Abstract
Pedestrian quantity estimation receives increasing attention and has important applications, e.g. in location evaluation and risk analysis. In this work, we focus on pedestrian quantity estimation for event monitoring. We address the problem (1) how to estimate quantities for unmeasured locations, and (2) where to place a bounded number of sensors during different phases of a soccer match. Pedestrian movement is no random walk and therefore characteristic traffic patterns occur in the data. This work utilizes traffic pattern information and incorporates it in a Gaussian process regression based approach. The empirical analysis on real world data collected with Bluetooth tracking technology during a soccer event at Stade des Costières in Nîmes (France) demonstrates the benefits of our approach.
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References
Liebig, T., Kemloh Wagoum, A.U.: Modelling microscopic pedestrian mobility using bluetooth. In: Proc. of the Fourth International Conference on Agents and Artificial Intelligience - ICAART 2012, pp. 270–275. SciTePress (2012)
Bruno, R., Delmastro, F.: Design and Analysis of a Bluetooth-Based Indoor Localization System. In: Conti, M., Giordano, S., Gregori, E., Olariu, S. (eds.) PWC 2003. LNCS, vol. 2775, pp. 711–725. Springer, Heidelberg (2003)
Liebig, T., Xu, Z.: Pedestrian monitoring system for indoor billboard evaluation. Journal of Applied Operational Research 4(1), 28–36 (2012)
Liebig, T.: A general pedestrian movement model for the evaluation of mixed indoor-outdoor poster campaigns. In: Proc. of the Third International Conference on Applied Operation Research, ICAOR 2011, pp. 289–300. Tadbir Operational Research Group Ltd. (2011)
Liebig, T., Stange, H., Hecker, D., May, M., Körner, C., Hofmann, U.: A general pedestrian movement model for the evaluation of mixed indoor-outdoor poster campaigns. In: Proc. of the Third International Workshop on Pervasive Advertising and Shopping (2010)
Li, M., Konomi, S., Sezaki, K.: Understanding and modeling pedestrian mobility of train-station scenarios. In: Sabharwal, A., Karrer, R., Zhong, L. (eds.) WINTECH, pp. 95–96. ACM (2008)
Pels, M., Barhorst, J., Michels, M., Hobo, R., Barendse, J.: Tracking people using Bluetooth. Implications of enabling Bluetooth discoverable mode. Technical report, University of Amsterdam (2005)
Hallberg, J., Nilsson, M., Synnes, K.: Positioning with Bluetooth. In: 10th International Conference on Telecommunications, vol. 2, pp. 954–958 (2003)
Andrienko, N., Andrienko, G., Stange, H., Liebig, T., Hecker, D.: Visual analytics for understanding spatial situations from episodic movement data. KI - Künstliche Intelligenz, 241–251 (2012)
Utsch, P., Liebig, T.: Monitoring Microscopic Pedestrian Mobility Using Bluetooth. In: Proceedings of the 8th International Conference on Intelligient Environments, pp. 173–177. IEEE Press (2012)
Hagemann, W., Weinzerl, J.: Automatische Erfassung von Umsteigern per Bluetooth-Technologie. In: Nahverkerspraxis. Springer, Heidelberg (2008)
Leitinger, S., Gröchenig, S., Pavelka, S., Wimmer, M.: Erfassung von Personenströmen mit der Bluetooth-Tracking-Technologie. In: Angewandte Geoinformatik 2010, 15th edn. Addison Wesley Longman Inc., New York (2010)
Stange, H., Liebig, T., Hecker, D., Andrienko, G., Andrienko, N.: Analytical Workflow of Monitoring Human Mobility in Big Event Settings using Bluetooth. In: Proceedings of the 3rd International Workshop on Indoor Spatial Awareness, pp. 51–58. ACM (2011)
Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Transaction on Computers 18(5), 401–409 (1969)
Gong, X., Wang, F.: Three improvements on knn-npr for traffic flow forecasting. In: Proceedings of the 5th International Conference on Intelligent Transportation Systems, pp. 736–740. IEEE Press (2002)
May, M., Hecker, D., Körner, C., Scheider, S., Schulz, D.: A vector-geometry based spatial knn-algorithm for traffic frequency predictions. In: Data Mining Workshops, International Conference on Data Mining, pp. 442–447. IEEE Computer Society, Los Alamitos (2008)
Neumann, M., Kersting, K., Xu, Z., Schulz, D.: Stacked gaussian process learning. In: Proceeding of the 9th IEEE International Conference on Data Mining, ICDM 2009, pp. 387–396. IEEE Computer Society (2009)
Liebig, T., Xu, Z., May, M., Wrobel, S.: Pedestrian Quantity Estimation with Trajectory Patterns. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012, Part II. LNCS, vol. 7524, pp. 629–643. Springer, Heidelberg (2012)
De Raedt, L.: Logical and Relational Learning. Springer (2008)
Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. The MIT Press (2007)
Yu, K., Chu, W., Yu, S., Tresp, V., Xu, Z.: Stochastic relational models for discriminative link prediction. In: Neural Information Processing Systems (2006)
Chu, W., Sindhwani, V., Ghahramani, Z., Keerthi, S.: Relational learning with gaussian processes. In: Neural Information Processing Systems (2006)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press (2006)
Kondor, R.I., Lafferty, J.D.: Diffusion kernels on graphs and other discrete input spaces. In: Proceeding of the International Conference on Machine Learning, pp. 315–322 (2002)
Krause, A., Guestrin, C., Gupta, A., Kleinberg, J.: Near-optimal sensor placements: maximizing information while minimizing communication cost. In: Proceedings of the 5th International Conference on Information Processing in Sensor Networks, IPSN 2006, pp. 2–10. ACM, New York (2006)
National Institute of Standards and Technology: Secure Hash Standard. National Institute of Standards and Technology, Washington, Federal Information Processing Standard 180-2 (2002)
Rigoutsos, I., Floratos, A.: Combinatorial pattern discovery in biological sequences: The teiresias algorithm. Bioinformatics 14(1), 55–67 (1998)
Kisilevich, S., Keim, D., Rokach, L.: A novel approach to mining travel sequences using collections of geotagged photos. In: Painho, M., Santos, M.Y., Pundt, H., Cartwright, W., Gartner, G., Meng, L., Peterson, M.P. (eds.) Geospatial Thinking. Lecture Notes in Geoinformation and Cartography, pp. 163–182. Springer, Heidelberg (2010)
Liebig, T., Körner, C., May, M.: Fast visual trajectory analysis using spatial bayesian networks. In: ICDM Workshops, pp. 668–673. IEEE Computer Society (2009)
Liebig, T., Körner, C., May, M.: Scalable sparse bayesian network learning for spatial applications. In: ICDM Workshops, pp. 420–425. IEEE Computer Society (2008)
Naini, F.M., Dousse, O., Thiran, P., Vetterli, M.: Population size estimation using a few individuals as agents. In: Proceedings of the International Symposium on Information Theory, pp. 2499–2503. IEEE (2011)
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Liebig, T., Xu, Z., May, M. (2013). Incorporating Mobility Patterns in Pedestrian Quantity Estimation and Sensor Placement. In: Nin, J., Villatoro, D. (eds) Citizen in Sensor Networks. CitiSens 2012. Lecture Notes in Computer Science(), vol 7685. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36074-9_7
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DOI: https://doi.org/10.1007/978-3-642-36074-9_7
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