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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (6): 1202-1209    DOI: 10.3785/j.issn.1008-973X.2020.06.018
Traf fic Engineering     
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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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     
Received: 12 September 2019      Published: 06 July 2020
CLC:  U 491  
Corresponding Authors: Yan-jie JI     E-mail: 230169206@seu.edu.cn;jiyanjie@seu.edu.cn
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Xin-wei MA
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Cite this article:

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.

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


租赁自行车用户出行特征及方式的影响因素分析

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 
类别 变量 变量内容 R/% 类别 变量 变量内容 R/%
因变量 租赁自行车
方式 		公共自行车1)
共享单车 		?
? 		主
观
感
知 		公共自行车2 h免费
使用时长 		较长1)
合理
较短 		63.9
14.3
21.8
个
人
属
性 		性别 男1)
女 		53.9
46.1 		租赁自行车用车方式 智能卡1)
手机APP 		27.4
72.6
年龄段 <21岁1)
21~40岁
41~60岁
>60岁 		14.5
56.2
16.4
12.9 		共享单车的骑行优惠活动
对用户产生吸引 		不赞成1)
中立
赞成 		23.4
53.7
22.9
居住区位 市中心1)
郊区 		47.3
52.7 		从众心理使您选择共享单车 不赞成1)
中立
赞成 		44.1
30.6
25.3
职业 学生1)
政府职员
企业职员
个体
其他 		22.9
12.4
45.2
10.8
7.7 		从众心理使您选择公共自行车 不赞成1)
中立
赞成 		52.1
22.3
25.6
Tab.A1 Definition and content of variables in binary Logistic regression model
续表 A1
类别 变量 变量内容 R/% 类别 变量 变量内容 R/%
注：1）为建模时的参考系变量
个
人
属
性 		月收入 <5 000元1)
5 000~10 000元
10 000~15 000元
15 000~20 000元
>20 000元 		23.3
41.2
15.3
13.0
7.2 		主
观
感
知 		共享单车乱停乱放现象 不严重1)
一般
严重 		13.8
62.5
23.7
教育背景 高中及以下1)
本科
硕士及以上 		38.3
46.5
15.2 		公共自行车调度问题 不严重1)
一般
严重 		23.5
48.3
28.2
是否拥有私人自行车 否1)
是 		28.4
71.6 		注册共享单车的复杂程度 不复杂1)
一般
复杂 		28.7
42.7
28.6
是否拥有电动自行车 否1)
是 		38.7
61.3 		注册公共自行车的复杂程度 不复杂1)
一般
复杂 		17.1
62.2
20.7
是否拥有小汽车 否1)
是 		24.3
75.7 		遇到损坏的共享单车的频率 从未1)
有时
经常 		19.8
40.4
39.8
? ?
? 		?
? 		遇到损坏的公共自行车的频率 从未1)
有时
经常 		67.2
18.3
14.5
Tab.A1 
Fig.1 Percentage and cumulative percentage distribution of travel distance for dockless and docked bike-sharing systems
Fig.2 Percentage and cumulative percentage distribution of travel time for dockless and docked bike-sharing systems
Fig.3 Percentage and cumulative percentage distribution of usage frequency for dockless and docked bike-sharing systems
Fig.4 Distribution of hourly usage volume for dockless and docked bike-sharing systems
类别 变量 变量内容 回归系数 ${R_{\rm{O}}}$ p
注：***表示p≤0.001, **表示p≤0.01, *表示p≤0.05
个
人
属
性 		年龄 > 60岁 ?4.450 0.012 0.000***
职业 企业职员 ?3.645 0.026 0.000***
月收入 > 20 000元 3.650 38.483 0.000***
拥有电动自行车 是 ?1.177 0.308 0.012*
主
观
感
知 		公共自行车2 h免费使用时长 合理 ?2.645 0.071 0.000***
用车方式 手机APP 1.559 4.752 0.001***
共享单车的骑行优惠活动对用户产生吸引 赞成有吸引力 2.878 17.775 0.000***
从众心理使您选择共享单车 赞成 2.072 7.937 0.000***
注册公共自行车的复杂程度 复杂 2.328 10.260 0.005**
遇到损坏的共享单车的频率 经常 ?2.318 0.098 0.000***
Tab.A2 Coefficient estimation results of binary logistic model
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