Predicting Occupancy Trends in Barcelona's Bicycle Service Stations Using Open Data

TL;DR

This paper demonstrates that using open data and machine learning, it is possible to predict station occupancy in Barcelona's bike-sharing system up to two days in advance, enhancing service reliability.

Contribution

The study introduces a predictive model using Random Forest and open data to forecast bike station statuses, improving upon prior reactive approaches.

Findings

Nearly 50% accuracy in predicting full or empty stations two days ahead

Utilized weather and other open data to enhance model performance

Potential to improve user experience and station management

Abstract

In 2008, the CEO of the company that manages and maintains the public bicycle service in Barcelona recognized that one may not expect to always find a place to leave the rented bike nearby their destination, similarly to the case when, driving a car, people may not find a parking lot. In this work, we make predictions about the statuses of the stations of the public bicycle service in Barcelona. We show that it is feasible to correctly predict nearly half of the times when the stations are either completely full of bikes or completely empty, up to 2 days before they actually happen. That is, users might avoid stations at times when they could not return a bicycle that they have rented before, or when they would not find a bike to rent. To achieve that, we apply the Random Forest algorithm to classify the status of the stations and improve the lifetime of the models using publicly available data, such as information about the weather forecast. Finally, we expect that the results of the predictions can be used to improve the quality of the service and make it more reliable for the users.