Model-based count series clustering for Bike-sharing system usage mining, a case study with the Vélib’ system of Paris. CÔME ETIENNE and OUKHELLOU LATIFA

The bicycle sharing systems are increasingly numerous nowadays. These transportation systems generate sizable transportation data the mining of which can reveal the underlying urban phenomenons linked to city dynamics. This paper introduces a statistical model to automatically analyze bike sharing system trips data. This model will introduce a latent variable to partition the stations in terms of their temporal dynamics over the day with respect to the number of rented and returned bikes. This generative model is based on Poisson mixtures and introduces a station scaling factor that handles the discrepancy between the stations activities. Eventually, the difference of dynamics between week days and week-end will also be taken into account. This model will find the latent factors that shape the geography of trips. The results produced by such an approach give insights on the relationships between stations neighborhoods type (the amenities it offers, its sociology, ...) and the generated mobility pattern. In other words, the proposed method enables the discovery of regions of different functions, that induce specific usage patterns in BSS data. These potentials are demonstrated through an in-depth analysis of the results obtained on the Vélib’ large-scale bike sharing system of Paris.