Exploring Dockless Bikeshare Usage: A Case Study of Beijing, China
Abstract
:1. Introduction
- To what extent do sociodemographics, social environment, travel attitudes and the built environment of residential neighborhoods influence individuals’ tendency to adopt dockless bikeshare systems?
- To what extent do sociodemographics, social environment, travel attitudes and the built environment of residential neighborhoods influence dockless bikeshare users’ frequency of using dockless shared bikes for the following four travel purposes: work or education commuting, sports and leisure, grocery shopping and recreational activities such as shopping, eating and drinking?
2. Materials and Methods
2.1. Context
2.2. Sampling and Data Collection
2.3. Methodology
2.4. Variables
3. Results
3.1. Descriptive Results
3.2. Model Results
3.2.1. Sociodemographics and Social Environment
3.2.2. Travel Attitude
3.2.3. Built Environment
4. Discussion and Conclusion
4.1. Dockless Bikeshare Users
4.2. Attitudinal and Environmental Correlates
4.3. Membership vs. Use Frequency
4.4. The Usage of Dockless Shared Bikes for Different Purposes
5. Limitations and Future Studies
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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%(N = 606) | Mean (Std. Dev.) | |
---|---|---|
Dockless bikeshare usage | ||
Users | 80.7% | |
Nonusers | 19.3% | |
Dockless bikeshare usage purposes among users | ||
Work or education commuting | 72.4% (N = 489) | |
Sports and leisure | 56.6% (N = 489) | |
Grocery shopping | 51.1% (N = 489) | |
Recreational activities (e.g., shopping, drinking, eating) | 53.8% (N = 489) | |
Gender | ||
Female | 51.0% | |
Male | 49.0% | |
Age (years) | ||
16–30 | 59.9% | |
31–45 | 32.7% | |
46–64 | 7.4% | |
Education | ||
High school/Secondary technical school and below | 5.0% | |
University/College Bachelors’ degree | 72.1% | |
Master’s degree and above | 22.9% | |
Household income | ||
Low income (less than 12,000 yuan) | 31.0% | |
Median income (12,000–20,000 yuan) | 36.0% | |
High income (more than 20,000 yuan) | 33.0% | |
Employment | ||
Full-time employment | 72.9% | |
Part-time employment, students, etc. | 27.1% | |
Housing situation | ||
Private purchase/self-built | 51.5% | |
Employers’ offer/student dormitory | 19.0% | |
Other | 29.5% | |
Car ownership | ||
No | 27.9% | |
Yes | 72.1% | |
Self-reported health | ||
Fair and below | 37.5% | |
Good | 34.7% | |
Very good and above | 27.9% | |
Spatial variables | ||
Accessibility to trip attractions | ||
Number of grocery stores in the neighborhood | 1.57 (3.140) | |
Number of bars in the neighborhood | 1.48 (5.991) | |
Number of restaurants in the neighborhood | 3.9 (8.982) | |
Distance to closest shopping mall (km) | 5.209 (7.729) | |
Distance to closest education facility (km) | 1.368 (1.688) | |
Number of education facilities in the neighborhood | 0.45 (1.489) | |
Distance to closest entertainment facility (km) | 5.021 (6.848) | |
Distance to closest sports facility (km) | 1.486 (2.486) | |
Distance to closest park (km) | 1.751 (2.201) | |
Design | ||
The length of all roads in the neighborhood (km) | 13.44 (6.469) | |
The length of bicycle roads in the neighborhood (km) | 3.391 (2.700) | |
The length of pedestrian-priority roads in the neighborhood (km) | 3.909 (3.159) | |
Distance to transit | ||
Distance to closest bus stop (km) | 0.332 (0.331) | |
Distance to closest subway stop (km) | 3.232 (7.575) |
Factors | Indicators | Loadings |
---|---|---|
Pro-car | I like driving | 0.715 |
Without a car, I cannot handle my daily life | 0.678 | |
Owning a car allows me to do more | 0.812 | |
Owning a car gives me freedom | 0.821 | |
I do not have any alternative for car use | 0.732 | |
A car gives me prestige and status | 0.618 | |
Pro-e-bikes/scooters | I like riding e-bikes | 0.891 |
If possible, I would rather use e-bikes than take public transportation | 0.911 | |
Riding e-bikes can sometimes be easier for me than other modes | 0.906 | |
I think that traveling by e-bike is safer than all other modes | 0.805 | |
