租赁自行车用户出行特征及方式的影响因素分析

doi:10.3785/j.issn.1008-973X.2020.06.018

浙江大学学报(工学版)

2020, Vol. 54

Issue (6): 1202-1209 DOI: 10.3785/j.issn.1008-973X.2020.06.018

交通运输

租赁自行车用户出行特征及方式的影响因素分析

马新卫1(

),季彦婕1,*(

),金雪1,徐洋2,曹睿明3

1. 东南大学交通学院，江苏南京 211189
2. 中国科学院地理科学与资源研究所，资源与环境信息系统国家重点实验室，北京 100101
3. 东南大学建筑设计研究院有限公司，江苏南京 210096

Analysis on travel characteristics of bike-sharing users and influence factors on way to travel

Xin-wei MA1(

),Yan-jie JI1,*(

),Xue JIN1,Yang XU2,Rui-ming CAO3

1. School of Transportation, Southeast University, Nanjing 211189, China
2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China
3. Architects and Engineers Co. LTD of Southeast University, Nanjing 210096, China

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摘要：

针对我国市面上2种主流租赁自行车（公共自行车与共享单车），以南京市为例，基于共享单车骑行数据、公共自行车智能卡数据和租赁自行车用户问卷调查数据，对比2种租赁自行车用户在出行特征及其影响因素方面的差异. 从骑行距离、车辆使用频率与时间分布等方面揭示租赁自行车用户的出行特征差异；构建二元Logistic模型，从用户个人属性和主观感知2个层面探究影响租赁自行车用户出行方式选择的显著性因素. 结果表明：相较于公共自行车，共享单车的平均骑行距离和骑行时间更短，但小时使用量更高；2种租赁自行车在工作日均呈现出明显的早晚高峰时段，且使用量均远高于周末. 退休人员、企业职员和电动自行车拥有者更倾向于使用公共自行车；高收入群体、对互联网技术以及在线支付服务高度敏感的人则更倾向于使用共享单车.

关键词： 共享单车; 公共自行车; 出行特征; 二元Logistic模型

Abstract:

The bike-sharing systems operated in China can be divided into two categories: docked bike-sharing and dockless bike-sharing. The travel patterns and its determinants of docked and dockless bike-sharing users was compared by using the multi-source data, including trip data of a dockless bike-sharing scheme, smart card data of a docked bike-sharing scheme, and survey data of bike-sharing users in Nanjing. Firstly, the difference in travel characteristics of docked and dockless bike-sharing users were compared, such as travel distance, usage frequency and temporal travel patterns. Secondly, the binary Logistic regression model was built to explore the significant factors that influenced the choice of way to travel from two aspects: the user's personal attribute and subjective perception. Results show that dockless bike-sharing systems have shorter average travel distance and travel time but higher hourly usage volume, compared to docked bike-sharing systems. Trips of docked and dockless bike-sharing generated on workdays are more frequent than those on weekends, especially during the morning and evening rush hours. As to the factors that influence users ’ choice on way to travel, results show that retirees, enterprise staff and users with E-bikes are less likely to use docked sharing-bikes than dockless sharing-bikes; both high-income travelers and people who are highly sensitive to discounts, internet technology and online payment service are more likely to use the dockless bike-sharing.

Key words: dockless bike-sharing docked bike-sharing travel pattern binary Logistic regression

收稿日期: 2019-09-12 出版日期: 2020-07-06

CLC:

U 491

基金资助: 国家重点研发计划资助项目（2018YFB1600900）；东南大学优秀博士学位论文培育基金资助项目（YBJJ1842）

通讯作者: 季彦婕 E-mail: 230169206@seu.edu.cn;jiyanjie@seu.edu.cn

作者简介: 马新卫（1989—），男，博士生，从事交通大数据挖掘与建模研究. orcid.org/0000-0003-4419-0187. E-mail： 230169206@seu.edu.cn

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引用本文:

马新卫,季彦婕,金雪,徐洋,曹睿明. 租赁自行车用户出行特征及方式的影响因素分析[J]. 浙江大学学报(工学版), 2020, 54(6): 1202-1209.

Xin-wei MA,Yan-jie JI,Xue JIN,Yang XU,Rui-ming CAO. Analysis on travel characteristics of bike-sharing users and influence factors on way to travel. Journal of ZheJiang University (Engineering Science), 2020, 54(6): 1202-1209.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.06.018 或 http://www.zjujournals.com/eng/CN/Y2020/V54/I6/1202

表 A1 二元logistic模型中变量的定义与变量内容

表 A1

图 1 共享单车和公共自行车的骑行距离百分比与累积百分比分布图

图 2 共享单车和公共自行车的骑行时间百分比与累积百分比分布图

图 3 共享单车和公共自行车的车辆使用频率百分比与累积百分比分布图

图 4 共享单车与公共自行车的小时使用量分布图

表 A2 二元logistic模型参数估计结果

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