Constructing activity–mobility trajectories of college students based on smart card transaction data

Negin Ebadi1, Jee Eun Kang2, Samiul Hasan3
1Transportation Analytics and Operations Research, UPS, United States
2Industrial and Systems Engineering and Institute for Sustainable Transportation and Logistics, University at Buffalo, United States
3Civil, Environmental, and Construction Engineering, University of Central Florida, United States

Tài liệu tham khảo

Allahviranloo, M., Regue, R., Recker, W., 2014. Pattern clustering and activity inference. In: Transportation Research Board 93rd Annual Meeting, Number 14-1274. Barry, 2002, Origin and destination estimation in new york city with automated fare system data, Transp. Res. Record: J. Transp. Res. Board, 1817, 183, 10.3141/1817-24 Blondel, V.D., Esch, M., Chan, C., Clérot, F., Deville, P., Huens, E., Morlot, F., Smoreda, Z., Ziemlicki, C., 2012. Data for development: the d4d challenge on mobile phone data. arXiv preprint arXiv: 1210.0137. Calabrese, 2011, Real-time urban monitoring using cell phones: a case study in rome, IEEE Trans. Intell. Transp. Syst., 12, 141, 10.1109/TITS.2010.2074196 de Montjoye, 2015, Unique in the shopping mall: on the reidentifiability of credit card metadata, Science, 347, 536, 10.1126/science.1256297 de Montjoye, Y.-A., Smoreda, Z., Trinquart, R., Ziemlicki, C., Blondel, V.D., 2014. D4d-senegal: the second mobile phone data for development challenge. arXiv preprint arXiv: 1407.4885. Do, 2014, The places of our lives: visiting patterns and automatic labeling from longitudinal smartphone data, IEEE Trans. Mob. Comput., 13, 638, 10.1109/TMC.2013.19 González, 2008, Understanding individual human mobility patterns, Nature, 453, 779, 10.1038/nature06958 Hasan, 2013 Hasan, 2013, Spatiotemporal patterns of urban human mobility, J. Stat. Phys., 151, 304, 10.1007/s10955-012-0645-0 Hasan, 2014, Urban activity pattern classification using topic models from online geo-location data, Transp. Res. Part C: Emerg. Technol., 44, 363, 10.1016/j.trc.2014.04.003 Hasan, 2015, Location contexts of user check-ins to model urban geo life-style patterns, PLoS One, 10, e0124819, 10.1371/journal.pone.0124819 Hasan, 2017, Reconstructing activity location sequences from incomplete check-in data: a semi-markov continuous-time bayesian network model, IEEE Trans. Intell. Transp. Syst., PP, 1, 10.1109/TITS.2017.2700481 Hasan, S., Zhan, X., Ukkusuri, S.V., 2013Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, UrbComp ’13. ACM, New York, NY, USA, pp. 1–8. Herrera, 2010, Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment, Transp. Res. Part C: Emerg. Technol., 18, 568, 10.1016/j.trc.2009.10.006 Houston, 2014, Tracking daily travel; assessing discrepancies between gps-derived and self-reported travel patterns, Transp. Res. Part C: Emerg. Technol., 48, 97, 10.1016/j.trc.2014.08.013 Jaro, 1989, Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida, J. Am. Statist. Assoc., 84, 414, 10.1080/01621459.1989.10478785 Jiang, 2009, Characterizing the human mobility pattern in a large street network, Phys. Rev. E, 80, 021136, 10.1103/PhysRevE.80.021136 Joh, 2002, Activity pattern similarity: a multidimensional sequence alignment method, Transp. Res. Part B: Methodological, 36, 385, 10.1016/S0191-2615(01)00009-1 Kang, C., Gao, S., Lin, X., Xiao, Y., Yuan, Y., Liu, Y., Ma, X., 2010. Analyzing and geo-visualizing individual human mobility patterns using mobile call records. In: 2010 18th International Conference on Geoinformatics. IEEE, pp. 1–7. Kruskal, 1983, An overview of sequence comparison: time warps, string edits, and macromolecules, SIAM Rev., 25, 201, 10.1137/1025045 Kusakabe, 2014, Behavioural data mining of transit smart card data: a data fusion approach, Transp. Res. Part C: Emerg. Technol., 46, 179, 10.1016/j.trc.2014.05.012 Lenormand, M., Louail, T., Cantú-Ros, O.G., Picornell, M., Herranz, R., Arias, J.M., Barthelemy, M., Miguel, M.S., Ramasco, J.J., 2014. Influence of sociodemographic characteristics on human mobility. arXiv preprint arXiv: 1411.7895. Liao, 2007, Learning and inferring transportation routines, Artif. Intell., 171, 311, 10.1016/j.artint.2007.01.006 Noulas, A., Scellato, S., Mascolo, C., Pontil, M., 2011. An empirical study of geographic user activity patterns in foursquare. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM). Pan, J., Rao, C.V., Agarwal, E.P.K., Gelfand, A.E., 2016. Markov-modulated marked poisson processes for check-in data. In: Proceedings of The 33rd International Conference on Machine Learning, pp. 2244–2253. Pelletier, 2011, Smart card data use in public transit: a literature review, Transp. Res. Part C: Emerg. Technol., 19, 557, 10.1016/j.trc.2010.12.003 Rashidi, 2017, Exploring the capacity of social media data for modelling travel behaviour: opportunities and challenges, Transp. Res. Part C: Emerg. Technol., 75, 197, 10.1016/j.trc.2016.12.008 Rieser-Schüssler, 2012, Capitalising modern data sources for observing and modelling transport behaviour, Transp. Lett., 4, 115, 10.3328/TL.2012.04.02.115-128 Shang, F., Liu, Y., Cheng, J., Cheng, H., 2014. Robust principal component analysis with missing data. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. ACM, pp. 1149–1158. Shi, 2015, Human mobility patterns in different communities: a mobile phone data-based social network approach, Ann. GIS, 21, 15, 10.1080/19475683.2014.992372 Song, 2010, Limits of predictability in human mobility, Science, 327, 1018, 10.1126/science.1177170 Sun, L., Lee, D.-H., Erath, A., Huang, X., 2012. Using smart card data to extract passenger’s spatio-temporal density and train’s trajectory of mrt system. In: Proceedings of the ACM SIGKDD International Workshop on Urban Computing. ACM, pp. 142–148. Toole, 2015, Coupling human mobility and social ties, J. R. Soc. Interface, 12, 10.1098/rsif.2014.1128 Trépanier, 2007, Individual trip destination estimation in a transit smart card automated fare collection system, J. Intell. Transp. Syst., 11, 1, 10.1080/15472450601122256 Wilson, 1998, Activity pattern analysis by means of sequence-alignment methods, Environ. Planning A, 30, 1017, 10.1068/a301017 Winkler, W.E., 1999. The state of record linkage and current research problems. In: Statistical Research Division. US Census Bureau, Citeseer. Zhang, L., Zhao, S., Zhu, Y., Zhu, Z., 2007. Study on the method of constructing bus stops od matrix based on ic card data. In: 2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, pp. 3147–3150. Zhao, 2007, Estimating a rail passenger trip origin-destination matrix using automatic data collection systems, Computer-Aided Civil Infrastruct. Eng., 22, 376, 10.1111/j.1467-8667.2007.00494.x