大数据 ›› 2019, Vol. 5 ›› Issue (1): 87-97.doi: 10.11959/j.issn.2096-0271.2019007

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共享单车运营分析及决策研究

张红,周迪新,程传祺,沙毓   

  1. 兰州理工大学计算机与通信学院,甘肃 兰州 730050
  • 出版日期:2019-01-01 发布日期:2019-02-01
  • 作者简介:张红(1977- ),女,博士,兰州理工大学副教授,主要研究方向为交通大数据、机器学习。|周迪新(1996- ),男,兰州理工大学本科生,主要研究方向为机器学习、数据挖掘。|程传祺(1995- ),男,兰州理工大学硕士生,主要研究方向为机器学习。|沙毓(1996- ),男,兰州理工大学本科生,主要研究方向为机器学习。
  • 基金资助:
    国家自然科学基金资助项目(No.61663021);甘肃省高校科研基金资助项目(No.2015B-031)

Study on operation analysis and decisionmaking for sharing-bicycles

Hong ZHANG,Dixin ZHOU,Chuanqi CHENG,Yu SHA   

  1. College of Computer&Communication, Lanzhou University of Technology, Lanzhou 730050, China
  • Online:2019-01-01 Published:2019-02-01
  • Supported by:
    The National Natural Science Foundation of China(No.61663021);Scientific Research Projects of Universities in Gansu(No.2015B-031)

摘要:

针对共享单车运营过程中出现的分配不均衡和调度不合理的问题,基于某城市10个区域的共享单车骑行记录数据,综合应用时空统计及回归演绎分析和群智能算法,在分析共享单车时空分布特征的基础上,研究了基于蚁群算法的单车调度路径优化,设计了基于满足程度的共享单车区域最佳分配方案,并建立了共享单车投放量和打车人次间的回归模型,探讨了共享单车对打车市场的影响。研究结果对解决共享单车运营中存在的问题和提高共享单车运营效率及管理水平有重要的指导意义。

关键词: 共享单车, 时空分布, 蚁群算法, 满足程度, 决策支持

Abstract:

Aiming at the problem of unbalanced distribution and unreasonable scheduling in the process of sharing-bicycles operating, based on the data of sharing-bicycles riding record for ten regions in a city, spatio-temporal statistics, regression deduction analysis and swarm intelligence algorithm were synthetically applied to analyze the spatio-temporal distribution characteristics and the optimization of path scheduling based on ant colony algorithm of sharing-bicycles was studied. At the same time, a optimal allocation scheme of sharing-bicycles based on satisfaction degree was designed. Finally, a regressive model of the number of bikes and taxi passengers was established to discuss the influence of sharing-bicycles on the taxi market. The research resultshave important guiding significance for solving the problems existing in the process of running and improving the operational efficiency and management level of sharing-bicycles.

Key words: haring-bicycles, spatio-temporal distribution, ant colony algorithm, satisfaction degree, decision-making

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