Exploring the weather impact on bike sharing usage through a clustering analysis
This work addresses bike sharing system operators by providing insights for rebalancing strategies, but it is incremental as it builds on existing methods with new data.
The study tackled the problem of understanding how weather conditions affect bike sharing usage by analyzing data from Washington D.C. using k-means clustering, finding that temperature and precipitation had the most significant impact on usage patterns.
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.