Abstract
The knowledge of the individual trajectories of citizens’ mobility in the urban space is critical for smart cities. The data concerning trajectories from the providers of mobile phone services are still difficult to be obtained in practice and one of the considerable obstacles here are legal aspects. We have designed and implemented the tourist trajectories generator for objects located in a selected but arbitrary urban area. A generation process is based on the random selection of the pre-defined profiles of tourist activeness, including mobility patterns. It is possible to generate a practically unlimited number of trajectories, if needed, and they may also be directed at the certain specific types of behaviours. Thus obtained large sets of data may be used for understanding urban behaviours, calibrating urban models, recommending systems under construction, as well as anticipating the smart city further software testing.
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References
Ahas, R., Aasa, A., Roose, A., Ülar Mark, Silm, S.: Evaluating passive mobile positioning data for tourism surveys: an Estonian case study. Tour. Manag. 29(3), 469–486 (2008). https://doi.org/10.1016/j.tourman.2007.05.014
Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., Ratti, C.: Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans. Intell. Transp. Syst. 12(1), 141–151 (2011). https://doi.org/10.1109/TITS.2010.2074196
Giurlanda, F., Perazzo, P., Dini, G.: HUMsim: a privacy-oriented human mobility simulator. In: Kanjo, E., Trossen, D. (eds.) S-CUBE 2014. LNICST, vol. 143, pp. 61–70. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17136-4_7
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008). https://doi.org/10.1038/nature06958
Klimek, R.: Exploration of human activities using message streaming brokers and automated logical reasoning for ambient-assisted services. IEEE Access 6, 27127–27155 (2018). https://doi.org/10.1109/ACCESS.2018.2834532
Klimek, R.: Towards recognising individual behaviours from pervasive mobile datasets in urban spaces. Sustainability 11(6), 1–25 (2019). https://doi.org/10.3390/su11061563
Kwan, M., Cartwright, W., Arrowsmith, C.: Tracking movements with mobile phone billing data: a case study with publicly-available data. In: Gartner, G., Ortag, F. (eds.) Advances in Location-Based Services, pp. 109–117. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-24198-7_7
Olesek, A.: Mobile phone trajectory generator using user profiles [in Polish]. Master thesis. AGH University of Science and Technology (2020). Supervisor: Radosław Klimek
Pappalardo, L., Simini, F.: Data-driven generation of spatio-temporal routines in human mobility. Data Min. Knowl. Disc. 32(3), 787–829 (2017). https://doi.org/10.1007/s10618-017-0548-4
Pelekis, N., Ntrigkogias, C., Tampakis, P., Sideridis, S., Theodoridis, Y.: Hermoupolis: a trajectory generator for simulating generalized mobility patterns. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8190, pp. 659–662. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40994-3_49
Renso, C., et al.: Wireless network data sources: tracking and synthesizing trajectories. In: Giannotti, F., Pedreschi, D. (eds.) Mobility, Data Mining and Privacy, pp. 73–100. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-75177-9_4
Uhlirz, M.: A market and user view on LBS. In: Gartner, G., Cartwright, W., Peterson, M.P. (eds.) Location Based Services and TeleCartography, pp. 47–58. Springer, Berlin (2007). https://doi.org/10.1007/978-3-540-36728-4_4
Zhou, F., Yin, R., Trajcevski, G., Zhang, K., Wu, J., Khokhar, A.: Improving human mobility identification with trajectory augmentation. GeoInformatica 1–31 (2019)
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Klimek, R., Olesek, A. (2021). Profile-Driven Synthetic Trajectories Generation to Enhance Smart System Solutions. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_47
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