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J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

Influence of urban built environment on online car-hailing passenger flow

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城市建成环境对网约车客流的影响研究

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Sept.2023, Vol.46 No.9: 1247-1253

DOI: 10.3969/j.issn.1003-5060.2023.09.015

Authors

GENG Xuelin, XU Lunhui

(School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China)

Abstract and Keywords

Abstract: In this paper, the 65 km² area of Chengdu City is selected as the research area, and six kinds of points of interest (POI) and land use mix data representing the urban built environment are used to construct a set of built environment factors affecting online car-hailing passenger flow combined with online car-hailing orders data. Then, the geographically and temporally weighted regression (GTWR) model is built to explore the relationship between various factors and online car-hailing passenger flow. Compared with traditional ordinary least squares (OLS) and geographically weighted regression (GWR) models, GTWR model can better explain the influence of urban built environment factors on online car-hailing passenger flow. It can also quantitatively analyze and explain the spatiotemporal heterogeneity of urban built environment factors. The results show that online car-hailing passenger flow is mainly affected by shopping service, corporate business, and catering service, and the influence degree is not evenly distributed in time and space. Land use mix will inhibit online car-hailing travel, but the degree of inhibition is weak. The results can provide reference for the operation and management of online car-hailing.

Keywords: online car-hailing; passenger flow; urban built environment; spatiotemporal heterogeneity; geographically and temporally weighted regression(GTWR) model

作者信息

耿雪琳，许伦辉

（华南理工大学土木与交通学院，广东广州 510641）

摘要和关键词

摘要: 文章选取成都市内一个65 km²范围为研究区域,运用表征城市建成环境的6种兴趣点(point of interest,POI)和土地利用混合度数据,结合网约车订单数据,构建影响网约车客流的建成环境因素集,建立基于时空地理加权回归(geographically and temporally weighted regression,GTWR)模型的网约车客流影响模型,探究各因素与网约车客流之间的关系。相比于普通最小二乘(ordinary least squares,OLS)法和地理加权回归(geographically weighted regression,GWR)模型,采用GTWR模型能更好地解释城市建成环境因素对网约车客流的影响,并定量分析解释城市建成环境因素的时空异质性影响。研究结果表明:网约车客流主要受购物服务、公司企业、餐饮服务影响,且影响程度时空分布不均衡;土地利用混合度始终会抑制网约车的客流出行,但抑制程度较弱。研究结果可为网约车的运营管理提供参考。

关键词: 网约车；客流；城市建成环境；时空异质性；时空地理加权回归（GTWR）模型

Funding

基金项目：国家自然科学基金资助项目（61903145）；广东省科技创新战略专项资金（大学生科技创新培育）资助项目（pdjh2020a0030）

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