The impact of machine learning forecasting on strategic decision-making for bike sharing systems
研究用机器学习预测共享单车各站点借还车差额，并整合到仿真框架中支持长期决策和车辆调度，基于意大利布雷西亚的真实数据评估预测质量。
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 analyse 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.