@article{ref1,
title="Rebalancing strategy for bike-sharing systems based on the model of level of detail",
journal="Journal of advanced transportation",
year="2021",
author="Hu, Zhenghua and Huang, Kejie and Zhang, Enyou and Ge, Qi'ang and Yang, Xiaoxue and Chen, Chi-Hua",
volume="2021",
number="",
pages="e3790888-e3790888",
abstract="Traveling by bike-sharing systems has become an indispensable means of transportation in our daily lives because green commuting has gradually become a consensus and conscious action. However, the problem of “difficult to rent or to return a bike” has gradually become an issue in operating the bike-sharing system. Moreover, scientific and systematic schemes that can efficiently complete the task of rebalancing bike-sharing systems are lacking. This study aims to introduce the basic idea of the <italic>k</italic>-divisive hierarchical clustering algorithm. A rebalancing strategy based on the model of level of detail in combination with genetic algorithm was proposed. Data were collected from the bike-sharing system in Ningbo. <br><br>RESULTS showed that the proposed algorithm could alleviate the problem of the uneven distribution of the demand for renting or returning bikes and effectively improve the service from the bike-sharing system. Compared with the traditional method, this algorithm helps reduce the effective time for rebalancing bike-sharing systems by 28.3%. Therefore, it is an effective rebalancing scheme.<p /> <p>Language: en</p>",
language="en",
issn="0197-6729",
doi="10.1155/2021/3790888",
url="http://dx.doi.org/10.1155/2021/3790888"
}