Effects of new dock-less bicycle-sharing programs on cycling: a retrospective study in Shanghai

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Objectives: To examine (1) the effect of new dock-less bicycle-sharing programmes on change in travel mode and (2) the correlates of change in travel mode. Design: A retrospective natural experimental study. Setting 12 neighbourhoods in Shanghai.
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  1 Jia Y, et al  . BMJ Open   2019; 9 :e024280. doi:10.1136/bmjopen-2018-024280 Open access Effects of new dock-less bicycle-sharing programs on cycling: a retrospective study in Shanghai Yingnan Jia, 1,2  Ding Ding, 3,4  Klaus Gebel, 3,5,6  Lili Chen, 1,2  Sen Zhang, 1,2  Zhicong Ma, 1,2  Hua Fu 1,2 To cite: Jia Y, Ding D, Gebel K, et al  . Effects of new dock-less bicycle-sharing programs on cycling: a retrospective study in Shanghai.  BMJ Open   2019; 9 :e024280. doi:10.1136/ bmjopen-2018-024280   ► Prepublication history and additional material for this paper are available online. To view these files, please visit the journal online (http:// dx. doi. org/ 10. 1136/ bmjopen- 2018- 024280). DD and HF contributed equally.Received 23 May 2018Revised 13 October 2018 Accepted 29 October 2018For numbered affiliations see end of article. Correspondence to Professor Hua Fu; hfu@ fudan. edu. cn and Dr Ding Ding; melody. ding@ sydney. edu. au Research © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. ABSTRACT Objectives  To examine (1) the effect of new dock-less bicycle-sharing programmes on change in travel mode and (2) the correlates of change in travel mode. Design  A retrospective natural experimental study. Setting  12 neighbourhoods in Shanghai. Participants  1265 respondents were recruited for a retrospective study in May 2017. Main outcome measures  Prevalence of cycling before and after launch of dock-less bicycle-sharing programme. Results  The proportion of participants cycling for transport increased from 33.3% prior to the launch of the bicycle-sharing programmes to 48.3% 1 year after the launch (p<0.001). Being in the age group of 30–49 years (OR 2.28; 95% CI 1.30 to 4.00), living within the inner ring of the city (OR 2.27; 95% CI 1.22 to 4.26), having dedicated bicycle lanes (OR 1.37, 95% CI 1.12 to 1.68) and perceiving riding shared bicycles as fashionable (OR 1.46, 95% CI 1.21 to 1.76) were positively associated with adopting cycling for transport. Access to a public transportation stop/station (OR 0.82, 95% CI 0.67 to 0.99) was inversely correlated with adopting cycling for transport. Conclusions  Dock-less bicycle sharing may promote bicycle use in a metropolitan setting. Findings from this study also highlight the importance of cycling-friendly built environments and cultural norms as facilitators of adopting cycling. INTRODUCTION Regular physical activity (PA) reduces the risk of major chronic diseases and premature mortality. 1  However, around the world, large proportions of the population are not suffi-ciently active or completely inactive which has significant health and economic conse-quences. 2–5  Active transportation by cycling has the potential to contribute consider-ably to overall activity levels of adults and is associated with significant health bene-fits. 6–11  Moreover, greater use of bicycles for day-to-day travel provides wider benefits, including reductions in carbon emissions, air pollution and traffic congestion. 10 12  In Chinese cities, cycling used to be a conven-tional mode of travel for most people, to the point that the country was once referred to as the ‘Kingdom of Bicycles’. 12  However, since the turn of the century, Chinese cities have become increasingly cycling-unfriendly due to increasing car ownership and car-oriented urban planning policies such as the conver-sion of non-motorised to motorised lanes and banning non-motorised vehicles from arterial roads in some cities. 