Uncovering commuting flows in Bike Sharing Systems
Abstract
Drawing on the observed regularity of typical trips, this study proposes a method for identifying commuting patterns without using the individual identifiers of public bike-share users. The approach combines an analysis of trip duration distributions, mixture models and logistic regression based on the posterior probabilities of belonging to the _fast' and _typical' journey categories.
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