Mobility Mining Using Nonnegative Tensor Factorization
Mobility mining has lots of applications in urban planning and transportation systems. In particular, extracting mobility patterns enables service providers to have a global insight about the mobility behaviors which consequently leads to providing better services to the citizens. In the recent years several data mining techniques have been presented to tackle this problem. These methods usually are either spatial extension of temporal methods or temporal extension of spatial methods. However, still a framework that can keep the natural structure of mobility data has not been considered. Non-negative tensor factorizations (NNTF) have shown great applications in topic modelling and pattern recognition. However, unfortunately their usefulness in mobility mining is less explored. In this paper we propose a new mobility pattern mining framework based on a recent non-negative tensor model called BetaNTF. We also present a new approach based on interpretability concept for determination of number of components in the tensor rank selection process. We later demonstrate some meaningful mobility patterns extracted with the proposed method from bike sharing network mobility data in Boston, USA.
KeywordsMobility mining Nonnegative tensor factorization BetaNTF
This research was carried out in the framework of the project “TEC4Growth – RL SMILES – Smart, mobile, Intelligent and Large Scale Sensing and analytics NORTE-01-0145-FEDER-000020” which is financed by the north Portugal regional operational program (NORTE 2020), under the Portugal 2020 partnership agreement, and through the European regional development fund. The authors thank Antoine Liutkus for providing the code for BetaNTF and Huway Company for providing the dataset.
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