Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon

With urban development in cities, shared bicycle systems are increasingly used as a way to avoid traffic caused by cars, promoting sustainable mobility and contributing for traffic and pollution reduction in urban areas. The imbalance in the availability of bicycles and docks at the stations of the systems makes it impossible to rent and return bicycles, making it necessary to redistribute them across the network. However, this process has flaws, mainly during rush hours. In this paper, we analyse data provided by the Lisbon City Council regarding their bike sharing system, which has the rebalancing operations' influence. Since the original data was contaminated with the rebalancing operations, an analysis was conducted in an attempt to remove this influence from the data. Following this analysis, a new dataset was created using only the trip data to enable model development for each station and predict the bicycle demand. The plateaus in the created dataset were then analysed to determine if they're due to lack of demand from costumers, or due to stations being full or empty.