面向地铁站点的共享单车接驳出行特征及影响因素分析

doi:10.13229/j.cnki.jdxbgxb.20240009

摘要/Abstract

摘要：

为揭示共享单车接驳地铁出行客流量的时空特征，并分析建成环境因素对接驳需求的影响，本文考虑深圳地铁站出入口周边的建成环境，结合泰森多边形构建接驳出行的识别方法。该方法从时空角度挖掘工作日早、晚高峰进、出站4类接驳客流量的特征，利用时空地理加权回归模型分析4类建成环境因素对接驳需求的影响。结果表明：接驳客流量的分布具有空间异质性，早高峰的接驳需求高于晚高峰；商务住宅类和科教文化服务类兴趣点对城市核心区的接驳客流量的影响呈正相关，对城郊区域的影响呈负相关；然而，交通设施服务类兴趣点对接驳客流量的影响变化则相反；公司企业类兴趣点对城市西部区域接驳客流量的影响呈正相关，而对城市东部区域呈负相关。

关键词: 道路与交通工程, 共享单车, 地铁, 接驳客流量, 时空特征, 时空地理加权回归模型

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

中图分类号:

U491

马君泽,郑长江,吴非,汪妍妍,陆野,郑树康. 面向地铁站点的共享单车接驳出行特征及影响因素分析[J]. 吉林大学学报(工学版), 2025, 55(8): 2639-2650.

Jun-ze MA,Chang-jiang ZHENG,Fei WU,Yan-yan WANG,Ye LU,Shu-kang ZHENG. Analysis of characteristics and influencing factors of dockless bike sharing connected to metro[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2639-2650.

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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
POI	4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	OpenStreet-Map

变量		定义	单位	最小值	最大值	平均值	标准差
因变量	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	0	4 287	669.330	760.387
	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		0	9 831	840.418	1 279.973
	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		0	4 894	621.410	797.558
	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		0	3 687	540.992	593.324
自变量	人口密度	街道一级的人口密度	千人/km²	2.033	57.729	18.484	10.716
	公交站点密度	地铁站出入口2 km缓冲区内的密度	个/km²	0.955	28.250	16.535	5.493
	路网密度	地铁站出入口2 km缓冲区内的密度	km/km²	2.492	21.576	12.271	2.894
	商务住宅类POI	地铁站出入口2 km缓冲区内的POI数量	个	3	716	311.391	156.595
	公司企业类POI			48	4 343	1 585.13	871.910
	科教文化服务类POI			0	55	12.429	10.458
	交通设施服务类POI			20	1 650	654.548	402.327

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POI	2.541
公司企业类POI	2.340
科教文化服务类POI	1.557
交通设施服务类POI	7.616

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	0.707	0.617	0.782	0.704	0.705	0.745	0.835
预期指数	-0.004	-0.004	-0.004	-0.004	-0.004	-0.004	-0.004
方差	0.002	0.002	0.002	0.002	0.002	0.001	0.002
Z得分	18.226	15.879	20.094	18.116	18.160	19.364	21.448
P值	0.000	0.000	0.000	0.000	0.000	0.000	0.000

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	OLS	0.227	0.206	4 155.044
	GWR	0.383	0.314	4 125.312
	GTWR	0.582	0.570	4 124.630
早高峰AT 客流量	OLS	0.278	0.258	4 408.928
	GWR	0.483	0.425	4 351.119
	GTWR	0.661	0.651	4 341.820
晚高峰ET 客流量	OLS	0.227	0.205	4 180.032
	GWR	0.443	0.381	4 123.650
	GTWR	0.652	0.643	4 101.400
晚高峰AT 客流量	OLS	0.141	0.118	4 052.995
	GWR	0.303	0.225	4 027.834
	GTWR	0.495	0.481	4 014.657