用机器学习预测共享单车站点供需差，优化长期调度决策
The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems
- 基于机器学习预测每个站点的还车与借车差额
- 对比多种预测方法，验证模型在真实数据上的高精度
- 结果可直接用于仿真系统，辅助车辆调度与长期规划
本文采用机器学习技术，预测共享单车系统中各站点的还车与借车数量差异。将预测结果集成至仿真框架，用于支持长期决策并模拟日常运营动态，包括车辆重新调度。通过意大利布雷西亚市实际运行数据，从两方面评估预测性能：一是与其它预测方法进行比较，二是分析预测对仿真输出质量的影响。实验表明，该方法能有效提升预测准确率，并显著改善仿真系统的决策支持能力。
原文摘要 · Abstract (English)
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.
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