The Master in City & Technology’s academic structure is based on IAAC’s innovative, learn-by-doing and design-through-research methodology which focuses on the development of interdisciplinary skills. During the Master in City & Technology students will have the opportunity to be part of a highly international group, including faculty members, researchers, and lecturers, in which they are encouraged to develop collective decision-making processes and materialize their project ideas.

Machine Learning to predict Bicing availability

Introduction to Bicing Bicing is the bike-sharing service in Barcelona, Spain, providing residents and tourists with an eco-friendly, convenient, and affordable means of transportation. Launched in 2007, Bicing has grown to include thousands of bicycles distributed across hundreds of docking stations throughout the city. Users can pick up a bike at one station and return … Read more

Never Miss A Ride

Introduction This presentation outlines the development and evaluation of machine learning models aimed at predicting the availability of bikes at various bicing stations in Barcelona. We’ll discuss the variables involved, the modeling techniques used, and compare the performance of different models. Python is a friendly environment for preparing, training and forecasting machine learning algorithms within … Read more

Predicting Biking Station Vacancy in Barcelona

Introduction Urban transportation planning relies on data science to explain the conditions driving mobility patterns. This exploration of bicing, Barcelona’s resident bike rental program, analyzes the actors impacting discrepancies in bicing data to select machine-learning strategies able to predict biking station vacancies across Barcelona for the year 2024 with an accuracy of 0.02387. With this … Read more