Sensing and predicting the pulse of the city through shared bicycling

By Jon E. Froehlich, Joachim Neumann, Nuria Oliver. International Joint Conference on Artificial Intelligence, 2009

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Traffic prediction in a bike-sharing system (2015, background): “In addition, data formats are different, e.g. our dataset does not contain information about the number of available bikes/docks at each station while those in [12][13][22] do.”
Mining bicycle sharing data for generating insights into sustainable transport systems (2014, methodology): “usage) variation throughout the day and six separate spatial clusters based on the intra-day change of availability of bicycles in each docking station (Froehlich et al., 2009). In addition, Kaltenbrunner et al. (2010) looked at the system’s usage patterns across seven weeks, and also developed a si”
How Does Land-Use and Urban Form Impact Bicycle Flows--Evidence from the Bicycle-Sharing System (BIXI) in Montreal (2014, background): “…and refilling the docks of 1 A stream of studies concentrates on operational issues of bicycle-sharing systems such as maximizing efficiency of operator rebalancing program (Borgnat et al., 2011; Froehlich et al., 2009; Jensen et al., 2010; Kaltenbrunner et al., 2010; Vogel and Mattfeld, 2011).”
Prediction of urban human mobility using large-scale taxi traces and its applications (2012, background): “For the prediction problem, the work in [10,11,17] is similar to ours.”