Abstract
Predicting the future location of mobile objects has become an important and challenging problem. With the widespread use of mobile devices, applications of location prediction include location-based services, resource allocation, handoff management in cellular networks, animal migration research, and weather forecasting. Most current techniques try to predict the next location of moving objects such as vehicles, people or animals, based on their movement history alone. However, ignoring the dynamic nature of mobile behavior may yield inaccurate predictions, at least part of the time. Analyzing movement in its context and choosing the best movement pattern by the current situation, can reduce some of the errors and improve prediction accuracy. In this chapter, we present a context-aware location prediction algorithm that utilizes various types of context information to predict future location of vehicles. We use five contextual features related to either the object environment or its current movement data: current location; object velocity; day of the week; weather conditions; and traffic congestion in the area. Our algorithm incorporates these context features into its trajectory-clustering phase as well as in its location prediction phase. We evaluate the proposed algorithm using two real-world GPS trajectory datasets. The experimental results demonstrate that the context-aware approach can significantly improve the accuracy of location predictions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amini, S., Brush, A.J., Krumm, J., Teevan, J.: Trajectory-aware mobile search. In: Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, pp. 2561–2564 (2012)
Kuang, L., Xia, Y., Mao, Y.: Personalized services recommendation based on context-aware QoS prediction. In: IEEE 19th International Conference in Web Services, pp. 400–406 (2012)
Lee, J., Han, J., Whang, K.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data (2007)
Zhang, L., Seta, N., Miyajima, H., Hayashi, H.: Fast authentication based on heuristic movement prediction for seamless handover in wireless access environment. In: IEEE WCNC, Wireless Communications and Networking Conference, pp. 2889–2893 (2007)
Akoush, S., Sameh, A.: Mobile user movement prediction using bayesian learning for neural networks. In: ACM IWCMC, pp. 191–196 (2007)
Elnekave, S., Last, M., Maimon, O.: Predicting future locations using clusters centroids. In: The Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems (2008)
Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: a location predictor on trajectory pattern mining. In: 15th ACM SIGKDD, pp. 637–646 (2009)
Patel, J., Chen, Y., Chakka, V.: Stripes: an efficient index for predicted trajectories. In: SIGMOD, pp. 635–646 (2004)
Tao, Y., Faloutsos, C., Papadias, D., Liu, B.: Prediction and indexing of moving objects with unknown motion patterns. In: SIGMOD Conference, pp. 611–622. ACM Press (2004)
Jeung, H., Liu, Q., Shen, H., Zhou, X.: A hybrid prediction model for moving objects. In: IEEE 24th International Conference on Data Engineering, pp. 70–79 (2009)
Nizetic, I., Fertalj, K., Kalpic, D.: A Prototype for the short-term prediction of moving object’s movement using Markov chains. In: Proceedings of the ITI, pp. 559–564 (2009)
Han, Y., Yang, J.: Clustering moving objects. In: KDD, pp. 617–622 (2004)
Elnekave, S., Last, M., Maimon, O.: Incremental Clustering of mobile objects. In: STDM07. IEEE (2007)
Schilit, B., Adams, N., Want, R.: Context-aware computing applications. In: Proceedings of the Workshop on Mobile Computing Systems and Applications. IEEE Computer Society, MD (1994)
Schmidt, A., Beigl, M., Gellersen, H.: There is more to context than location. In: Proceedings of the International Workshop on Interactive Applications of Mobile Computing (1998)
Chen, G., Kotz, D.: A survey of context-aware mobile computing research, Technical report TR2000-381, DartmouthCollege, ComputerScience (2000)
Sanchez, L., Lanza, J., Olsen, R., Bauer, M.: A generic context management framework for personal networking environments. In: Mobile and Ubiquitous Systems-Workshops, pp. 1–8 (2006)
Zhang, D., Huang, H., Lai, C.F., Liang, X.: Survey on context-awareness in ubiquitous media. Multimedia Tools Appl. 67(1), 179–211 (2013)
Lopes, J., Gusmão, M., Duarte, C., Davet, P.: Toward a distributed architecture for context awareness in ubiquitous computing. J. App. Comput. Res. 3, 19–33 (2014)
Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web. ACM (2009)
Gao, H., Tang, J., Liu, H.: Mobile location prediction in spatio-temporal context. In: Nokia Mobile Data Challenge Workshop (2012)
Žliobaitė, I., Mazhelis, O., Pechenizkiy, M.: Context-aware personal route recognition. In: Elomaa, T., Hollmén, J., Mannila, H. (eds.) DS 2011. LNCS, vol. 6926, pp. 221–235. Springer, Heidelberg (2011)
Veness, C.: Calculate distance, bearing and more between points (2002). http://www.movable-type.co.uk/scripts/latlong.html
Jensen, C., Lahrmann, H., Pakalnis, S., Runge, J.: The INFATI Data, TimeCenter Technical report, pp. 1–10 (2004)
Zheng, Y., Xing, X., Ma, W.: GeoLife: A Collaborative Social Networking Service among User, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–40 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Bar-David, R., Last, M. (2016). Context-Aware Location Prediction. In: Atzmueller, M., Chin, A., Janssen, F., Schweizer, I., Trattner, C. (eds) Big Data Analytics in the Social and Ubiquitous Context. SENSEML MUSE MSM 2015 2014 2014. Lecture Notes in Computer Science(), vol 9546. Springer, Cham. https://doi.org/10.1007/978-3-319-29009-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-29009-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29008-9
Online ISBN: 978-3-319-29009-6
eBook Packages: Computer ScienceComputer Science (R0)