A Cluster-Based Approach Using Smartphone Data for Bike-Sharing Docking Stations Identification: Lisbon Case Study

Abstract

Urban mobility is a massive issue in the current century, being widely promoted the need of adopting sustainable solutions regarding transportation within large urban centres. The evolution of technologies has democratised smart cities to better plan and manage their mobility solutions, without compromising the social, economic, and environmental impacts. Pursuing the carbon neutrality and the climate agreement goals, soft mobility is one of the most popular emerging methods to provide greener alternatives regarding mobility. Among these transportation modes are the bicycle, which has been widely used in several public systems across the world, one of them being in Lisbon. This article provides a decision support system for bike-sharing docking stations for three council parishes of the city, namely, Parque das Nações, Marvila, and Beato. Taking advantage of clustering methods and GSM data from a telecommunication operator, this study pretends to highlight a novel approach to identify soft mobility hotspots, in specific bike-sharing docking stations, for suited mobility management systems in Lisbon’s city centre.