Mobility Mining Using Nonnegative Tensor Factorization

Abstract

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.

An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition

“…In the literature, tensor decomposition has previously been used to analyze transport networks: [19] used a regularized non-negative Tucker decomposition (rNTD) method to discover the urban spatio-temporal structure from time-evolving traffic networks; ref [20] proposed a grey prediction model for short-time traffic flows based on tensor decomposition; a tensor-based framework combining with "priori modeling" and "posterior analysis" to forecast peak-hour passenger was proposed by [5]; a hybrid approach combining the tensor decomposition and clustering techniques was presented in [21] to extract the features of traffic flow of urban road networks; a mobility pattern mining framework based on a non-negative tensor model called BetaNTF was proposed and applied to analyze bike sharing network mobility data in Boston, MA [22]; a sparsity constraint nonnegative tensor factorization (SNTF) method was used to study mobility patterns from the location based social networks (LBSNs) usage data [23]; a multi-way probabilistic factorization model based on the concept of tensor decomposition and probabilistic latent semantic analysis (PLSA) was applied on a four-way dataset recording 14 million public transport journeys extracted from smart card transactions in Singapore [24]; in ref [25], a non-negative tensor factorization was used to extract underlying spatio-temporal movement patterns from large-scale urban trajectory data; and finally, ref [2] applied NCP tensor decomposition to discover the main characteristics of travel patterns in the metro network of Shenzhen in China.…”