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User identification from mobility traces

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Abstract

Geolocation is a powerful source of information through which user patterns can be extracted. User regions-of-interest, along with these patterns, can be used to recognize and imitate user behavior. In this work we develop a methodology for preprocessing location data in order to discover the most relevant places the user visits, and we propose a Probabilistic Finite Automaton structure as mobility model. We analyse both location prediction and user identification tasks. Our model is assessed with two evaluation metrics regarding its predictive accuracy and user identification accuracy, and compared against other models.

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References

  • Amirat H, Benslimane A, Fournier-Viger P, Lagraa N (2018) Locrec: Rule-based successive location recommendation in lbsn. In: 2018 IEEE International Conference on Communications (ICC), pp 1–6

  • Argall BD, Chernova S, Veloso M, Browning B (2009) A survey of robot learning from demonstration. Robot Auton Syst 57(5):469–483

    Article  Google Scholar 

  • Batabyal S, Bhaumik P (2015) Mobility models, traces and impact of mobility on opportunistic routing algorithms: a survey. IEEE Commun Surv Tutor 17(3):1679–1707

    Article  Google Scholar 

  • Billard A, Calinon S, Dillmann R, Schaal S (2008) Robot programming by demonstration. Springer, Berlin, Heidelberg, pp 1371–1394

    Google Scholar 

  • Bishop CM, Lasserre J (2007) Generative or discrimative? Getting the best of both worlds. Oxford University Press, Oxford, pp 3–24

    MATH  Google Scholar 

  • Cook DJ, Augusto JC, Jakkula VR (2009) Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob Comput 5(4):277–298

    Article  Google Scholar 

  • Dereszynski E, Hostetler J, Fern A, Dietterich T, Hoang TT, Udarbe M (2011) Learning probabilistic behavior models in real-time strategy games. In: Proceedings of the seventh AAAI conference on artificial intelligence and interactive digital entertainment, AAAI Press, AIIDE’11, pp 20–25

  • Do TMT, Dousse O, Miettinen M, Gatica-Perez D (2015) A probabilistic kernel method for human mobility prediction with smartphones. Pervasive Mob Comput 20(C):13–28

    Article  Google Scholar 

  • Duan Y, Andrychowicz M, Stadie B, Jonathan Ho O, Schneider J, Sutskever I, Abbeel P, Zaremba W (2017) One-shot imitation learning. In: Advances in neural information processing systems 30, Curran Associates, Inc., pp 1087–1098

  • Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining, AAAI Press, KDD’96, pp 226–231

  • Feng Z, Zhu Y (2016) A survey on trajectory data mining: techniques and applications. IEEE Access 4:2056–2067

    Article  Google Scholar 

  • Gambs S, Killijian MO, del Prado Cortez MNN (2012) Next place prediction using mobility markov chains. In: Proceedings of the first workshop on measurement, privacy, and mobility, ACM, New York, NY, USA, MPM ’12, pp 3:1–3:6

  • Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, USA, KDD ’07, pp 330–339

  • González M, Hidalgo C, Barabási a L (2008) Understanding individual human mobility patterns. Nature 453(June):779–782

    Article  Google Scholar 

  • Jahromi KK, Zignani M, Gaito S, Rossi GP (2016) Simulating human mobility patterns in urban areas. Simul Model Pract Theory 62:137–156

    Article  Google Scholar 

  • Jiang S, Ferreira J, Gonzalez MC (2017) Activity-based human mobility patterns inferred from mobile phone data: a case study of singapore. IEEE Trans Big Data 3(2):208–219

    Article  Google Scholar 

  • Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma WY (2008) Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, ACM, New York, NY, USA, GIS ’08, pp 34:1–34:10

  • Lin M, Hsu WJ (2014) Mining gps data for mobility patterns: a survey. Pervasive Mob Comput 12:1–16

    Article  Google Scholar 

  • Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the Thirtieth AAAI conference on artificial intelligence, AAAI Press, AAAI’16, pp 194–200

  • Lu X, Wetter E, Bharti N, Tatem AJ, Bengtsson L (2013) Approaching the limit of predictability in human mobility. Sci Rep 3:2923

    Article  Google Scholar 

  • Lv Q, Qiao Y, Ansari N, Liu J, Yang J (2017) Big data driven hidden markov model based individual mobility prediction at points of interest. IEEE Trans Veh Technol 66(6):5204–5216

    Article  Google Scholar 

  • Munjal A, Camp T, Aschenbruck N (2012) Changing trends in modeling mobility. J Electric Comput Eng 2012:372572:1–372572:16

    Article  MathSciNet  Google Scholar 

  • Ontañón S, Mishra K, Sugandh N, Ram A (2008) Learning from demonstration and case-based planning for real-time strategy games. In: Prasad B (eds) Soft computing applications in industry. Studies in fuzziness and soft computing, vol 226. Springer, Berlin, Heidelberg

    Chapter  Google Scholar 

  • Ontañón S, Montaña JL, Gonzalez AJ (2014) A dynamic-bayesian network framework for modeling and evaluating learning from observation. Expert Syst Appl 41(11):5212–5226

    Article  Google Scholar 

  • Pomerleau DA (1989) Alvinn: an autonomous land vehicle in a neural network. Adv Neural Inf Process Syst 1:305–313

    Google Scholar 

  • Rios JDMY, Banks J, Cavendish H (1797) Recherches sur les principaux problemes de l’astronomie nautique. Par Don Josef de Mendoza y Rios, F. R. S. Communicated by Sir Joseph Banks, Bart. K. B. P. R. S. Philos Trans Royal Soc London Series I 87:43–122

    Google Scholar 

  • Roxin A, Gaber J, Wack M, Nait-Sidi-Moh A (2007) Survey of wireless geolocation techniques. In: 2007 IEEE Globecom Workshops, pp 1–9

  • Salomón S, Tîrnăucă C, Duque R, Montaña JL (2017) Daily routines inference based on location history. In: Ubiquitous computing and ambient intelligence. Springer International Publishing, Cham, pp 828–839

  • Tîrnăucă C, Montaña JL, Ontañón S, González AJ, Pardo LM (2016) Behavioral modeling based on probabilistic finite automata: an empirical study. Sensors 16(7):958

    Article  Google Scholar 

  • Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol 6(3):29:1–29:41

    Article  Google Scholar 

  • Zheng Y, Chen Y, Xie X, Ma WY (2009) Geolife2.0: A location-based social networking service. In: 2009 tenth international conference on mobile data management: systems, services and middleware, pp 357–358

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Acknowledgements

The authors gratefully acknowledge the financial support from FEDER (Fondo Europeo de Desarrollo Regional) and SODERCAN (Sociedad para el Desarrollo Regional de Cantabria) for the project TI16-IN-007 within the program “I+C=+C 2016—PROYECTOS DE I+D EN EL ÁMBITO DE LAS TIC, LÍNEA SMART”, and from Ministerio de Ciencia e Innovación (MICINN), Spain for the project PAC::LFO (MTM2014-55262-P).

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Authors and Affiliations

  1. AXPE Consulting Cantabria, Polígono de la Cros, Camargo, Spain

    Sergio Salomón

  2. Departamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, Santander, Spain

    Cristina Tîrnăucă, Rafael Duque & José Luis Montaña

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  1. Sergio Salomón
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  2. Cristina Tîrnăucă
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  3. Rafael Duque
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  4. José Luis Montaña
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Correspondence to Rafael Duque.

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Salomón, S., Tîrnăucă, C., Duque, R. et al. User identification from mobility traces. J Ambient Intell Human Comput 14, 31–40 (2023). https://doi.org/10.1007/s12652-018-1117-4

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