Reference : Estimating bike-share trips using station level data
Scientific journals : Article
Social & behavioral sciences, psychology : Human geography & demography
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/10993/21134
Estimating bike-share trips using station level data
English
Medard de Chardon, Cyrille mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Identités, Politiques, Sociétés, Espaces (IPSE) >]
Caruso, Geoffrey mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Identités, Politiques, Sociétés, Espaces (IPSE) >]
Aug-2015
Tranportation Research Part B: Methodological
Elsevier
78
260–279
Yes
International
[en] Bicycle-sharing systems ; Bike-share ; Open data ; Trip estimation ; Rebalancing
[en] Bicycle sharing systems (BSS) have increased in number rapidly since 2007. The potential benefits of BSS, mainly sustainability, health and equity, have encouraged their adoption through support and promotion by mayors in Europe and North America alike. In most cases municipal governments desire their BSS to be successful and, with few exceptions, state them as being so. New technological improvements have dramatically simplified the use and enforcement of bicycle return, resulting in the widespread adoption of BSS. Unfortunately little evaluation of the effectiveness of differently distributed and managed BSS has taken place. Comparing BSS systems quantitatively is challenging due to the limited data made available. The metrics of success presented by municipalities are often too general or incomparable to others making relative evaluations of BSS success arduous. This paper presents multiple methodologies allowing the estimation of the number of daily trips, the most significant measure of BSS usage, based on data that is commonly available, the number of bicycles available at a station over time. Results provide model coefficients as well as trip count estimates for select cities. Of four spatial and temporal aggregate models the day level aggregation is found to be most effective for estimation. In addition to trip estimation this work provides a rigorous formalization of station level data and the ability to distinguish spatio-temporal rebalancing quantities as well as new characteristics of BSS station use.
Fonds National de la Recherche - FnR
Researchers ; Professionals
http://hdl.handle.net/10993/21134
10.1016/j.trb.2015.05.003
http://www.sciencedirect.com/science/article/pii/S0191261515000983

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