Broken Bikes Detection Using CitiBike Bikeshare System Open Data

Abstract : It seems necessary to detect a broken bike rooted at a station in near realtime as the number of bikes within bikeshare systems has reached more than a million in 2015. Indeed, a bike that cannot be moved is not cost effective in terms of number of trips. This brings frustration to users who were expecting to find a bike at that station without knowing that it is actually defective. We thus propose a methodology from feature extraction to anomaly detection on a distributed cloud infrastructure in order to detect bicycles requiring a repair. Through a first step of K-means clustering, and a second step consisting of spotting samples that do not clearly belong to any cluster, we separate anomalies from normal behaviors. The proposal is validated on a publicly available dataset provided by Motivate, the operator of the New-York bikeshare system. The number of distinct bikes that have been classified by this algorithm as broken at least once during a month is close to the number of repairs given in monthly reports of Motivate.
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Rémi Delassus, Romain Giot, Raphael Cherrier, Gabriele Barbieri, Guy Melançon. Broken Bikes Detection Using CitiBike Bikeshare System Open Data. IEEE Symposium Series on Computational Intelligence (SSCI 2016), Dec 2016, Athenes, Greece. ⟨hal-01428773⟩

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