Data-Adaptive Simulation: Cooperativeness of Users in Bike-Sharing Systems

TitleData-Adaptive Simulation: Cooperativeness of Users in Bike-Sharing Systems
Publication TypeConference Paper
Year of Publication2015
AuthorsPreisler, T, Dethlefs, T, Renz, W
EditorKersten, W, Blecker, T, Ringle, CM
Title of ProceedingsInnovations and Strategies for Logistics and Supply Chains
Conference LocationHamburg
ISBN Number978-3-7375-7805-9
Other NumbersISSN (print) 2365-4430, ISSN (online) 2365-5070
KeywordsBike-Sharing System, Data-Adaptive, Self-Organization, Simulation

Bike-sharing systems undergo a rapid expansion due to technical improvements in
the operation combined with an increased environmental and health awareness of
people. The acceptance of such system depends heavily on the availability of bikes
at stations. In spite of truck-based redistribution efforts by the operators, stations
still tend to become empty/full, especially in rush-hour situations. In this paper, we
explore an incentive scheme that encourages users to approach nearby stations for
renting or returning bikes, thereby redistributing them in a self-organized fashion. A
cooperativeness parameter is determined by the fraction of users that responds to
an incentive by choosing the proposed stations. The microscopic simulations of the
actual bike-sharing system is based on data taken from Washington, D.C. (2014).
From these data, stochastic parameters can be determined such as the rush of users
for a station given as a function over time. Here, we propose a data-adaptive simulation
approach to measure the impact of different cooperativeness parameters. The
proposed approach realizes a data-adaptive simulation where the knowledge/data
space of the application is filled on demand. If knowledge/data already exist, no further
simulations are required. If not, the required microscopic simulations are executed
and the data set is enriched with their results.