Elsevier

Journal of Transport & Health

Volume 10, September 2018, Pages 56-73
Journal of Transport & Health

The public bicycle-sharing scheme in Brisbane, Australia: Evaluating the influence of its introduction on changes in time spent cycling amongst a middle- and older-age population

https://doi.org/10.1016/j.jth.2018.07.003Get rights and content

Highlights

  • We assessed of the impacts of a bicycle-sharing scheme (BSS) on cycling behaviour.

  • We analysed a large cohort of baby boomers before and after the BSS introduction.

  • Residential proximity to the BSS did not predict its use.

  • Residential proximity did not predict a change in time spent cycling.

Abstract

Background

Active travel may improve individual health as it contributes to higher levels of physical activity, particularly in an aging society. Bicycle-sharing schemes may contribute to public health by encouraging active travel.

Aim

To investigate whether exposure to a bicycle-sharing scheme—measured as residential proximity to a bicycle station—was associated with the propensity to use it. Second, we aimed to study the extent to which exposure to the scheme was associated with a change in time spent cycling.

Method

In this natural-experimental study, we analysed a large panel of residents in Brisbane, Australia, who were surveyed before and after the introduction of a bicycle-sharing scheme in 2010. Data were collected as part of the HABITAT study, a multilevel longitudinal investigation of physical activity and health among ‘baby boomers’ (persons aged 40–65). Data were collected in 2009 (n = 7866), 2011 (n = 6900), and 2013 (n = 6520). Two self-reported outcome variables were examined: (1) a stages-of-change variable measuring the likelihood of using the scheme and the intention to use it in the future, and (2) change in time spent cycling between 2009 and 2013.

Results

In the unadjusted model, proximity was significantly associated with stages of change, but became non-significant after adjustment. Moreover, higher levels of exposure to the intervention did not predict a change in time spent cycling. Younger respondents and respondents with a higher education level were more likely to consider using the bicycle-sharing scheme. Individuals who had a college degree were more likely to have used this scheme.

Conclusion

Residential proximity to a bicycle-sharing station was not found to be associated with the use of the bicycle-sharing scheme nor did its introduction significantly predict an increase in time spent cycling. Other interventions may be more supportive of increasing cycling in the baby boomer cohort, and, thereby, improving their overall health.

Introduction

Physical inactivity is a major cause of morbidity and mortality (Lee et al., 2012). The World Health Organization (WHO) recommends spending at least 150 min of moderate-intensity aerobic activity, or at least 75 min of vigorous-intensity aerobic activity, or an equivalent combination a week (WHO, 2010). Older adults in particular do not achieve this recommended level of physical activity (Taylor, 2014; Sun et al., 2013), even though physical activity has been shown to result in improved health in older age groups (Wen et al., 2011; Landi et al., 2004; Guell et al., 2016). Increases in active travel time are associated with increases in total physical activity (Sahlqvist et al., 2013; Foley et al., 2016), and offer levels sufficient to improve individual health (Chief Medical Officers, 2011). Therefore, encouraging active travel amongst an aging population may result in improved individual and public health.

Bicycle-sharing schemes (BSS) may contribute to public health by encouraging acStive travel. Over the last 15 years, BSS have been launched in more than 800 cities, including many ‘world cities’ such as London, Paris, and New York. For the purposes of this study, we define BSS as schemes that provide time-restricted rental of bicycles to anyone, which sometimes require registration or subscription. The limited research on the health impacts of BSS concluded that the benefits of the schemes are indeed greater than the risks to health for most users (Woodcock et al., 2014; Rojas-Rueda, 2011). The contribution of BSS to public health depends, amongst other things, on changes in travel behaviour. In this respect, both the level of use of the scheme as well as the extent to which public bicycle schemes generate new trips or substitute another mode of transport are important, as physical activity benefits are achieved by an increase in time spent cycling, either from new trips or a change in the mode choice of existing trips.