13  With the economic development and booming car industry, between 2002 and 2010–2012, the propor-tion of people using motorised transport as the main mode of transportation increased from 33.5% to 61.9%, while the proportion travelling by bicycle and walking decreased from 35.8% and 30.7% to 15.6% and 22.5%, respectively. 14   As a strategy for promoting cycling and sustainable transportation overall, public bicy-cle-sharing programmes (PBSPs) have been introduced in many cities around the world to provide bicycles as a mode of transporta-tion for relatively short trips and to bridge ‘the last mile’ of public transport services. 15 16  These PBSPs usually have docking stations  where users obtain and return the rental bicycles. 17  Although some studies have shown that cycling has increased in some cities Strengths and limitations of this study   ►  An ecological framework can guide inquiry into a more comprehensive understanding of the factors that influence cycling behaviours.   ► This study is the first to quantitatively evaluate whether the introduction of dock-less bicycle-shar-ing programmes leads to more cycling.   ►  All measures were based on self-reports.   ► This study applied a retrospective design, due to practical reasons outlined earlier. This limits causal inference from the current study.   ► It could not verify whether the significant change from inactive transport modes to cycling has in-creased physical activity at the population level.   on1 1 F  e b r  u ar  y 2  0 1  9  b  y  g u e s  t  .P r  o t   e c  t   e d  b  y  c  o p y r i   gh  t  .h  t   t   p:  /   /   b m j   o p en. b m j  . c  om /  B M J  O p en: f  i  r  s  t   p u b l  i   s h  e d  a s 1  0 .1 1  3  6  /   b m j   o p en-2  0 1  8 - 0 2 4 2  8  0  on 9 F  e b r  u ar  y 2  0 1  9 .D  ownl   o a d  e d f  r  om   2 Jia Y, et al  . BMJ Open   2019; 9 :e024280. doi:10.1136/bmjopen-2018-024280 Open access since the introduction of PBSPs, such as Washington, DC, Dublin, Beijing and Hangzhou, China, there are still some common problems and challenges for conventional PBSPs, which may have limited their reach and usage at a population level, such as reliance on docking stations, inconvenience in payment and insufficient supply of shared bicycles. 15 18–20  With the increasing popularity of smart phone payments, global positioning system (GPS) tracking and other technology, dock-less bicycle sharing provides new opportunities for promoting active travel and PA. 21  Dock-less PBSPs use mobile-controlled wheel lock and GPS tracking, so that users can locate the nearest bicycle, unlock and lock the bicycle, and pay (usually, around ¥1–US$0.15 per half hour) through a mobile app. Moreover, some of the shared bicycles (eg, Mobike) have solid tires,  which are durable and low maintenance. Dock-less PBSPs are currently deployed in many cities in China such as Shanghai, Beijing and Guangzhou. 22  As of May 2017, a total of 10 million dock-less shared bicycles had been deployed in China, 1.5 million of which in Shanghai,  which even led the government to ban additional shared bicycles. 23 24  Despite the rapid growth in dock-less PBSPs, there is very limited evidence on whether dock-less PBSPs can change travel modes at the population level. 25 Furthermore, the introduction of bicycle-sharing schemes alone may not lead to population-level uptake, as various other factors may need to be present to facili-tate population-level cycling. In line with socioecological models, previous research suggests that population-level cycling behaviour is associated with a range of individ-ual-level and environmental-level characteristics. 