Rental bike sharing is an urban mobility model that is affordable and ecofriendly. The public bike sharing model is widely used in several cities across the world over the past decade. Because bike use is rising constantly, understanding the system demand in prediction is important to boost the operating system readiness. This article presents a prediction model to meet user demands and efficient operations for rental bikes using Random Forest (RF), which is a homogeneous ensemble method. The approach is carried out in Seoul, South Korea to predict the hourly use of rental bikes. RF is compared with Support Vector Machine with Radial Basis Function Kernel, k-nearest neighbor and Classification and Regression Trees to verify RF supremacy in rental bike demand prediction. Performance Index measures the efficiency of RF compared to the other predictive models. Also, the variable importance analysis is performed to assess the most important characteristics during different seasons by creating a predictive model using RF for each season. The results show that the influence of variables changes depending on the seasons that suggest different operating conditions. RF models trained with yearly and seasonwise models show that bike sharing demand can be further improved by considering seasonal change.

中文翻译：

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使用随机森林算法进行季节性自行车共享需求分析

共享单车租赁是一种经济实惠且环保的城市出行模式。过去十年，公共自行车共享模式在全球多个城市得到广泛应用。由于自行车使用量不断增加，因此了解预测中的系统需求对于提高操作系统的准备度非常重要。本文提出了一种使用随机森林（RF）（一种同质集成方法）的预测模型，以满足用户需求和租赁自行车的高效运营。该方法在韩国首尔实施，用于预测租赁自行车的每小时使用情况。将 RF 与具有径向基函数核、k最近邻以及分类和回归树的支持向量机进行比较，以验证 RF 在租赁自行车需求预测中的优势。性能指数衡量 RF 与其他预测模型相比的效率。此外，还执行变量重要性分析，通过使用每个季节的 RF 创建预测模型来评估不同季节期间最重要的特征。结果表明，变量的影响随着季节的变化而变化，这表明不同的操作条件。使用年度和季节模型训练的 RF 模型表明，考虑季节变化可以进一步改善自行车共享需求。