Exploring the Weather Impact on Bike Sharing Usage Through a Clustering Analysis

作     者：Quach, Jessica Malekian, Reza 

作者机构：Department of Computer Science and Media Technology Malmö University Malmö205 06 Sweden Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria0002 South Africa 

出 版 物：《Journal of Computers (Taiwan)》 (J. Comput.)

年 卷 期：2022年第33卷第5期

页      面：163-173页

核心收录：

主　　题：Cluster analysis 

摘      要：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. © 2022 Authors. All rights reserved.