26–29  However, these socioecological correlates have rarely been examined in evaluations of PBSPs and remain important research gaps.Therefore, this study aims to (1) evaluate whether the introduction of dock-less PBSPs leads to more cycling and (2) to examine correlates of initiation of cycling, including sociodemographic characteristics and aspects of the built and social environment. MATERIALS AND METHODSPatient and public involvement  A retrospective study was conducted in May 2017. An inter-cept convenience sample survey was conducted among residents from 12 neighbourhoods. On approaching potential participants, information about the study was provided and written informed consent was obtained before participating in the study. Participants have the right to find out the results of the study by contacting the member of the project. Intervention Dock-less bicycle-sharing systems can be considered as a city-level intervention for travel mode. The system was officially launched in Shanghai in April 2016. By July 2017, there were more than 13 million registered users and more than 1 million dock-less shared bicycles in Shanghai. 30  The development of dock-less shared bicy-cles was so rapid in China that it limited opportunities for prospective data collection or inclusion of a control city that is comparable to Shanghai, but without a bicy-cle-sharing system. Therefore, a retrospective study design  was used. Study areas and recruitment of participants To explore the correlates of travel mode, a two-stage sampling method was employed. First, based on the Shanghai Transportation Map, the city was divided into four areas: within the inner ring, between the inner and middle rings, between the middle and outer rings, and beyond the outer ring. Then, three neighbourhoods  were selected in each of the four areas of Shanghai by purposive sampling. The selection criteria for neighbour-hoods were as follows: (1) within 1–2 km distance from the nearest subway station; (2) the number of residents  within the neighbourhood was more than 1000. Within each selected neighbourhood, trained interviewers conducted at least 100 self-administered intercept surveys in May 2017. The inclusion criteria for participants were (1) being 18–70 years old; (2) having lived in the selected neighbourhood for more than 3 months and (3) being physically capable of riding a bicycle. Altogether, 1265 respondents were sampled from 12 neighbourhoods.  After excluding 100 respondents with more than 20% missing data, 1165 respondents (92.1%) remained in the analysis. Measurements Travel mode Travel mode before and after the advent of the dock-less PBSPs was assessed by asking respondents two questions: (1) How did you travel most of the time before the advent of dock-less PBSPs? (2) How have you been travelling most of the time after the advent of dock-less PBSPs? Respon-dents selected one of the following options, including  walking, cycling, by car, public transport (subway, bus, ferry and shuttle bus), motorcycles/electric motorcy-cles, combined public transport with walking (>500 m), combined public transport with cycling, do not travel (staying at home) and other. According to respondents’ travel mode before and after the advent of dock-less PBSPs, they were classified into cyclists and non-cyclists at both time points. Cyclists were defined as participants  who travelled by bicycle or those who combined cycling and public transport most of the time. Perceived bikeability To date, only few instruments have been developed to measure perceived bicycle-friendliness of neighbourhood environments and most of these were developed for the physical environments of Western countries. 