In addition to health effects modelling, research on BSS is diverse. One strand focusses on the technical aspects, such as the optimal location for stations and the optimisation of continuous bicycle distribution over the city (e.g. Ahillen et al., 2016; Benarbia et al., 2013; Kadri et al., 2015). A second focus has been on the economic modelling of bicycle schemes, such as the cost effectiveness and willingness to pay (e.g. Wuerzer and Mason, 2016; Dell'Olio et al., 2011). The main research focus has been on the probability of using a station and the characteristics of individuals who use these schemes (e.g. Wang et al., 2016, Clark and Curl, 2016; El-Assi et al., 2017; Medard de Chardon and Caruso, 2015; Bernatchez et al., 2015; Fishman et al., 2014a, Fishman et al., 2014b). These studies indicate that the proximity of residential housing, train stations, shops, or employment sites to a docking station increases ridership (e.g. Fishman et al., 2015, Fishman et al., 2014a; Bachand-Marleau et al., 2012; Buck and Buehler, 2012, Daddio, 2012; Wang et al., 2016; Rixey, 2013; Nair et al., 2013; Hampshire and Marla, 2012; Fuller et al., 2011). BSS stations located in the city centre and on the university campus generally have high ridership (Mattson and Godavarthy, 2017, Zhang et al., 2016). Docking station density and population size are positively associated with the use of BSS (Médard de Chardon et al., 2017). The presence of a helmet law was associated with lower levels of use (Médard de Chardon et al., 2017). Several socio-economic characteristics are also associated with higher levels of membership and use: users appear to be younger adults, have higher incomes than average, male and are more likely to own a bicycle (Fishman et al., 2015, Fishman et al., 2013, Ji et al., 2017). Ogilvie and Goodman (2012) reported that registered users of the London scheme were more likely to be male and living in socioeconomically advantaged areas and areas with high cycling levels. However, amongst registered users, individuals living in more deprived areas made more trips than individuals in less deprived areas.

These studies provide useful insights about the characteristics of the users of bicycle-sharing schemes, and show, to a certain extent, the determinants of use (e.g. Fuller et al., 2011; Fishman et al., 2014a; Fishman et al., 2015). They also suggest that bicycle-sharing schemes appear to have the potential to alter travel behaviour away from the car towards active travel (Fishman et al., 2014b). However, most existing studies share two limitations. First, the majority of studies only collect data from users/members (e.g. Ogilvie and Goodman, 2012). Although user data allows us to determine user profiles, it does not enable us to investigate the correlates of usage or predictors of changes in travel behaviour on a population level (i.e. including non-users). Moreover, study findings involving only users are subject to self-selection bias (i.e. individuals who prefer cycling become a member of a scheme). Second, the majority of the studies on bicycle-sharing schemes rely on cross-sectional data (i.e. collected at one moment in time) (e.g. Fuller et al., 2011; Fishman et al., 2014a). The nature of cross-sectional data (irrespective of the collection from users and/or non-users) prevents causal inference of the bicycle-sharing scheme. As a result, changes in behaviour cannot be attributed to the introduction of such schemes.

The aim of this quasi-experimental study was twofold. First, we investigated whether exposure to a bicycle-sharing scheme—measured as residential proximity to a bicycle station—was associated with the propensity to use this scheme amongst a middle- and older-age population. We used a stages-of-change model to differentiate between (1) individuals who had never used the BSS and who did not intend to use it in the future, (2) individuals who had never used the BSS, but who intended to use the scheme in the future, and (3) individuals who had used the scheme. Second, this study investigated the extent to which exposure to this bicycle-sharing scheme has influenced individual travel behaviour amongst a middle- and older-age population, particularly whether its introduction was associated with changes in time spent cycling. We used residential proximity as our exposure measure, as the most frequently used BSS station is the one closest to home (Shaheen et al., 2011). It is therefore conceivable that the likelihood of using the BSS or changing one's travel behaviour may be influenced by residential proximity to a BSS station.

