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

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

更新时间：2026-05-27

- GeoHash与KNN在共享单车停靠点优化选择中的应用
- Application of GeoHash and KNN in the Optimization Selection of Shared Bicycle Stops
- 广东工业大学学报 2022年39卷第3期页码：1-7
- 作者机构：
  
  广东工业大学土木与交通工程学院,广东,广州,510006
- 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
  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
- 基金信息：
  
  国家自然科学基金青年基金资助项目(61803092，718010520)
- DOI：10.12052/gdutxb.210158
  中图分类号： U121
- 网络首发：2022-05-10，
  
  纸质出版：2022
- 稿件说明：
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王小霞, 欧阳露, 郑诗琪, 胡三根, 韩霜. GeoHash与KNN在共享单车停靠点优化选择中的应用[J]. 广东工业大学学报, 2022, 39(3): 1-7.

Wang Xiao-xia, Ouyang Lu, Zheng Shi-qi, et al. Application of GeoHash and KNN in the Optimization Selection of Shared Bicycle Stops[J]. 2022, 39(3): 1-7.
王小霞, 欧阳露, 郑诗琪, 胡三根, 韩霜. GeoHash与KNN在共享单车停靠点优化选择中的应用[J]. 广东工业大学学报, 2022, 39(3): 1-7. DOI： 10.12052/gdutxb.210158.

Wang Xiao-xia, Ouyang Lu, Zheng Shi-qi, et al. Application of GeoHash and KNN in the Optimization Selection of Shared Bicycle Stops[J]. 2022, 39(3): 1-7. DOI： 10.12052/gdutxb.210158.

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。

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.

关键词

Keywords

references

YUE Y J, HU H H. An analysis of the governance of sharing bicycles in cities from the perspective of multiple theories [J]. Contemporary Economy Management, 2019, 41(7): 68-72.

SUN Z, LI Y M. Solving a static bike repositioning problem with multiple depots[J]. Operations Research and Management Science, 2021, 30(1): 121-129.

LIU Y X, NIU Z W. Research on urban governance problems caused by shared bikes [J]. Urban Development Studies, 2021, 28(5): 135-140.

SHUI C S, SZETO W Y. A review of bicycle-sharing service planning problems [J]. Transportation Research Part C:Emerging Technologies, 2020, 117(C): 102648.

HE M J, MA X Y, JIN Y C. Station importance evaluation in dynamic bike-sharing rebalancing optimization using an entropy-based TOPSIS approach [J]. IEEE Access, 2021, 9: 38119-38131.

LIN J R, YANG T H. Strategic design of public bicycle sharing systems with service level constraints [J]. Transportation Research Part E:Logistics and Transportation Review, 2011, 4(47): 284-294.

SUN W X, ZHAO X J, MA X J. Optimal configuration of station of internet rental bicycle-transit system based on complex network theory [J]. Highway and Transportation Research and Development, 2018, 35(10): 111-116.

JIN S, PANG M B. Integrated optimization of community connection planning and bus dispatching for urban rail transit considering shared bicycle [J]. Highway and Transportation Research and Development, 2020, 37(2): 123-129.

ERDOGAN G, BATTARRA M, CALVO R W. An exact algorithm for the static rebalancing problem arising in bicycle sharing systems [J]. European Journal of Operational Research, 2015, 245(3): 667-679.

LIU J W, DAI Y, YANG F, et al . A cooperative covering model for location of bike sharing stations and its fleet deployment [J ] . Industrial Engineering and Management, 2020, 25(1): 127-135.

AHMED A, KADRE, IMDE K, et al . Lower and upper bounds for scheduling multiple balancing vehicles in bicycle-sharing systems [J ] . Soft Computing, 2019, 23(14): 5945-5966.

JIN S, PANG M B. Planning of shared bicycle stop for urban rail transit community connection based on K-means [J]. Science Technology and Engineering, 2019, 19(30): 343-347.

HUANG K, LI G Q, WANG J. Rapid retrieval strategy for massive remote sensing metadata based on GeoHash coding [J]. Remote Sensing Letters, 2018, 9(11): 1070-1078.

JIN A, CHENG C Q, SONG S H, et al . Regional query of area data based on GeoHash [J ] . Geography and Geo-information Science, 2013, 29(5): 31-35.

MA C C, DU X H, CAO L F. Improved KNN algorithm for fine-grained classification of encrypted network flow [J]. Electronics, 2020, 9(2): 324.

NGUYEN G B, LE H H, TRAN H H. Improvement of K-nearest neighbors (KNN) algorithm for network intrusion detection using Shannon-Entropy [J]. Journal of Communications, 2021, 16(8): 347-354.

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