31  A new scale for measuring Chinese neighbourhood bikeability  was developed based on existing instruments, literature reviews, field visits and expert consultation. Specifically,  on1 1 F  e b r  u ar  y 2  0 1  9  b  y  g u e s  t  .P r  o t   e c  t   e d  b  y  c  o p y r i   gh  t  .h  t   t   p:  /   /   b m j   o p en. b m j  . c  om /  B M J  O p en: f  i  r  s  t   p u b l  i   s h  e d  a s 1  0 .1 1  3  6  /   b m j   o p en-2  0 1  8 - 0 2 4 2  8  0  on 9 F  e b r  u ar  y 2  0 1  9 .D  ownl   o a d  e d f  r  om   3 Jia Y, et al  . BMJ Open   2019; 9 :e024280. doi:10.1136/bmjopen-2018-024280 Open access  we adopted five questions (ie, distance to a public trans-portation stop/station, access to destinations, physical condition of bicycle lanes, maintenance of lanes and vege-tation/shade along the bicycle lanes) from the Chinese  Walkable Environment Scale for urban community resi-dents. 32  Based on consultation with several Chinese local PA experts to discuss potential correlates and determi-nants of cycling, we added four questions to the survey, including the presence of dedicated bicycle lanes, and the degree to which traffic violations, traffic volume and motorbikes/electric scooters impede cycling. Finally, this instrument was pilot tested and adjusted prior to the survey. All bikeability variables were on a 5-point scale and the composite score was analysed as a continuous  variable. More details about the questions are provided in online supplementary appendix 1. Social norms Two survey items assessed social norms: ‘Riding dock-less shared bicycles is fashionable’ and ‘Riding dock-less shared bicycles represents low socioeconomic status’. Each item was rated on a 5-point scale, from 1 (strongly disagree) to 5 (strongly agree). Demographic variables and other covariates Self-reported sociodemographic variables included gender, age, education, personal monthly income and marital status. Age was categorised as <30, 30–49 and ≥ 50 years. Educational attainment was categorised as ≤  junior high school, high school/technical secondary school, junior college, and university and higher. Monthly income was categorised as <¥2000, ¥2000–¥4999, ¥5000– ¥9999 and ≧  ¥10 000 (¥1=US$0.15 in May 2017). In addi-tion, questions about motor vehicle and bicycle ownership and characteristics of the commute were asked, including the following: (1) what is the distance between your home and work/college/university and (2) how long does it take you to go to work/college/university every day, both of which were converted to categorical variables. Statistical analysis McNemar’s test was used to examine the change in travel mode after the introduction of the dock-less PBSPs. To explore the potential correlates of change in travel mode, we focused on the participants who did not cycle before the bicycle sharing became available and classi-fied them as those who (1) changed from not cycling to cycling and (2) remained not cycling as their travel mode. More details can be found in figure 1. Because the data  were hierarchical in nature (individuals clustered within neighbourhoods), we explored multilevel modelling. However, on examination of the outcome variable, we decided against multilevel modelling because the intra-class correlation coefficient was 0.0645 and we only found a significant random effect in 1 out of 12 neighbour-hoods. Therefore, logistic regression was conducted to examine the association of sociodemographic variables, perceived bikeability and social norms with change in cycling behaviour. Sequential modelling was used with model 1 including only sociodemographic variables, model 2 including sociodemographic and bikeability vari-ables and model 3 additionally including social norms. Statistical analysis was performed using the SPSS V.20.0 (SPSS) and the significance level was set at 0.05. RESULTS The demographic characteristics of the study sample are reported in table 1. The final sample consisted of 1165 participants from 12 neighbourhoods. Nearly 40% of the participants were 30–49 years old, and over 75%  were married. More than 40% reported an income level between ¥2000 and ¥4999/month. Over 75% of the participants owned bicycles, while nearly half of the participants had motor vehicles. The average distance from work/college/university