Location Prediction in Mobile Applications

  • A. Cipcigan-Rațiu Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania


The vast developments of mobile technologies and applications in recent years produced a lot of issues to address, as mobile devices have surpassed the usage of classical computers. Predicting the future location of a user who utilizes a mobile application caught the eye of both academia and the industry. Most existing results either use excessive computing, most often relying on a server, or neglect battery usage. We propose a new method that takes these major points into consideration, which gives good results by only relying on the end user’s mobile device, not draining the battery, respecting the privacy of the users and that achieves an accuracy of 80%.



[1] Ashbrook, D., and Starner, T. Using gps to learn significant locations and predict movement across multiple users. Personal and Ubiquitous computing 7, 5 (2003), 275–286.
[2] Breiman, L. Classification and regression trees. Routledge, 2017.
[3] Cover, T., and Hart, P. Nearest neighbor pattern classification. IEEE transactions on information theory 13, 1 (1967), 21–27.
[4] Du, Y., Wang, C., Qiao, Y., Zhao, D., and Guo, W. A geographical location prediction method based on continuous time series markov model. PloS one 13, 11 (2018).
[5] Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd (1996), vol. 96, pp. 226–231.
[6] Gambs, S., Killijian, M.-O., and del Prado Cortez, M. N. Next place prediction using mobility markov chains. In Proceedings of the First Workshop on Measurement, Privacy, and Mobility (2012), pp. 1–6.
[7] Laasonen, K. Clustering and prediction of mobile user routes from cellular data. In European Conference on Principles of Data Mining and Knowledge Discovery (2005), Springer, pp. 569–576.
[8] Lin, K., Kansal, A., Lymberopoulos, D., and Zhao, F. Energy-accuracy trade-off for continuous mobile device location. In Proceedings of the 8th international conference on Mobile systems, applications, and services (2010), pp. 285–298.
[9] Lloyd, S. Least squares quantization in pcm. IEEE transactions on information theory 28, 2 (1982), 129–137.
[10] Markov, A. A. The theory of algorithms. Trudy Matematicheskogo Instituta Imeni VA Steklova 42 (1954), 3–375.
[11] Moroney, L. The firebase realtime database. In The Definitive Guide to Firebase. Springer, 2017, pp. 51–71.
[12] Narzt, W. Context-based energy saving strategies for continuous determination of position on ios devices. In 2014 47th Hawaii International Conference on System Sciences (2014), IEEE, pp. 945–954.
[13] Smith, K. The influence of weather and climate on recreation and tourism. Weather 48, 12 (1993), 398–404.
[14] Song, C., Qu, Z., Blumm, N., and Barabasi, A.-L. ´ Limits of predictability in human mobility. Science 327, 5968 (2010), 1018–1021.
[15] Xia, L., Huang, Q., and Wu, D. Decision tree-based contextual location prediction from mobile device logs. Mobile Information Systems 2018 (2018).
[16] Yao, X., and Thill, J.-C. How far is too far?–a statistical approach to contextcontingent proximity modeling. Transactions in GIS 9, 2 (2005), 157–178.
[17] Ye, J., Zhu, Z., and Cheng, H. What’s your next move: User activity prediction in location-based social networks. In Proceedings of the 2013 SIAM International Conference on Data Mining (2013), SIAM, pp. 171–179.
[18] Zhou, C., Frankowski, D., Ludford, P., Shekhar, S., and Terveen, L. Discovering personal gazetteers: an interactive clustering approach. In Proceedings of the 12th annual ACM international workshop on Geographic information systems (2004), pp. 266–273.
How to Cite
CIPCIGAN-RAȚIU, A.. Location Prediction in Mobile Applications. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 65, n. 1, p. 46-58, may 2020. ISSN 2065-9601. Available at: <http://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/view/50>. Date accessed: 01 oct. 2020. doi: https://doi.org/10.24193/subbi.2020.1.04.