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.

翻译：本文采用机器学习技术预测共享单车系统各站点的自行车归还与取用数量差值。这些预测结果被整合至一个仿真框架中，该框架用于支持长期决策并模拟包含自行车调度在内的日常动态。我们通过两种方式评估基于机器学习的预测质量：首先，将预测结果与其他替代预测方法进行对比；其次，分析预测结果对仿真框架输出质量的影响。评估基于意大利布雷西亚市现行共享单车系统的真实运营数据。