sharing is an internationally implemented system for reducing public transit
congestion, minimizing carbon emissions, and encouraging a healthy lifestyle.
Since New York City’s launch of the CitiBike program in May 2013, however,
various issues have arisen due to overcrowding and general flow. In response to
these issues, CitiBike employees redistribute bicycles by vehicle throughout
the New York City area. During the past year, over 500,000 bikes have been
redistributed in this fashion. This solution is financially taxing,
environmentally and economically inefficient, and often suffers from timing
issues. What if CitiBike instead used its clientele to redistribute bicycles?
talk, we will describe the data analysis that we conducted in hopes of creating
an incentive and rerouting scheme for riders to self-balance the system. We
anticipate that we can decrease vehicle transportations by offering financial
incentives to take bikes from relatively full stations and return bikes to
relatively empty stations (with rerouting advice provided via an app). We used
publicly available data obtained via the CitiBike website, consisting of
starting and ending locations, times, and user characteristics for each trip
taken from July 2013 through May 2014. Using this dataset, we estimated
CitiBike traffic flow, which enabled us to build agent-based simulation models
in response to incentives and rerouting information. By estimating various
parameters under which to organize incentive schemes, we found that such a program
would help to improve CitiBike’s environmentalism and increase productivity, as
well as being financially beneficial for both CitiBike and its riders.