STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together

Alessandretti, 2018, Evidence for a conserved quantity in human mobility, Nature Human Behaviour, 2 Barbosa, 2015, The effect of recency to human mobility, EPJ Data Science, 4, 21, 10.1140/epjds/s13688-015-0059-8 Barbosa-Filho, 2018, Human mobility: Models and applications, Physics Reports, 734, 1, 10.1016/j.physrep.2018.01.001 Brockmann, 2006, The scaling laws of human travel, Nature, 439, 462, 10.1038/nature04292 Cho, E., Myers, S., Leskovec, J., 2011. Friendship and mobility: User movement in location-based social networks, pp. 1082–1090. Galland, S., Balbo, F., Gaud, N., Rodriguez, S., Picard, G., 2015. Contextualize agent interactions by combining social and physical dimensions in the environment. Galland, 2014, Multi-agent simulation of individual mobility behavior in carpooling, Transportation Research Part C: Emerging Technologies, 45 Gonzalez, 2008, Understanding individual human mobility patterns, Nature, 453, 779, 10.1038/nature06958 Jiang, S., Yang, Y., Gupta, S., Veneziano, D., Athavale, S., Gonzalez, M.C., 2016. The timegeo modeling framework for urban mobility without travel surveys. Proceedings of the National Academy of Sciences 113, 201524261. doi:10.1073/pnas.1524261113. Luca, M., Barlacchi, G., Lepri, B., Pappalardo, L., 2020. Deep learning for human mobility: a survey on data and models. arXiv:2012.02825. Montjoye, 2018, On the privacy-conscientious use of mobile phone data, Scientific Data, 5, 180286, 10.1038/sdata.2018.286 Montjoye, 2013, Unique in the crowd: The privacy bounds of human mobility, Scientific reports, 3, 1376, 10.1038/srep01376 Pappalardo, 2016, Human mobility modelling: Exploration and preferential return meet the gravity model, Procedia Computer Science, 83 Pappalardo, 2017, Data-driven generation of spatio-temporal routines in human mobility, Data Mining and Knowledge Discovery, 32 Pappalardo, L., Simini, F., Barlacchi, G., Pellungrini, R., 2020. scikit-mobility: a python library for the analysis, generation and risk assessment of mobility data. arXiv:1907.07062. Pappalardo, 2015, Returners and explorers dichotomy in human mobility, Nature Communications, 6 Pellungrini, 2017, A data mining approach to assess privacy risk in human mobility data, ACM Trans. Intell. Syst. Technol., 9 Pellungrini, 2020, Modeling adversarial behavior against mobility data privacy, IEEE Transactions on Intelligent Transportation Systems, 1, 10.1109/TITS.2020.3021911 Song, C., Koren, T., Wang, P., Barabasi, A.L., 2010a. Modelling the scaling properties of human mobility. Nature Physics 6. Song, C., Qu, Z., Blumm, N., Barabasi, A.L., 2010b. Limits of predictability in human mobility. Science (New York, N.Y.) 327, 1018–21. Tomasini, 2017, On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors, Pervasive and Mobile Computing, 38, 215, 10.1016/j.pmcj.2016.12.007 Toole, 2015, Coupling human mobility and social ties, Journal of the Royal Society, Interface / the Royal Society, 12 Yang, D., Qu, B., Yang, J., Cudre-Mauroux, P., 2019. Revisiting user mobility and social relationships in lbsns: A hypergraph embedding approach, pp. 2147–2157. doi:10.1145/3308558.3313635.