Abstract
We show that the Needleman-Wunsch algorithm for sequence alignment can be efficiently applied to comparing user trajectories, where user locations are provided by Global positioning system (GPS). We compare our approach based on this algorithm with other approaches such as the pairwise method and the proximity method. We describe all steps necessary to apply the Needleman-Wunsch algorithm when comparing user trajectories. In our experiments we use two different data sets: a data set that we collected with 455 mobile devices distributed among our students and the Geolife data set (Microsoft Research Asia). We conclude that our approach based on the Needleman-Wunsch algorithm performs better than other approaches, especially, in terms of true negatives, false positives and false negatives, while still offering improvement in terms of true positives.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Čavojský, M., Drozda, M.: Energy efficient trajectory recording of mobile devices using wifi scanning. In: Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), 2016 International IEEE Conferences, pp. 1079–1085 (2016)
Google: Location—Android Developers. https://developer.android.com/reference/android/location/package-summary.html
Henikoff, S., Henikoff, J.G.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. 89(22), 10915–10919 (1992)
Hung, C.C., Chang, C.W., Peng, W.C.: Mining trajectory profiles for discovering user communities. In: Proceedings of the 2009 International Workshop on Location Based Social Networks - LBSN 2009. pp. 1–8. ACM (2009). https://doi.org/10.1145/1629890.1629892
Karney, C., Deakin, R.E.: FW bessel (1825): The calculation of longitude and latitude from geodesic measurements. Astron. Nachr. 331(8), 852–861 (2010)
Čavojský, M., Uhlar, M., Ivanis, M., Molnar, M., Drozda, M.: User trajectory extraction based on wifi scanning. In: FiCloud 2018, The IEEE 6th International Conference on Future Internet of Things and Cloud, pp. 115–120 (2018)
Mavoa, S., Oliver, M., Witten, K., Badland, H.M.: Linking GPS and travel diary data using sequence alignment in a study of children’s independent mobility. Int. J. Health Geogr. 10(1), 64 (2011)
Michael, K., McNamee, A., Michael, M., Tootell, H.: Location-based intelligence – modeling behavior in humans using GPS location-based intelligence – modeling behavior in humans using GPS location-based intelligence – modeling behavior in humans using GPS. In: 2006 IEEE International Symposium on Technology and Society (ISTAS 2006), pp. 1–8 (2006)
Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 637–646. ACM (2009)
Montoliu, R., Blom, J., Gatica-Perez, D.: Discovering places of interest in everyday life from smartphone data. Multimed. Tools Appl. 62(1), 179–207 (2013)
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)
Thiagarajan, A., Ravindranath, L., Balakrishnan, H., Madden, S., Girod, L.: Accurate, low-energy trajectory mapping for mobile devices. In: Proceedings of USENIX Association (2011)
Van Brummelen, G.: Heavenly Mathematics: The Forgotten Art of Spherical Trigonometry. Princeton University Press, Princeton (2012)
Yang, D., Zhang, T., Li, J., Lian, X.: Synthetic fuzzy evaluation method of trajectory similarity in map-matching. J. Intell. Transp. Syst. 15(4), 193–204 (2011)
Ying, J.J.C., Lee, W.C., Weng, T.C., Tseng, V.S.: Semantic trajectory mining for location prediction. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 34–43. ACM (2011). https://doi.org/10.1145/2093973.2093980
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.Y.: Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 312–321. ACM (2008)
Zheng, Y., Xie, X., Ma, W.Y.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)
Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800. ACM (2009)
Zheng, Y., Zhou, X.: Computing with Spatial Trajectories. Springer, New York (2011). https://doi.org/10.1007/978-1-4614-1629-6
Acknowledgment
The authors were supported by the project “STU ako líder Digitálnej koalície”, project no. 002STU-2-1/2018, financed by Ministry of Education, Science, Research and Sport of the Slovak Republic. Maroš Čavojský also thankfully acknowledges a conference grant received from MAIND, s.r.o.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Čavojský, M., Drozda, M. (2019). Comparison of User Trajectories with the Needleman-Wunsch Algorithm. In: Yin, Y., Li, Y., Gao, H., Zhang, J. (eds) Mobile Computing, Applications, and Services. MobiCASE 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-030-28468-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-28468-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28467-1
Online ISBN: 978-3-030-28468-8
eBook Packages: Computer ScienceComputer Science (R0)