  Public bike renting is more and more popular in cities to incentivise a
reduction in car journeys and to boost the use of green transportation
alternatives. One of the challenges of this application is to effectively plan
the resources usage. This paper presents some analysis of Dublin bike renting
scheme based on statistics and data mining. It provides available bike patterns
at the most interesting bike stations, that is, the busiest and the quietest
stations. Consistency checking with new data reinforces confidence in the
patterns obtained. Identifying available bike patterns helps to better address
user needs such as organising the rebalancing of the bike numbers between
stations in advance of demand.
