Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (8): 2639-2650.doi: 10.13229/j.cnki.jdxbgxb.20240009

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Analysis of characteristics and influencing factors of dockless bike sharing connected to metro

Jun-ze MA1(),Chang-jiang ZHENG1(),Fei WU2,Yan-yan WANG1,Ye LU1,Shu-kang ZHENG3   

  1. 1.College of Civil and Transportation Engineering,Hohai University,Nanjing 210024,China
    2.Information Department,Hohai University,Nanjing 211100,China
    3.College of Environment,Hohai University,Nanjing 210024,China
  • Received:2024-01-03 Online:2025-08-01 Published:2025-11-14
  • Contact: Chang-jiang ZHENG E-mail:jz_ma@hhu.edu.cn;zheng@hhu.edu.cn

Abstract:

To reveal the spatiotemporal characteristics of the dockless bike sharing connected to the metro and analyze the impact of built environment factors on connection demand, the built environment around the entrances and exits of Shenzhen metro stations was considered in this paper, a method for identifying connection travel was constructed by employing Thiessen polygons. This method explores the characteristics of four types of connection traffic on weekdays from both temporal and spatial perspectives, including access transfer (AT) of morning rush hour, egress transfer (ET) of morning rush hour, AT evening rush hours and ET of evening rush hours. The geographically and temporally weighted regression (GTWR) model was utilized to analyze the impact of four types of built environment factors on connection demand. The results indicate that the distribution of connection traffic exhibits spatial heterogeneity, with higher demand during the morning rush hour than during the evening rush hour. Business residential and scientific educational cultural service POIs have a positive impact on connection traffic in the urban core area, while it is negatively correlated in the suburban areas. However, the impact of transportation facility service POIs on connection traffic varies in the opposite direction. Corporate enterprise POIs have a positive impact on connection traffic in the western urban areas, while it is negatively correlated in the eastern areas.

Key words: road and traffic engineering, dockless bike sharing, metro, connection traffic, spatiotemporal characteristics, geographically and temporally weighted regression model

CLC Number: 

  • U491

Fig.1

Study area"

Table 1

Basic information of the data"

数据名称详细信息数据来源
共享单车出行记录数据集为2021年3月24日—4月6日工作日期间的出行记录,包括用户ID、骑行开始时间、骑行结束时间、骑行起点经纬度、骑行终点经纬度深圳市交通运输局
地铁站点深圳市11条地铁线路站点和出入口经纬度坐标,241个站点名称
公共交通站点深圳市公交站点经纬度坐标、站点名称
人口密度深圳市10个区74个街道的人口密度第七次全国人口普查数据
POI4类POI,包括商务住宅类、公司企业类、科教文化服务类、交通设施服务类高德地图
深圳市道路网深圳市城市道路名称OpenStreet-Map
深圳市建筑轮廓深圳市建筑轮廓

Fig.2

Schematic diagram of dockless bike sharing connected to the metro"

Fig.3

Identification method of dockless bike sharing connected to the metro"

Fig.4

Temporal distribution of dockless bike sharing connected to the metro traffic"

Fig.5

Spatial distribution of dockless bike sharing connected to the metro traffic"

Table 2

Definition and descriptive statistics of variables"

变量定义单位最小值最大值平均值标准差

早高峰ET客流量工作日早高峰时段以地铁站为起点的出站换乘客流量人次04 287669.330760.387
早高峰AT客流量工作日早高峰时段以地铁站为终点的进站换乘客流量09 831840.4181 279.973
晚高峰ET客流量工作日晚高峰时段以地铁站为起点的出站换乘客流量04 894621.410797.558
晚高峰AT客流量工作日晚高峰时段以地铁站为终点的进站换乘客流量03 687540.992593.324

人口密度街道一级的人口密度千人/km22.03357.72918.48410.716
公交站点密度地铁站出入口2 km缓冲区内的密度个/km20.95528.25016.5355.493
路网密度地铁站出入口2 km缓冲区内的密度km/km22.49221.57612.2712.894
商务住宅类POI地铁站出入口2 km缓冲区内的POI数量3716311.391156.595
公司企业类POI484 3431 585.13871.910
科教文化服务类POI05512.42910.458
交通设施服务类POI201 650654.548402.327

Table 3

Results of the multicollinearity test"

变量VIF
人口密度2.157
公交站点密度2.883
路网密度5.665
商务住宅类POI2.541
公司企业类POI2.340
科教文化服务类POI1.557
交通设施服务类POI7.616

Table 4

Results of spatial autocorrelation test"

变量人口密度公交站点密度路网密度商务住宅类POI公司企业类POI科教文化服务类POI交通设施服务类POI
莫兰指数0.7070.6170.7820.7040.7050.7450.835
预期指数-0.004-0.004-0.004-0.004-0.004-0.004-0.004
方差0.0020.0020.0020.0020.0020.0010.002
Z得分18.22615.87920.09418.11618.16019.36421.448
P0.0000.0000.0000.0000.0000.0000.000

Table 5

Comparison results of OLS, GWR, and GTWR model performance"

变量模型决定系数调整的决定系数赤池信息准则

早高峰ET

客流量

OLS0.2270.2064 155.044
GWR0.3830.3144 125.312
GTWR0.5820.5704 124.630

早高峰AT

客流量

OLS0.2780.2584 408.928
GWR0.4830.4254 351.119
GTWR0.6610.6514 341.820

晚高峰ET

客流量

OLS0.2270.2054 180.032
GWR0.4430.3814 123.650
GTWR0.6520.6434 101.400

晚高峰AT

客流量

OLS0.1410.1184 052.995
GWR0.3030.2254 027.834
GTWR0.4950.4814 014.657

Table 6

Results of OLS model"

变量人口密度公交站点密度路网密度

商务住宅

类POI

公司企业

类POI

科教文化服务类POI交通设施服务类POI

早高峰ET

客流量

系数-6.697-20.36727.1223.1730.0959.756-0.789
t-1.163-1.5681.2787.4181.2891.946-2.735
P0.2460.1180.2030.000**0.1990.053*0.007**

早高峰AT

客流量

系数-15.907-23.41226.9156.0650.241-2.928-1.428
t-1.698-1.1080.7808.7172.009-0.359-3.046
P0.0910.2690.4360.000**0.046*0.7200.003**

晚高峰ET

客流量

系数-8.168-6.14020.4243.2620.1302.251-0.848
t-1.352-0.4500.9177.2691.6770.428-2.804
P0.1780.6530.3600.000**0.0950.6690.005**

晚高峰AT

客流量

系数-2.527-6.83420.9501.5400.10010.656-0.572
t-0.533-0.6391.2004.3781.6542.584-2.411
P0.5940.5230.2310.000**0.0990.010**0.017*

Fig.6

Impact of business residential POIs on the spatial distribution of connection traffic"

Fig.7

Impact of transportation facility service POIs on the spatial distribution of connection traffic"

Fig.8

Impact scientific educational cultural service of POIs on the spatial distribution of connection traffic"

Fig.9

Impact of corporate enterprise POIs on the spatial distribution of connection traffic"

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