Bike-Sharing Source-Sink Space Recognition Based on Riding Flow Density Clustering Method

TONG Zhaomin; LIU Yaolin; ZHANG Ziyi; AN Rui; ZHU Yi

doi:10.13203/j.whugis20220467

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Bike-Sharing Source-Sink Space Recognition Based on Riding Flow Density Clustering Method

Cartography and GIS | 更新时间：2026-11-05

- Bike-Sharing Source-Sink Space Recognition Based on Riding Flow Density Clustering Method
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 1, Pages: 184-196(2025)
- 作者机构：
  
  1.武汉大学资源与环境科学学院，湖北武汉，430079
  2.武汉大学地理信息系统教育部重点实验室，湖北武汉，430079
  3.地球空间信息科学协同创新中心，湖北武汉，430079
  4.自然资源部数字制图与国土信息应用重点实验室，湖北武汉，430079
  5.东华理工大学测绘工程学院，江西南昌，330032
  6.成都市国土规划地籍事务中心，四川成都，610072
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2017YFB0503601)
- DOI：10.13203/j.whugis20220467
  CLC： P208
- Received：08 May 2023，
  
  Published：05 January 2025
- 稿件说明：
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仝照民,刘耀林,张紫怡，等.骑行流密度聚类下的共享单车源汇空间识别[J].武汉大学学报（信息科学版）,2025,50(1):184-196.

TONG Zhaomin,LIU Yaolin,ZHANG Ziyi,et al.Bike-Sharing Source-Sink Space Recognition Based on Riding Flow Density Clustering Method[J].Geomatics and Information Science of Wuhan University,2025,50(1):184-196.
仝照民,刘耀林,张紫怡，等.骑行流密度聚类下的共享单车源汇空间识别[J].武汉大学学报（信息科学版）,2025,50(1):184-196. DOI：10.13203/j.whugis20220467

TONG Zhaomin,LIU Yaolin,ZHANG Ziyi,et al.Bike-Sharing Source-Sink Space Recognition Based on Riding Flow Density Clustering Method[J].Geomatics and Information Science of Wuhan University,2025,50(1):184-196. DOI：10.13203/j.whugis20220467

摘要

共享单车的时空分布不均给运营商和用户造成了不便，有必要识别共享单车的空间发散区（源）及聚集区（汇），对其空间源汇区进行针对性的管理和调度。对共享单车骑行空间社区进行检测后，使用路网约束下共享

最邻近密度聚类方法，从空间流视角识别共享单车的典型源汇区域，探究早晚高峰骑行模式的共性和差异性，结果表明：（1）工作日共享单车分时订单曲线M型特征明显，而休息日曲线更接近梯台型，骑行以交通小区单元内部流动及邻接流动为主。（2）共享单车骑行社区边界受天然屏障的约束显著大于行政区划，早高峰共享单车使用的高值区更为集中。（3）工作日通勤早高峰时段，丛集流以“居住-就业”流向为主，聚散区域呈现“一面-一线-多点”的空间分布特征；晚高峰时段，丛集流以“地铁站-居住区”为主，“共享单车-地铁站-共享单车”的联运特征突出。

Abstract

Objectives

The shared bike is an important tool for residents to connect to public transport and travel a short distance

but its uneven space-time distribution has caused inconvenience to operators and users

especially in the morning and evening rush hours of commuting on weekdays. Therefore

it is necessary to identify the spatial divergence area (source) and convergence area (sink) of shared bikes

and carry out targeted management and scheduling of their spatial source-sink area. However

most studies aggregate flow data into polygon units

which lose the precision of flow data and cannot provide accurate spatial location of source and sink areas. We use a method combining spatial thinning with flow density clustering to effectively identify the spatial location of the source and sink area of bike-sharing flow.

Methods

Spatial community detection algorithm and density-based clustering method for origin-destination riding flow are used to identify the typical source and sink regions of shared bikes. The first thing is to clean the original dataset

including deleting duplicate orders

orders with missing attributes and abnormal orders

and performing coordinate matching. After that

using the fast Fourier transform algorithm to test the time periodicity of the dataset. Then the fast unfolding algorithm is used to detect the spatial communities and dilute the order data of shared bikes on this basis. Finally

the spatial distance of bike-sharing flow based on the road network is defined

and the shared

nearest neighbor flow clustering method is used to identify typical source and sink regions.

Results

(1) The curve of bike-sharing timely orders on weekdays has obvious M-shaped characteristics

while the curve of weekends is more similar to the ladder type. At the scale of traffic analysis zone

the flow of shared bikes is mainly adjacent flow and internal flow. (2) The boundary of bike-sharing community is significantly more constrained by natural barriers than administrative divisions

and the high-value bike-sharing usage areas are more concentrated in the morning peak than evening. (3) During the morning rush hours of commuting on weekdays

the unidirectional flow clusters are mainly “residence to employment”

and the convergent and divergent areas show the spatial distribution characteristics of “one area

one line and many points”. In the evening peak hours

the flow clusters are mainly the pattern of subway station to residential area

and the intermodal characteristics of “shared bikes-subway station-shared bikes” are prominent.

Conclusions

Based on the perspective of flow space

we analyze the characteristics of residents' short-distance shared travel. The proposed method effectively identifies the spatial source and sink area of shared bikes

which fills the smallest link in the study of urban internal mobility links

and provides support for the optimal allocation of shared bicycle space.

关键词

Keywords

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