Pro-public transportation | I like to use public transportation | 0.807 |
If possible, I would rather use public transportation than drive | 0.731 | |
Public transit can sometimes be easier for me than other modes | 0.784 | |
Public transportation is unreliable | −0.532 | |
Traveling by public transit is safer than other modes | 0.456 | |
Pro-bicycles | I like cycling | 0.834 |
If possible, I would rather cycle than take public transportation | 0.839 | |
Cycling can sometimes be easier for me than other modes | 0.843 | |
I think that traveling by bicycle is safer than all other modes | 0.726 | |
Pro-walking | I like walking | 0.782 |
If possible, I would rather walk than take public transportation | 0.791 | |
Walking can sometimes be easier for me than other modes | 0.788 | |
I think that traveling by foot is safer than all other modes | 0.661 | |
Pro-environment/health | I’m concerned about the environmental impacts of my daily travel | 0.770 |
I’m willing to change travel mode if it’s good for the environment | 0.795 | |
I’m concerned about the health impacts of my daily travel | 0.691 | |
The trip to/from work is a useful transition between home and work | 0.537 | |
Anti-public transportation | Transferring to other buses or metros is annoying | 0.664 |
It bothers me that public transportation is too crowded | 0.846 | |
Anti-travelling | Travel time is generally wasted time | 0.761 |
I prefer to organize my errands so that I make as few trips as possible | 0.712 |
Model 1 | |||
---|---|---|---|
Coefficient | Odds Ratio | ||
Variables | |||
(Intercept) | −5.244 | 0.005 *** | |
Age | −0.051 | 0.950 ** | |
Gender (Male) | −0.348 | 0.706 | |
Education (ref = high school equivalent and below) | |||
University/college Bachelors’ degree | 1.643 | 5.173 ** | |
Masters’ degree and above | 1.496 | 4.464 * | |
Household income (ref = low income) | |||
Median income | 0.738 | 2.092 * | |
High income | 0.386 | 1.471 | |
Employment (ref = part-time employment, students, etc.) | |||
Full-time employment | 1.278 | 3.590 *** | |
Living situation (ref = private purchase/self-built) | |||
Employers’ offer/student dormitory | 1.672 | 5.320 ** | |
Other | 0.976 | 2.653 ** | |
Car ownership (Yes) | 0.453 | 1.574 | |
Self-reported health (ref = fair and below) | |||
Good | 0.148 | 1.159 | |
Very good and above | 0.425 | 1.529 | |
Social environment | 1.169 | 3.218 *** | |
Travel attitude | |||
Pro-car | 0.156 | 1.168 | |
Pro-e-bikes/e-scooters | 0.059 | 1.060 | |
Pro-public transportation | 0.262 | 1.300 . | |
Pro-bicycles | 1.004 | 2.729 *** | |
Pro-walking | −0.293 | 0.746 * | |
Pro-environment/health | −0.126 | 0.882 | |
Anti-public transportation | 0.095 | 1.100 | |
Anti-travelling | 0.068 | 1.070 | |
Spatial variables | |||
Accessibility to trip attractions | |||
Number of bars in the neighborhood | 0.097 | 1.102 | |
Number of restaurants in the neighborhood | −0.042 | 0.959 * | |
Number of education facilities in the neighborhood | −0.186 | 0.831 * | |
Distance to closest sports facility (km) | 0.036 | 1.037 | |
Distance to closest park (km) | 0.233 | 1.263 * | |
Design | |||
The length of all roads in the neighborhood (km) | 0.105 | 1.111 ** | |
The length of bicycle roads in the neighborhood (km) | −0.064 | 0.938 | |
Distance to transit | |||
Distance to closest bus stop (km) | −0.923 | 0.397 * | |
Distance to closest subway stop (km) | −0.053 | 0.948 * | |
Model statistics | |||
Number of observations | 606 | ||
R square | McFadden | 0.381 | |
Nagelkerke | 0.499 | ||
ROC (area under the curve) | 0.897 |
Model 2.1: Work or Education Commuting | Model 2.2: Sports and Leisure | Model 2.3: Grocery Shopping | Model 2.4: Recreational Activities | |||||
---|---|---|---|---|---|---|---|---|
Count | Zero | Count | Zero | Count | Zero | Count | Zero | |
Variables | ||||||||
(Intercept) | 0.765 . | −1.227 | −1.198 * | −3.197 ** | 0.497 | −1.399 | 0.020 | −3.288 ** |
Age | −0.001 | −0.036 * | 0.002 | −0.017 | 0.001 | 0.000 | −0.011 | 0.013 |
Gender (Male) | 0.117 | −0.116 | 0.057 | 0.208 | −0.111 | −0.018 | 0.051 | 0.279 |
