论文标题
通过聚类分析探索对自行车共享使用的天气影响
Exploring the weather impact on bike sharing usage through a clustering analysis
论文作者
论文摘要
多年来，自行车共享系统（BSS）一直是一种受欢迎的旅行服务，并且在全球范围内使用。它对想要促进更健康生活方式的城市和用户具有吸引力；减少空气污染和温室气体排放并改善流量。停靠自行车共享系统的一个主要挑战是重新分配自行车和平衡码头站。一些研究提出了可以帮助预测自行车使用情况的模型。重新平衡自行车分配的策略；建立模式或如何识别模式。其他研究建议通过包括天气数据来扩展该方法。这项研究旨在扩展这些建议和机会，以探讨天气如何影响自行车使用情况。自行车使用数据和天气数据是为华盛顿特区收集的，并使用K-均值聚类算法进行了分析。 K均值设法确定了三个与自行车使用相对应的群集，具体取决于天气条件。结果表明，在集群之间，对自行车使用的天气影响很明显。这表明温度随后降水加权最大，在五个天气变量中。
Bike sharing systems (BSS) have been a popular traveling service for years and are used worldwide. It is attractive for cities and users who wants to promote healthier lifestyles; to reduce air pollution and greenhouse gas emission as well as improve traffic. One major challenge to docked bike sharing system is redistributing bikes and balancing dock stations. Some studies propose models that can help forecasting bike usage; strategies for rebalancing bike distribution; establish patterns or how to identify patterns. Other studies propose to extend the approach by including weather data. This study aims to extend upon these proposals and opportunities to explore how and in what magnitude weather impacts bike usage. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed using k-means clustering algorithm. K-means managed to identify three clusters that correspond to bike usage depending on weather conditions. The results show that the weather impact on bike usage was noticeable between clusters. It showed that temperature followed by precipitation weighted the most, out of five weather variables.
