Enhancing Carsharing Experiences for Barcelona Citizens with Data Analytics and Intelligent Algorithms

Carsharing practices are spreading across many cities in the world. This paper analyzes real-life data obtained from a private carsharing company operating city of Barcelona, Spain. After describing main trends data, machine learning and time-series analysis methods employed to better understand citizens’ needs behavior, as well make predictions about evolution their demand for this service. In addition, an original proposal is made regarding location pick-up points. based on capacitated dispersion algorithm, aims at balancing two relevant factors, including scattering points (so that most users can benefit service) efficiency areas with higher covered). Our aim gain deeper understanding behavior relation services. The includes three components: descriptive, predictive, prescriptive, resulting customer segmentation forecast service demand, concepts optimizing parking station location.