Public bike-sharing (PBS) systems have expanded to major cities around the world in efforts to mitigate air pollution, traffic congestion and traffic accidents. Users can pickup and drop-off bicycles at any station, and thus inventory imbalances can occur. To improve system efficiency, system operators should establish appropriate repositioning strategies based on accurate predictions of demand for bicycles. This study aims to predict station-level demand for pickup and drop-off of bicycles using station activity information. In addition to time and weather information, the number of pickups and drop-offs at a station 1–3 h before the prediction was used as a predictor. A random forest machine learning technique is adopted for the demand prediction. The PBS database in Seoul, South Korea was used for the case study. To compare prediction accuracy by station usage patterns, the stations are classified into four clusters. The analysis results show that prediction accuracy including lag information provides mprovements of up to 20%, and the forecast for drop-off is more accurate than the forecast for pickup. This study practically contributes to increasing operational efficiency and reducing operating costs by improving demand predictability in a PBS system.

中文翻译：

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基于随机森林的具有站点活动的共享单车系统的需求预测

公共自行车共享 (PBS) 系统已扩展到世界各地的主要城市，以减轻空气污染、交通拥堵和交通事故。用户可以在任何站点取放自行车，因此可能会出现库存不平衡。为提高系统效率，系统运营商应根据对自行车需求的准确预测，制定适当的重新定位策略。本研究旨在利用车站活动信息预测车站级别的自行车上下车需求。除了时间和天气信息，预测前 1-3 小时站点的上下车次数也被用作预测因子。需求预测采用随机森林机器学习技术。案例研究使用了韩国首尔的 PBS 数据库。为了通过站点使用模式比较预测精度，站点被分为四个集群。分析结果表明，包括滞后信息在内的预测精度可提供高达20%的改进，并且对下车的预测比对上车的预测更准确。该研究通过提高 PBS 系统中的需求可预测性，实际上有助于提高运营效率和降低运营成本。