The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems
This work addresses operational challenges for bike sharing system managers, but it is incremental as it applies existing machine learning techniques to a specific domain.
The paper tackled the problem of forecasting bike imbalances in bike sharing systems using machine learning, and integrated these forecasts into a simulation framework to support long-term strategic decisions, with evaluation on real-world data from Brescia, Italy.
In this paper, machine learning techniques are used to forecast the difference between bike returns and withdrawals at each station of a bike sharing system. The forecasts are integrated into a simulation framework that is used to support long-term decisions and model the daily dynamics, including the relocation of bikes. We assess the quality of the machine learning-based forecasts in two ways. Firstly, we compare the forecasts with alternative prediction methods. Secondly, we analyze the impact of the forecasts on the quality of the output of the simulation framework. The evaluation is based on real-world data of the bike sharing system currently operating in Brescia, Italy.