Abstract
Analyzing human trajectories based on sensor data is a challenging research topic. It has been analyzed from many aspects like clustering, process mining, and others. Still, less attention has been paid on analyzing this data based on hidden factors that drive the behavior of people. We, therefore, adapt the standard matrix factorization approach and reveal factors which are interpretable and soundly explain the behavior of a dynamic population. We analyze the motion of a skier population based on data from RFID-recorded ski entrances of skiers on ski lift gates. The approach is applicable to other similar settings, like shopping malls or road traffic. We further applied recommender systems algorithms for testing how well we can predict the distribution of ski lift usage (number of ski lift visits) based on hidden factors, but also on other benchmark algorithms. The matrix factorization algorithm showed to be the best recommender score predictor with an RMSE of 2.569 ± 0.049 and an MAE of 1.689 ± 0.019 on a 1 to 10 scale.
B. Delibašić and S. Radovanović—Two authors contributed equally.
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References
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961–971 (2016)
Bell, R., Koren, Y., Volinsky, C.: Modeling relationships at multiple scales to improve accuracy of large recommender systems. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 95–104. ACM, August 2007
Bohanec, M., Delibašić, B.: Data-mining and expert models for predicting injury risk in ski resorts. In: Delibašić, B., Hernández, Jorge E., Papathanasiou, J., Dargam, F., Zaraté, P., Ribeiro, R., Liu, S., Linden, I. (eds.) ICDSST 2015. LNBIP, vol. 216, pp. 46–60. Springer, Cham (2015). doi:10.1007/978-3-319-18533-0_5
D’Urso, P., Massari, R.: Fuzzy clustering of human activity patterns. Fuzzy Sets Syst. 215, 29–54 (2013)
Davis, D.A., Chawla, N.V., Christakis, N.A., Barabási, A.L.: Time to CARE: a collaborative engine for practical disease prediction. Data Min. Knowl. Discov. 20(3), 388–415 (2010)
Delibašić, B., Marković, P., Delias, P., Obradović, Z.: Mining skier transportation patterns from ski resort lift usage data. IEEE Trans. Hum. Mach. Syst. 47(3), 417–422 (2016)
Ferrer, G., Sanfeliu, A.: Bayesian human motion intentionality prediction in urban environments. Pattern Recognit. Lett. 44, 134–140 (2014)
Gemulla, R., Nijkamp, E., Haas, P.J., Sismanis, Y.: Large-scale matrix factorization with distributed stochastic gradient descent. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 69–77. ACM, August 2011
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 135–142. ACM, September 2010
King, M.A., Abrahams, A.S., Ragsdale, C.T.: Ensemble methods for advanced skier days prediction. Expert Syst. Appl. 41(4), 1176–1188 (2014)
Lathia, N., Smith, C., Froehlich, J., Capra, L.: Individuals among commuters: building personalised transport information services from fare collection systems. Pervasive Mobile Comput. 9(5), 643–664 (2013)
Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: Proceedings of the 2005 SIAM International Conference on Data Mining, pp. 471–475. Society for Industrial and Applied Mathematics, April 2005
Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)
Ruedl, G., Kopp, M., Sommersacher, R., Woldrich, T., Burtscher, M.: Factors associated with injuries occurred on slope intersections and in snow parks compared to on-slope injuries. Accid. Anal. Prev. 50, 1221–1225 (2013)
Schamel, G.: Weekend vs. midweek stays: modelling hotel room rates in a small market. Int. J. Hosp. Manage. 31(4), 1113–1118 (2012)
Wolff, F.C.: Lift ticket prices and quality in French ski resorts: Insights from a non-parametric analysis. Eur. J. Oper. Res. 237(3), 1155–1164 (2014)
Xie, D., Shu, T., Todorovic, S., Zhu, S.C.: Modeling and inferring human intents and latent functional objects for trajectory prediction. arXiv preprint arXiv:1606.07827 (2016)
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We thank Ski resorts of Serbia and Mountain Rescue Service Serbia for allowing us to use data.
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Delibašić, B., Radovanović, S., Jovanović, M., Vukićević, M., Suknović, M. (2017). An Investigation of Human Trajectories in Ski Resorts. In: Trajanov, D., Bakeva, V. (eds) ICT Innovations 2017. ICT Innovations 2017. Communications in Computer and Information Science, vol 778. Springer, Cham. https://doi.org/10.1007/978-3-319-67597-8_13
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DOI: https://doi.org/10.1007/978-3-319-67597-8_13
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