A Discriminant Model and Influencing Factors of Perceived Waiting Time of Online Car-hailing

Hu San-gen; Li Chun; Yang Ying; Wang Xiao-xia

doi:10.12052/gdutxb.220102

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

A Discriminant Model and Influencing Factors of Perceived Waiting Time of Online Car-hailing

更新时间：2026-05-27

- A Discriminant Model and Influencing Factors of Perceived Waiting Time of Online Car-hailing
- Vol. 40, Issue 3, Pages: 1-9(2023)
- 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：
- 基金信息：
- DOI：10.12052/gdutxb.220102
  CLC： U491
- Online First：25 May 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
胡三根, 李春, 杨莹, 王小霞. 网约车感知等待时间判别模型及影响因素分析[J]. 广东工业大学学报, 2023, 40(3): 1-9.

Hu San-gen, Li Chun, Yang Ying, et al. A Discriminant Model and Influencing Factors of Perceived Waiting Time of Online Car-hailing[J]. 2023, 40(3): 1-9.
胡三根, 李春, 杨莹, 王小霞. 网约车感知等待时间判别模型及影响因素分析[J]. 广东工业大学学报, 2023, 40(3): 1-9. DOI： 10.12052/gdutxb.220102.

Hu San-gen, Li Chun, Yang Ying, et al. A Discriminant Model and Influencing Factors of Perceived Waiting Time of Online Car-hailing[J]. 2023, 40(3): 1-9. DOI： 10.12052/gdutxb.220102.

摘要

作为社会出行的重要组成部分，大学生早已成为网约车行业的主要用户对象，探究大学生等候网约车到达时间的感知行为，对进一步推动网约车新业态规范发展是十分必要的。首先，在线调查492位大学生的候车行为和感知状态；然后，使用Cronbach’s

$ \alpha $

img text_id='Z-20230506171009' class='formula-img' style='display:none;' src='gdgydxxb-40-3-1_Z-20230506171009.jpg'>

和验证性因子分析检验问卷的信效度；最后，将感知时间划分为3个分类，采用判别分析方法，建立网约车感知等待时间判别模型，并对判别分类结果及其影响因素进行了实证分析。研究发现，个人特征、环境因素、候车行为以及移动设备四个因素所包含的35个变量都会影响感知等待时间；在同组分类函数中，候车行为对感知等待时间的影响最大，其次是环境因素，最低是移动设备。研究结果为探究影响大学生感知等待时间长度的具体因素及潜在心理提供了有效支撑，为有效提高网约车的服务水平和吸引力提供了一定的理论价值和现实指导意义。

Abstract

As an important part of social travel

college students have long become the main users of the online car-hailing industry. It is highly necessary to explore the perceived behavior of college students waiting for the arrival time of online car-hailing to promote the new standardized development of online car-hailing industry. Firstly

an online questionnaire was conducted among 492 college students about their waiting behavior and perceived state. Second

Cronbach's

$ \alpha $

img text_id='Z-20230506171016' class='formula-img' style='display:none;' src='gdgydxxb-40-3-1_Z-20230506171016.jpg'>

and confirmatory factor analysis were used to test the reliability and validity of the questionnaire. Finally

the perceived time is divided into three categories

and a discriminant model of the perceived waiting time of ride-hailing vehicles is established based on the discriminant analysis method. Classification results and influencing factors of the discriminant analysis are empirically analyzed. It is found that these 35 variables contained in the four factors of personal characteristics

environmental factors

waiting behavior and mobile devices will affect the perceived waiting time. In the same group of classification functions

waiting behavior has the greatest impact on perceived waiting time

followed by environmental factors

and the lowest is mobile devices. The research results provide effective support for exploring the specific factors and potential psychology affecting college students' perceived waiting time

and provide certain theoretical value and practical guidance for effectively improving the service level and attraction of online car-hailing.

关键词

Keywords

references

NI L L, WANG Z. Light-asset ride-sourcing market competitive network equilibrium [J]. Journal of Railway Science and Engineering, 2020, 17(9): 2404-2411.

CHEN X Q. Review of App-based ridesharing mobility research [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 77-90.

WANG Z, CHEN X, CHEN X M. Ridesplitting is shaping young people’s travel behavior: evidence from comparative survey via ride-sourcing platform [J]. Transportation Research Part D:Transport and Environment, 2019, 75: 57-71.

赵继文. 基于Logit模型的大学生网约车用户出行行为研究[D]. 大连: 大连交通大学, 2020.

HABIB K N, WEISS A, HASNINE S. On the heterogeneity and substitution patterns in mobility tool ownership choices of post-secondary students: the case of Toronto [J]. Transportation Research Part A: Policy and Practice, 2018, 116: 650-665.

