The politics of smart expectations: Interrogating the knowledge claims of smart mobility

This paper studies the performativity of smart mobility expectations in envisioning urban futures. Smart mobility, or ICT-enabled transport services, are increasingly considered a necessary ingredient for sustainability transitions in cities. Expectations of smart mobility’s contribution to such a transition are constituted by a strong belief in the transformative potential of data collection and use. These knowledge claims embedded in smart mobility expectations tend to be unchallenged, yet contribute to a particular future vision of urban mobility. Our empirical analysis, which draws on two empirical smart cycling case studies in Utrecht, the Netherlands, and Bordeaux, France, underlines the politics of such smart knowledge claims in two smart cycling projects and identifies distinct processes as to how such claims may shape and structure mobility futures. We observe intimate entanglements between what is being developed in terms of technologies and services; and the societal needs that the projects’ expectations promise to fulfil. At the same time, we witness a disentanglement of these interconnected knowledge claims when projects unfold, leaving the promise of (un)achieved societal benefits out of view. Indeed, smart knowledge claims carried strong inherent legitimacy in the cases studied, thereby risking to exclude non-smart alternatives

Broken Bikes Detection Using CitiBike Bikeshare System Open Data

International audienceIt 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