  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.
