Optimizing Bicing: Data Analytics for Operational Efficiency in Barcelona’s Bike Sharing System

Abstract

Bicing, the bike sharing system in Barcelona, plays a crucial role in the promotion of sustainable urban mobility by significantly reducing carbon emissions and alleviating traffic congestion. As an integral component of the city’s transport infrastructure, bicycling provides an accessible eco- friendly alternative to carbon-motorized transport, encouraging healthier and more sustainable lifestyles. However, despite these benefits, the system faces notable operational challenges, particularly regarding efficient management of resources and maintenance reliability. Current strategies have room for improvement, especially in terms of optimizing the availability and the condition of bicycles. This thesis addresses these challenges by proposing a set of strategies to improve operational efficiency and user satisfaction. These include optimizing bicycle redistribution using advanced data analytics to predict demand patterns, enhancing predictive maintenance algorithms to reduce bicycle downtime. To implement these improvements, a web-based application has been developed. This application is designed to facilitate real-time monitoring and decision- making support. By streamlining operations, increasing bicycle availability, and delivering a more reliable and responsive service, this system ultimately aims to enhance the overall user experience and contribute further to the sustainability of urban mobility.