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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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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