Mobility Mining Using Nonnegative Tensor Factorization

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Nosratabadi, HE (Author) Other; Fanaee T, H (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2017)

Pages: 321-330

18th EPIA Conference on Artificial Intelligence (EPIA)

Univ Porto, Fac Engn, Porto, PORTUGAL, SEP 05-08, 2017

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00M-YHT

DOI: 10.1007/978-3-319-65340-2_27

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10