Abstract
This paper provides an analysis of mobility data of the bike sharing system in Mexico City from the year 2010 to 2015. Based on about 18 million trips, it is possible to compute the mean time of a trip and the number of trips from and to a single station. The 444 stations were classified according to the average number of input and output bikes using a Pareto chart. The patterns of mobility between the stations are shown graphically; thus, some clusters are identified based on the number of arrivals and departures. Finally, the number of input and output for some stations is plotted in ten-minute intervals for weekdays and weekends. Therefore, it is known the flow of bikes in stations per time interval per day. These patterns are applied to predict the behavior of a particular station to define policies to improve the bike system program. An R script, available to the public, is programmed to manage the amount of data and to carry on the analysis.
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