was 5.6 km, while the average commuting time was 26.6 min. Change in travel mode Before the launch of the dock-less PBSPs, 33.3% of the participants cycled for transport which increased Figure 1 Participants flow.   on1 1 F  e b r  u ar  y 2  0 1  9  b  y  g u e s  t  .P r  o t   e c  t   e d  b  y  c  o p y r i   gh  t  .h  t   t   p:  /   /   b m j   o p en. b m j  . c  om /  B M J  O p en: f  i  r  s  t   p u b l  i   s h  e d  a s 1  0 .1 1  3  6  /   b m j   o p en-2  0 1  8 - 0 2 4 2  8  0  on 9 F  e b r  u ar  y 2  0 1  9 .D  ownl   o a d  e d f  r  om   4 Jia Y, et al  . BMJ Open   2019; 9 :e024280. doi:10.1136/bmjopen-2018-024280 Open access significantly to 48.3% after the launch (p<0.001). Among the participants who usually travelled by car/motorcy-cles/electric motorcycles, walking/walking combined  with public transport and public transport before the launch of the dock-less PBSPs, there were 115 (28.4%), 50 (28.2%) and 28 (29.2%) participants who adopted cycling as their primary travel mode after the launch, respectively. Correlates of initiating commuting cycling  As shown in table 2, in model 1, among 645 participants  who did not report cycling commuting cycling at base-line, those who were <30 and 30–49 years old had more than twice the odds of adopting commuting cycling than participants who were 50 and older. Participants who lived within the inner ring had more than twice the odds to adopt cycling compared with those who lived in the area between the inner and middle rings. Participants living >5 km from work/college/university had more than twice the odds of initiating cycling compared with those living within 1.5 km from work/college/university. In model 2, presence of dedicated bicycle lanes was posi-tively associated with adopting cycling. Model 3 showed that participants who owned motor vehicles were more likely to adopt cycling than those without motor vehi-cles. In model 3, access to a public transportation stop/station was inversely associated with adopting cycling, and perceiving riding dock-less shared bicycles as fashion-able was positively correlated with adopting cycling. The perception that riding dock-less shared bicycles represents low socioeconomic status was inversely correlated with adopting cycling. DISCUSSION This is the first community-based study to evaluate the effect of new dock-less PBSPs on cycling for trans-port. Over the last 30 years, China has witnessed rapid economic development and a booming car industry and consequentially, a dramatic decrease in cycling. 12–14   With the introduction of dock-less PBSPs, we found that the proportion of participants that cycled for transport increased significantly from 33.3% to 48.3%.Nearly 30% of the participants who usually travelled by car/motorcycles/electric motorcycles adopted cycling after the launch of dock-less PBSPs. In comparison, a study that evaluated conventional PBSPs with docking stations showed that in Beijing, Shanghai and Hangzhou, 5.2%, 0.46% and 4% of car trips were replaced by bicycle. 33   Another study on members of bike-sharing programmes revealed that in Montreal, Toronto, Washington, DC, Minneapolis-Saint Paul, 40% of members reduced their number of car trips while only 0.4% of members increased their car trips. 34 35  Studies about PBSPs with docking stations in Barcelona, London, Montreal and  Washington, DC have all reported low transfer rates from car journeys to shared bicycles. 18 36  It appears that dock-less PBSPs might have the potential to be more effective and to have a wider reach in promoting cycling than conventional PBSPs. 