基于强化学习和PageRank算法的不可用共享单车检测方法

IF 3.7 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Yu Zhou , Ran Zheng , Gang Kou
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引用次数: 0

摘要

现有的研究模型既不能显示共享单车的可用性,也不能检测出不可用的共享单车,因为缺乏关于自行车维护和故障的信息。为了提高人们对共享单车可用性的认识,我们提出了一种基于强化学习和PageRank算法的检测不可用共享单车的创新方法。该方法根据本地出行数据识别出不可用的共享单车,并根据可用程度对共享单车进行排序。在给定滑动时间窗口的情况下,通过考虑共享单车的累计不可用数量、在同一站点取消租赁的比例以及取消租赁之间的平均时间来确定强化学习模型的值函数。然后使用强化学习来识别可用性最差的共享单车。使用PageRank算法对低于奖励阈值的共享单车进行可用性排序。将所提出的检测方法应用于实际共享单车系统的出行数据集,以说明建模过程及其有效性。无故障共享单车的检测结果和反馈数据可以为共享单车的维护管理决策提供必要的信息支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection approach for unusable shared bikes enabled by reinforcement learning and PageRank algorithm

Existing research models can neither indicate the availability of shared bikes nor detect unusable ones owing to a lack of information on bike maintenance and failure. To improve awareness regarding the availability of shared bikes, we propose an innovative approach for detecting unusable shared bikes based on reinforcement learning and the PageRank algorithm. The proposed method identifies unusable shared bikes depending on the local travel data and provides a ranking of the shared bikes according to their availability levels. Given a sliding time window, the value function for the reinforcement learning model was determined by considering the cumulative number of unavailable shared bikes, the proportion of rental cancelations at the same stations, and the mean time between the cancelations. Reinforcement learning was then used to identify shared bikes with the worst availability. An availability ranking for the shared bikes below the reward threshold was performed using the PageRank algorithm. The proposed detection approach was applied to a trip dataset of a real-world bike-sharing system to illustrate the modeling process and its effectiveness. The detection results of unusable shared bikes in the absence of failure and feedback data can provide essential information to support the maintenance management decisions regarding shared bikes.

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来源期刊
安全科学与韧性(英文)
安全科学与韧性(英文) Management Science and Operations Research, Safety, Risk, Reliability and Quality, Safety Research
CiteScore
8.70
自引率
0.00%
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0
审稿时长
72 days
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