Jardim, João Bruno Morais de SousaRodrigues, Duarte Nuno Antunes Caracol BarrosLouro, Tomás Conceição de Campos Cunha2025-11-112025-11-112025-10-28http://hdl.handle.net/10362/190479Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe fast paced evolution of modern cities and the urgent need for sustainable mobility solutions have positioned Bike-Sharing Systems as an essential tool to promote greener, healthier and more efficient urban transportation. This study focuses on GIRA, Lisbon’s bike-sharing system, and aims to design and implement a Business Intelligence framework to support data-driven decision-making. Based in the Kimball Data Warehouse/Business Intelligence Lifecycle, this research provides a structured and robust solution that enables GIRA stakeholders to better understand and optimize the system's performance. As scope of the study three key domains were identified: Usage Patterns, Stations, and Weather. Connected to these domains a set of research questions was formulated to explore user behavior across different calendar days, station-level performance and availability, and the influence of meteorological conditions on trip demand. Leveraging from the open data available a dimensional model was developed, comprising three fact tables – Trip, Occupancy and Weather, and three dimensional tables – Station, Date and Time. The final BI solution includes an interactive PowerBI report that visualizes past usage key performance indicators and trends, supporting operational and strategic decision-making. Findings from the dashboard were compared with insights from a systematic literature review, revealing convergence. Results confirmed that proximity to transportation hubs and academic institutions is associated with higher demand for rides, aligning with international studies on the impact of built environment on BSS usage. Temporal patterns, including the usage fluctuation across different weekdays, weekends and holidays, were consistent with prior findings from other BSS implementations. Weather conditions were also found to affect the demand for rides, with increased ridership observed in cold and mild temperatures along with dry conditions. This study is aligned with the Sustainable Development Goals 11 and 13 and contributes to the ongoing discourse on sustainable urban mobility. By transforming raw operational data into insights, the developed BI solution enhances the strategic planning and operational efficiency of GIRA.engBusiness IntelligenceAnalyticsBike Sharing SystemsSoft MobilityUrban PlanningSDG 11 - Sustainable cities and communitiesSDG 13 - Climate actionmaster thesis204071860