20 37  However, it is important to take into account that the effect sizes are not comparable because our study used individual-level data and previous Table 1 Participant characteristics  Variablen (%) Gender Male587 (50.5) Female575 (49.5) Age, years 18–29297 (25.5) 30–49460 (39.5) ≥50408 (35.0)Education Junior high school289 (25.2) High school/technical secondary school339 (29.5) Junior college210 (18.3) University and above310 (27.0)Personal monthly income (¥) <2000203 (17.5) 2000–4999504 (43.4) 5000–9999329 (28.3) >10 000125 (10.8)Marital status Married891 (76.5) Unmarried/divorced/widowed274 (23.5) Area of residence Within the inner ring284 (24.4) Between the inner and middle rings265 (22.7) Between the middle and outer rings316 (27.1) Beyond the outer ring300 (25.8)Ownership of bicycle Yes879 (75.5) No286 (24.5)Ownership of motor vehicle Yes550 (47.2) No615 (52.8)Distance from work/college/university <1.5 km282 (25.0) 1.5–5 km432 (38.2) >5 km319 (28.2) Staying at home/not working97 (8.6)Commuting time (one way) <15 min359 (31.8) 15–30 min416 (36.8) >30 min257 (22.8) Staying at home/not working97 (8.6)   on1 1 F  e b r  u ar  y 2  0 1  9  b  y  g u e s  t  .P r  o t   e c  t   e d  b  y  c  o p y r i   gh  t  .h  t   t   p:  /   /   b m j   o p en. b m j  . c  om /  B M J  O p en: f  i  r  s  t   p u b l  i   s h  e d  a s 1  0 .1 1  3  6  /   b m j   o p en-2  0 1  8 - 0 2 4 2  8  0  on 9 F  e b r  u ar  y 2  0 1  9 .D  ownl   o a d  e d f  r  om   5 Jia Y, et al  . BMJ Open   2019; 9 :e024280. doi:10.1136/bmjopen-2018-024280 Open access Table 2 Predictors of adopting cycling Demographic characteristicsModel 1 (n=645)†Model 2 (n=641)‡Model 3 (n=641)§OR (95% CI)OR (95% CI)OR (95% CI) Gender Female (ref)1.001.001.00Male0.81 (0.56 to 1.16)0.73 (0.50 to 1.06)0.75 (0.51 to 1.11) Age (years) ≥50 (ref)1.001.001.00 30–492.26 (1.32 to 3.87)**2.31 (1.33 to 4.00)**2.28 (1.30 to 4.00)** <302.23 (1.18 to 4.21)*2.11 (1.10 to 4.07)*1.92 (0.99 to 3.74)Education University and above (ref)1.001.001.00 Junior college0.95 (0.57 to 1.59)0.91 (0.53 to 1.54)0.86 (0.50 to 1.48) High school/technical secondary school1.31 (0.79 to 2.17)1.30 (0.77 to 2.18)1.26 (0.74 to 2.13) Junior high school0.88 (0.45 to 1.72)0.83 (0.42 to 1.66)0.75 (0.38 to 1.52)Marital status Unmarried/divorced/widowed (ref)1.001.001.00 Married0.85 (0.53 to 1.37)0.85 (0.52 to 1.39)0.83 (0.50 to 1.37)Personal monthly income (¥) ≥10 000 (ref)1.001.001.00 5000–99991.26 (0.70 to 2.27)1.25 (0.68 to 2.30)1.29 (0.70 to 2.41) 2000–49991.45 (0.78 to 2.69)1.39 (0.74 to 2.64)1.43 (0.75 to 2.74) <20000.94 (0.41 to 2.15)0.86 (0.37 to 2.02)1.01 (0.42 to 2.41) Area Within the inner ring (ref)1.001.001.00 Between the inner and middle ring0.52 (0.29 to 0.93)*0.45 (0.25 to 0.84)*0.44 (0.24 to 0.82)** Between the middle and outer ring0.92 (0.56 to 1.51)0.78 (0.46 to 1.31)0.72 (0.43 to 1.23) Beyond the outer ring0.69 (0.42 to 1.15)0.59 (0.33 to 1.05)0.56 (0.31 to 1.01)Ownership of motor vehicle No (ref)1.001.001.00 Yes1.37 (0.95 to 1.98)1.45 (0.99 to 2.12)1.53 (1.04 to 2.25)*Ownership of bicycle No (ref)1.001.001.00 Yes0.85 (0.54 to 1.33)0.84 (0.53 to 1.35)0.92 (0.57 to 1.48)Distance from work/college/university ≤1.5 km (ref)1.001.001.00 1.5–5 km1.28 (0.73 to 2.24)1.27 (0.71 to 2.27)1.33 (0.73 to 2.39) >5 km2.04 (1.07 to 3.90)*2.22 (1.13 to 4.33)*2.58 (1.30 to 5.12)**Commuting time (one way) ≤15 min (ref)1.001.001.00 15–30 min0.96 (0.57 to 1.61)0.97 (0.57 to 1.65)0.93 (0.54 to 1.60) >30 min0.84 (0.45 to 1.58)0.91 (0.48 to 1.73)0.83 (0.43 to 1.62)Perceived bikeability Presence of dedicated bicycle lane1.38 (1.12 to 1.68)**1.37 (1.12 to 1.68)** Access to a public transportation stop/ station0.83 (0.68 to 1.01)0.82 (0.67 to 0.99)* Access to destinations0.85 (0.66 to 1.10)0.81 (0.63 to 1.06)Continued   on1 1 F  e b r  u ar  y 2  0 1  9  b  y  g u e s  t  .P r  o t   e c  t   e d  b  y  c  o p y r i   gh  t  .h  t   t   p:  /   /   b m j   o p en. b m j  . c  om /  B M J  O p en: f  i  r  s  t   p u b l  i   s h  e d  a s 1  0 .1 1  3  6  /   b m j   o p en-2  0 1  8 - 0 2 4 2  8  0  on 9 F  e b r  u ar  y 2  0 1  9 .D  ownl   o a d  e d f  r  om 
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