Abstract

Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations’ occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City’s bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule.

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The different versions of the original document can be found in:

https://doaj.org/toc/1424-8220 under the license cc-by
http://dx.doi.org/10.3390/s18020512
https://trid.trb.org/view/1503672,
https://pubmed.ncbi.nlm.nih.gov/29419771,
https://www.ncbi.nlm.nih.gov/pubmed/29419771,
http://europepmc.org/abstract/MED/29419771,
https://doi.org/10.3390/s18020512,
https://core.ac.uk/display/151104842,
https://academic.microsoft.com/#/detail/2793018423 under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.3390/s18020512
Licence: Other

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