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Sensors 2018, 18(2), 512; https://doi.org/10.3390/s18020512

A Dynamic Approach to Rebalancing Bike-Sharing Systems

1
Department of Information Engineering, University of Padova, 35131 Padova PD, Italy
2
Human Inspired Technologies (HIT) Research Center, University of Padova, 35131 Padova PD, Italy
*
Author to whom correspondence should be addressed.
Received: 18 December 2017 / Revised: 16 January 2018 / Accepted: 30 January 2018 / Published: 8 February 2018
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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. View Full-Text
Keywords: bike sharing; Smart Cities; dynamic rebalancing bike sharing; Smart Cities; dynamic rebalancing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Chiariotti, F.; Pielli, C.; Zanella, A.; Zorzi, M. A Dynamic Approach to Rebalancing Bike-Sharing Systems. Sensors 2018, 18, 512.

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