Viger, 2020, Discovering rare correlated periodic patterns in multiple sequences, Data Knowl. Eng., 126, 10.1016/j.datak.2019.101733 Fournier-Viger, 2021, Mining local periodic patterns in a discrete sequence, Inf. Sci., 544, 519, 10.1016/j.ins.2020.09.044 Fournier-Viger, 2022, Tspin: Mining top-k stable periodic patterns, Appl. Intell., 52, 6917, 10.1007/s10489-020-02181-6 Gonzalez, 2008, Understanding individual human mobility patterns, Nature, 453, 779, 10.1038/nature06958 Gu, 2022, Short-term trajectory prediction for individual metro passengers integrating diverse mobility patterns with adaptive location-awareness, Inf. Sci., 599, 25, 10.1016/j.ins.2022.03.074 Han, 2000, Mining frequent patterns without candidate generation, ACM SIGMOD Rec., 29, 1, 10.1145/335191.335372 Huynh, U., Le, B., Dinh, D.T., et al., 2021. Mining periodic high-utility sequential patterns with negative unit profits. Periodic Pattern Mining. Springer, Singapore, pp. 153-170. Huynh, U., Le, B., Dinh, D.T., et al., 2021. Hiding periodic high-utility sequential patterns. Periodic Pattern Mining. Springer, Singapore, pp. 171-189. Ismail, 2018, Mining productive-periodic frequent patterns in tele-health systems, J. Netw. Comput. Appl., 115, 33, 10.1016/j.jnca.2018.04.014 Ismail, 2018, Mining of productive periodic-frequent patterns for IoT data analytics, Futur. Gener. Comput. Syst., 88, 512, 10.1016/j.future.2018.05.085 Kiran, R.U., Kitsuregawa, M., 2013. Discovering quasi-periodic-frequent patterns in transactional databases. In: International Conference on Big Data Analytics. Springer, Cham, pp. 97-115. Kiran, R.U., Venkatesh, J.N., Fournier-Viger, P., et al., 2017. Discovering periodic patterns in non-uniform temporal databases. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, pp. 604-617. Kiran, 2016, Efficient discovery of periodic-frequent patterns in very large databases, J. Syst. Softw., 112, 110, 10.1016/j.jss.2015.10.035 Kiran, R.U., Watanobe, Y., Chaudhury, B., et al., 2020. Discovering maximal periodic-frequent patterns in very large temporal databases. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, pp. 11-20. Kiran, R.U., Saideep, C., Ravikumar, P., et al., 2020. Discovering fuzzy periodic-frequent patterns in quantitative temporal databases. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, pp. 1-8. Li, 2012, Mining periodic behaviors of object movements for animal and biological sustainability studies, Data Min. Knowl. Disc., 24, 355, 10.1007/s10618-011-0227-9 Li, Z., Ding, B., Han, J., et al., 2010. Mining periodic behaviors for moving objects. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1099-1108. Lomb, 1976, Least-squares frequency analysis of unequally spaced data, Astrophys. Space Sci., 39, 447, 10.1007/BF00648343 Nofong, V.M., Abdel-Fatao, H., Afriyie, M.K., et al., 2021. Discovering Self-reliant Periodic Frequent Patterns. Periodic Pattern Mining. Springer, Singapore, 2021, pp. 105-131. Rashid, M., Karim, M., Jeong, B.S., et al., 2012. Efficient mining regularly frequent patterns in transactional databases. In: International Conference on Database Systems for Advanced Applications. Springer, Berlin, Heidelberg, pp. 258-271. Scargle, 1982, Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data, Astrophys. J., 263, 835, 10.1086/160554 Shou, 2018, Similarity analysis of frequent sequential activity pattern mining, Transp. Res. Part C: Emerging Technologies, 96, 122, 10.1016/j.trc.2018.09.018 Song, 2010, Limits of predictability in human mobility, Science, 327, 1018, 10.1126/science.1177170 Surana, A., Kiran, R.U., Reddy, P.K., 2011. An efficient approach to mine periodic-frequent patterns in transactional databases. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, pp. 254-266. Tanbeer, S.K., Ahmed, C.F., Jeong, B.S., et al., 2009. Discovering periodic-frequent patterns in transactional databases. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, pp. 242-253. Tanbeer, 2017, Scalable regular pattern mining in evolving body sensor data, Futur. Gener. Comput. Syst., 75, 172, 10.1016/j.future.2016.04.008 Venkatesh, J.N., Uday Kiran, R., Krishna Reddy, P., et al., 2018. Discovering periodic-correlated patterns in temporal databases. In: Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXVIII. Springer, Berlin, Heidelberg, pp. 146-172. Yuan, 2017, Multi-granularity periodic activity discovery for moving objects, Int. J. Geogr. Inf. Sci., 31, 435, 10.1080/13658816.2016.1205194 Zhang, 2019, Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories, Expert Syst. Appl., 122, 85, 10.1016/j.eswa.2018.12.047 Zhang, 2019, Semantic periodic pattern mining from spatio-temporal trajectories, Inf. Sci., 502, 164, 10.1016/j.ins.2019.06.035 Zhang, 2018, Predicting citywide crowd flows using deep spatio-temporal residual networks, Artif. Intell., 259, 147, 10.1016/j.artint.2018.03.002