LI J, DENG Y X, HUANG L H. Arrangement and evaluation of bus arrival timetable based on service reliability [J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2020, 59(5): 86-94.

OSUNA E E. The psychological cost of waiting [J]. Journal of Mathematical Psychology, 1985, 29(1): 82-105.

KATZ K, LARSON B, LARSON R. Prescription for the waiting-in-line blues entertain, enlighten, and engage [J]. Oper Manag Crit Perspect Bus Manag, 2003, 2: 160.

FENG S, WU H, SUN X, et al . Factors on perceived waiting time and implications on passengers’ satisfaction with waiting time [J ] . Promet-Traffic Transportation, 2016, 28(2): 155-163.

TYRINOPOULOS Y, ANTONIOU C. Public transit user satisfaction: variability and policy implications [J]. Transport Policy, 2008, 15(4): 260-272.

BRAKEWOOD C, BARBEAU S, WATKINS K. An experiment evaluating the impacts of real-time transit information on bus riders in Tampa, Florida [J]. Transportation Research Part A:Policy and Practice, 2014, 69: 409-422.

GOOZE A, WATKINS K E, BORNING A. Benefits of real-time transit information and impacts of data accuracy on rider experience [J]. Transportation Research Record, 2013, 2351(1): 95-103.

WATHINS K E, FERRIS B, BORNING A, et al . Where is my bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders [J ] . Transportation Research Part A:Policy and Practice, 2011, 45(8): 839-848.

LAGUNE-REUTLER M, GUTHRIE A, FAN Y, et al . Transit stop environments and waiting time perception: impacts of trees, traffic exposure, and polluted air [J ] . Transportation Research Record, 2016, 2543(1): 82-90.

JI Y, GAO L, FAN Y, et al . Waiting time perceptions at bus and metro stations in Nanjing, China: the importance of station amenities, trip contexts, and passenger characteristics [J ] . Transportation Letters, 2019, 11(9): 479-485.

MILLONIG A, SLESZYNSKI M, ULM M. Sitting, waiting, wishing: waiting time perception in public transport[C]//2012 15th International IEEE Conference on Intelligent Transportation Systems. Anchorage, AK:IEEE, 2012: 1852-1857.

SUN X L, FENG S M, WU H Y. Influence of stop environment and waiting behavior on perceived waiting time [J]. Journal of Harbin Institute of Technology, 2019, 51(2): 186-190.

FAN C M, LI H Q, JIA J M, et al . The relationships among waiting time, perceived economic loss and service Satisfaction: a study of taxi drivers’ waiting for gas[J ] Management Review, 2014, 26(11) : 99-105.

张文彤, 董伟. SPSS统计分析高级教程[M]. 北京: 高等教育出版社, 2018.

HAIR J F, BLACK W C, BABIN B J, et al. Multivariate data analysis: a global perspective [M].7th ed. Upper Saddle River: Pearson Education, 2010.

JIANG X H, SHEN Z Z, ZHANG N N, et al . Reliability and validity analysis of the questionnaire [J ] . Modern Preventive Medicine, 2010, 37(3): 429-431.

WANG P C, HSU Y T, HSU C W. Analysis of waiting time perception of bus passengers provided with mobile service [J]. Transportation Research Part A:Policy and Practice, 2021, 145: 319-336.

SUNG T K, CHANG N, LEE G. Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction [J]. Journal of Management Information Systems, 1999, 16(1): 63-85.

蔡建琼, 于慧芳, 朱志洪, 等. SPSS统计分析实例精选[M]. 北京: 中国人民大学出版社, 1999.

ZHANG C H, LI Y L, WU M. Empirical analysis and model study of personal credit scoring method based on discriminant analysis [J]. Journal of Dalian University of Technology (Social Science), 2009, 30(1): 6-10.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Study of the Factors Influencing the Degradation of Metoprolol by UV-LED Combined with Sodium Hypochlorite

An Analysis of the Dwelling Time at Bus Stop and Its Influencing Factors

Antimicrobial Activity of Tannic Acid in Vitro and Its Influencing Factors

Research on Spatial Distribution of Rural Co-operative Financing Housing and the Factors in Guangzhou：Based on GIS Spatial Analysis and SPSS Quantitative Statistics

The Cross-twin Physics-informed Neural Network for Wave Equations with Conserved Quantities

Related Author

Wang Si-si

Liu Li-fan

Li Shao-feng

Ran Zhi-lin

Hu San-gen

He Qi

Lin Rui-hao

Zheng Zheng-nan

Related Institution

Department of Building and Environmental Engineering, Shenzhen Polytechnic Institute

School of Traffic and Environment, Shenzhen Institute of Information Technology

AI问答

⁰