广东工业大学学报 ›› 2022, Vol. 39 ›› Issue (03): 1-7.doi: 10.12052/gdutxb.210158

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GeoHash与KNN在共享单车停靠点优化选择中的应用

王小霞, 欧阳露, 郑诗琪, 胡三根, 韩霜   

  1. 广东工业大学 土木与交通工程学院, 广东 广州 510006
  • 收稿日期:2021-10-24 出版日期:2022-05-10 发布日期:2022-05-19
  • 通信作者: 胡三根(1989-),男,讲师,博士,主要研究方向为交通系统建模与仿真等,E-mail:husangen2010@163.com
  • 作者简介:王小霞(1981-)，女，副教授，博士，主要研究方向为土木水利与交通土建工程
  • 基金资助:
    国家自然科学基金青年基金资助项目(61803092，718010520)

Application of GeoHash and KNN in the Optimization Selection of Shared Bicycle Stops

Wang Xiao-xia, Ouyang Lu, Zheng Shi-qi, Hu San-gen, Han Shuang   

  1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-10-24 Online:2022-05-10 Published:2022-05-19

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摘要： 针对共享单车停靠点供需时空失衡、潮汐效应明显、优化选择水平低的问题，本文把区域编码与分类学习方法相结合，提出一种基于GeoHash与K最近邻模型(K Nearest Neighbor model，KNN)的共享单车停靠点优化选择方法。首先，在分析现有共享单车停靠问题的基础上，利用GeoHash算法的区域编码分割，得到停靠点空间分布；然后，在停靠点可供选择数量和可容纳车辆数量的双重约束条件下，利用KNN聚类算法进行二次划分，完成共享单车停靠点的优化选择；最后，利用厦门市思明区和湖里区共享单车数据进行综合评价。研究结果表明，该方法具有一定的合理性，能够为缓解共享单车停靠点潮汐现象提供有益借鉴。

关键词: 共享单车, 停靠点优化, GeoHash编码, KNN算法, 潮汐现象

Abstract: To deal with the problems of time-space imbalance between supply and demand of shared bicycle stops, obvious tidal effect and low level of optimization selection, by combining region coding with classification learning method, an optimal selection method of shared bicycle stops is proposed based on GeoHash and KNN. Firstly, based on the analysis of the existing shared bicycle parking problem, the spatial distribution of parking points is obtained by using the region coding segmentation of GeoHash algorithm. Then, under the double constraints of the number of stops and the bicycles that can be accommodated, the KNN clustering algorithm is used for secondary division to complete the optimal selection of shared bicycle stops. Finally, a comprehensive evaluation is carried out based on the shared bicycle data of Siming District and Huli District in Xiamen. The result shows that this method is reasonable, which can provide a useful reference for alleviating the tide phenomenon of shared bicycle stops.

Key words: shared bicycle, optimization of stops, GeoHash encoding, KNN algorithm, tide phenomenon

中图分类号: 

  • U121
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