Education (ref = high school equivalent and below) | ||||||||
University/college Bachelors’ degree | −0.291 . | −0.348 | −0.124 | 1.338 * | 0.090 | 0.397 | −0.747 *** | 0.323 |
Masters’ degree and above | −0.528 ** | −0.385 | −0.411 | 1.103 . | −0.086 | 0.426 | −1.015 *** | −0.364 |
Household income (ref = low income) | ||||||||
Median income | −0.071 | 0.386 | −0.018 | 0.019 | 0.020 | 0.015 | −0.079 | −0.230 |
High income | 0.013 | 0.234 | 0.157 | −0.014 | 0.058 | 0.052 | 0.267 * | 0.124 |
Employment (ref = part-time employment, students, etc.) | ||||||||
Full-time employment | −0.235 * | 0.184 | −0.202 | 0.659 * | 0.281 | −0.357 | 0.461 * | −0.173 |
Living situation (ref = private purchase/self-built) | ||||||||
Employers’ offer/student dormitory | −0.174 | 0.127 | −0.019 | 0.714 . | 0.152 | −0.644 . | 0.261 | 0.439 |
Other | 0.141 | 0.059 | 0.197 | −0.194 | −0.157 | −0.124 | −0.031 | 0.014 |
Car ownership (yes) | 0.061 | 0.373 | 0.296 * | 0.523 * | 0.515 ** | 0.055 | −0.061 | −0.237 |
Self-reported health (ref = fair and below) | ||||||||
Good | 0.155 . | 0.198 | 0.260 * | 0.479 * | −0.186 | 0.137 | 0.087 | 0.009 |
Very good and above | 0.076 | 0.658 * | 0.030 | 0.066 | −0.228 | −0.007 | 0.069 | −0.316 |
Social environment | 0.302 *** | 0.805 *** | 0.444 *** | 0.531 ** | −0.057 | 0.286 | 0.257 * | 0.789 *** |
Travel attitude | ||||||||
Pro-car | −0.134 *** | −0.287 * | −0.022 | 0.099 | −0.125 . | 0.044 | 0.008 | 0.264 * |
Pro-e-bikes/e-scooters | −0.001 | 0.246 * | 0.065 | 0.001 | 0.031 | 0.080 | −0.016 | −0.006 |
Pro-public transportation | 0.020 | 0.228 . | −0.018 | −0.019 | 0.010 | 0.132 | 0.098 . | 0.072 |
Pro-bicycles | 0.120 * | 0.188 | 0.020 | 0.187 | 0.092 | 0.191 | 0.019 | 0.271 * |
Pro-walking | −0.040 | −0.184 | 0.093 . | −0.033 | 0.110 . | −0.142 | 0.065 | −0.027 |
Pro-environment/health | −0.031 | 0.221 . | −0.018 | 0.274 * | 0.067 | −0.065 | −0.001 | −0.107 |
Anti-public transportation | 0.062 . | −0.194 | 0.004 | 0.022 | −0.072 | 0.022 | 0.152 ** | 0.171 |
Anti-travelling | −0.062 . | −0.039 | −0.123 * | −0.262 * | −0.101 . | −0.255 * | −0.038 | −0.216 * |
Spatial variables | ||||||||
Accessibility to trip attractions | ||||||||
Number of grocery stores | 0.039 * | −0.015 | −0.010 | 0.062 | −0.035 | 0.057 | ||
Number of bars | −0.013 . | 0.007 | 0.006 | 0.025 | ||||
Number of restaurants | 0.002 | −0.030 | ||||||
Distance to closest shopping mall | −0.019 | −0.004 | −0.007 | −0.028 | ||||
Distance to closest education facility | 0.047 | 0.072 | ||||||
Distance to closest entertainment facility | 0.015 | 0.001 | 0.035 * | 0.017 | ||||
Distance to closest sports facility | 0.027 | 0.048 | ||||||
Distance to closest park | 0.032 | −0.047 | ||||||
Design | ||||||||
The length of all roads | −0.009 | −0.027 | 0.008 | −0.044 . | 0.005 | 0.027 | 0.024 . | 0.003 |
The length of bicycle roads | −0.020 | 0.109 . | −0.044 | −0.001 | 0.014 | −0.061 | 0.046 | −0.100 . |
The length of pedestrian-priority roads | 0.039 . | −0.046 | 0.018 | 0.063 | −0.032 | −0.002 | −0.062 * | 0.083 |
Distance to transit | ||||||||
Distance to closest bus stop | −0.129 | −0.059 | −0.168 | −0.486 | −0.220 | −0.180 | −0.256 | 0.103 |
Distance to closest subway stop | −0.016 * | −0.034 . | −0.026 . | −0.004 | 0.023 * | 0.019 | −0.013 | 0.009 |
Model statistics | ||||||||
Log(theta) | 1.553 *** | 2.346 *** | 2.59 *** | 3.827 * | ||||
N of cases | 489 | 489 | 489 | 489 | ||||
AIC | 2320.88 | 1644.77 | 1531.55 | 1520.02 |
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Chen, Z.; van Lierop, D.; Ettema, D. Exploring Dockless Bikeshare Usage: A Case Study of Beijing, China. Sustainability 2020, 12, 1238. https://doi.org/10.3390/su12031238
Chen Z, van Lierop D, Ettema D. Exploring Dockless Bikeshare Usage: A Case Study of Beijing, China. Sustainability. 2020; 12(3):1238. https://doi.org/10.3390/su12031238
Chicago/Turabian StyleChen, Zheyan, Dea van Lierop, and Dick Ettema. 2020. "Exploring Dockless Bikeshare Usage: A Case Study of Beijing, China" Sustainability 12, no. 3: 1238. https://doi.org/10.3390/su12031238