Abstract
Huge amount of location and tracking data is gathered by location and tracking technologies, such as global positioning system (GPS) and global system for mobile communication (GSM) devices; leading to the collection of large spatiotemporal datasets and to the opportunity of discovering usable knowledge about movement behavior. Movement behavior can be extremely useful in many ways when applied, for example, in the domain of traffic management, planning metropolitan areas, mobile marketing, tourism, etc. In this research, we move towards this direction and propose a framework for finding trajectory patterns of frequent behaviors using GSM data. The research question is "how to use trajectory data analysis in support of solving traffic management problems utilizing data mining techniques?" Our framework is illustrated to explain how GSM data can provide accurate information about population movement behavior, and hence support traffic decisions.
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References
Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S., Kyriakakos, M., Kalousis, A.: Predicting the Location of Mobile Users: A Machine Learning Approach. In: ICPS 2009, London (2009)
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Trajectory Pattern Mining. In: KDD 2007, California (2007)
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Trajectory Pattern Analysis for Urban Traffic. In: IWCTS 2009, Seattle (2009)
Yan, Z., Parent, C., Spaccapietra, S., Chakraborty, D.: A Hybrid Model and Computing Platform for Spatio-semantic Trajectories. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 60–75. Springer, Heidelberg (2010)
Yang, J., Hu, M.: TrajPattern: Mining Sequential Patterns from Imprecise Trajectories of Mobile Objects. In: Ioannidis, Y., et al. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 664–681. Springer, Heidelberg (2006)
Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y.: Mining Interesting Locations and Travel Sequences from GPS Trajectories. In: WWW 2009, Madrid (2009)
Alvares, L.O., Bogorny, V., de Macedo, J.A.F., Moelans, B., Spaccapietra, S.: Dynamic Modeling of Trajectory Patterns using Data Mining and Reverse Engineering. In: ER 2007, Aukland (2007)
Li, Z., Ji, M., Lee, J.-G., Tang, L.-A., Yu, Y., Han, J., Kays, R.: MoveMine: Mining Moving Object Databases. In: Proceedings of the ACM SIGMOD Conference, Indianapolis (2010)
Leonardi, L., Orlando, S., Raffaetà , A., Roncato, A., Silvestri, C.: Frequent Spatio-Temporal Patterns in Trajectory Data Warehouses. In: SAC 2009, Honolulu (2009)
Marketos, G.: Mobility Data Warehousing and Mining. In: VLDB 2009, Lyon (2009)
Oueslati, W., Akaichi, J.: Mobile Information Collectors Trajectory Data Warehouse Design. International Journal of Managing Information Technology (IJMIT)Â 2010 (2010)
Alvares, L.O., Bogorny, V., Kuijpers, B., Fernandes, J.A., Moelans, B., Vaisman, A.: A Model for Enriching Trajectories with Semantic Geographical Information. In: GIS 2007, Seattle (2007)
Brakatsoulas, S., Pfoser, D., Tryfona, N.: Modeling, Storing and Mining Moving Object Databases. In: International Database Engineering and Applications Symposium (IDEAS 2004), Coimbra (2004)
Gidófalvi, G., Huang, X., Pedersen, T.B.: Privacy–Preserving Trajectory Collection. In: ACM GIS 2008, Irvine (2008)
Mohammed, N., Fung, B.C.M., Debbabi, M.: Walking in the Crowd: Anonymizing Trajectory Data for Pattern Analysis. In: CIKM 2009, Hong Kong (2009)
Schreck, T., Bernard, J., Tekusova, T., Kohlhammer, J.: Visual Cluster Analysis of Trajectory Data With Interactive Kohonen Maps. In: VAST 2008, Columbus (2008)
Jin, X., Itmi, M., Abdulrab, H.: A Cooperative Multi-agent System Simulation Model for Urban Traffic Intelligent Control. In: SCSC 2007 (2007)
Tártaro, M.L., Wainer, G.: Defining Models of Urban Traffic Using the TSC Tool. In: Proceedings of the 2001 Winter Simulation Conference (2001)
Behnisch, M., Ultsch, A.: Urban data-mining: spatiotemporal exploration of multidimensional data. Building Research & Information, 520–532 (2009)
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Elragal, A., Raslan, H. (2014). Analysis of Trajectory Data in Support of Traffic Management: A Data Mining Approach. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Lecture Notes in Computer Science(), vol 8557. Springer, Cham. https://doi.org/10.1007/978-3-319-08976-8_13
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DOI: https://doi.org/10.1007/978-3-319-08976-8_13
Publisher Name: Springer, Cham
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