Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/209256 
Year of Publication: 
2015
Citation: 
[Editor:] Kersten, Wolfgang [Editor:] Blecker, Thorsten [Editor:] Ringle, Christian M. [Editor:] 978-3-7375-4059-9 [Title:] Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 20 [Publisher:] epubli GmbH [Place:] Berlin [Year:] 2015 [Pages:] 201-228
Publisher: 
epubli GmbH, Berlin
Abstract: 
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.
Subjects: 
Bike-Sharing System
Data-Adaptive
Simulation
Self-Organization
Creative Commons License: 
cc-by-sa Logo
Document Type: 
Conference Paper

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