We analysed data from a large panel of residents in Brisbane, Australia, followed before and after the introduction of a large-scale BSS in 2010. The cohort consisted of adults aged between 40 and 65 years at baseline (2007). Whereas older individuals are less likely to cycle (e.g. Heinen et al., 2010), the benefits of cycling for older individuals are much greater than for younger individuals (Woodcock et al., 2014). Thus, it is important to understand the determinants of use and predictors of change in the active travel behaviour of this population.

Section snippets

Setting

Brisbane is the capital city of Queensland, Australia, and had over two million inhabitants in 2016. It is a rapidly growing city: its population increased by about 10% between 2011 and 2016 (Australian Bureau of Statistics, 2016). Of its commuting population, 75.3% travel to work by car as a driver, 10.5% commute by public transport, and 4.9% commute by active transport (Australian Bureau of Statistics, 2016).

Cycling infrastructure was limited, but has expanded in Brisbane over the past

Analyses

In this paper we perform two analyses:

Analysis 1: the use of CityCycle

The first analysis addresses the likelihood

Descriptive analyses

Of the 4637 respondents included in analysis 1, 4279 (92.3%) reported not having used CityCycle and not intending to use it in the future (i.e. Pre-Contemplation) in 2011. Four hundred five respondents (6.9%) belonged to the Contemplation and Preparation group (i.e. not having used the scheme, but planning to use it in the future). A small proportion of our respondents (n = 40, 0.98%) belonged to the Action and Maintenance group (i.e. individuals who had used the CityCycle).

Multivariate analyses

Residential

Discussion

Residential proximity to a bicycle-sharing station was not found to be associated with the use of CityCycle in Brisbane amongst a baby boomer cohort. Although individuals on higher stages of change based on the Prochaska and DiClemente model were on average living closer to bicycle stations, the association between proximity and the stages of change became non-significant after adjustment for socioeconomic and built environmental characteristics. Although non-significant, proximity had a

Conclusion

This study aimed to investigate the correlates of the use of a public bicycle scheme and to investigate the extent to which exposure to the introduced bicycle scheme has influenced individual travel behaviour, in particular whether it has increased the time spent cycling. For this, we analysed a large panel of residents in Brisbane, Australia between 2009 and 2013, Australia, followed before and after the introduction of a large-scale bicycle-sharing scheme in 2010. Our results indicate that

Acknowledgement

The HABITAT study is funded by the Australian National Health and Medical Research Council (NHMRC) (#497236, 339718 and 1047453). EH was funded by The Netherlands Organisation for Scientific Research, VENI-Grant (016.145.073).

Conflict of interest

The authors report that they have no conflicts of interest.

References (61)

  • K. Heesch et al.

    Cycling for transport and recreation: associations with socio-economic position, environmental perceptions, and psychological disposition

    Prev. Med.

    (2014)
  • K. Heesch et al.

    Cycling for transport and recreation: cross-sectional associations with the socio-economic, natural and built environment

    Health Place

    (2015)
  • Y. Ji et al.

    Public bicycle as a feeder mode to rail transit in China: the role of gender, age, income, trip purpose, and bicycle theft experience

    Int. J. Sustain. Transp.

    (2017)
  • I.M. Lee et al.

    Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy

    Lancet

    (2012)
  • E. Leslie et al.

    Walkability of local communities: using geographic information systems to objectively assess relevant environmental attributes

    Health Place.

    (2007)
  • J. Mattson et al.

    Bike share in Fargo, North Dakota: keys to success and factors affecting ridership

    Sustain. Cities Soc.

    (2017)
  • C. Médard de Chardon et al.

    Estimating bike-share trips using station level data

    Transp. Res. Part B: Methodol.

    (2015)
  • C. Médard de Chardon et al.

    Bicycle sharing system ‘success' determinants

    Transp. Res. Part A: Policy Pract.

    (2017)
  • M. Ricci

    Bike sharing: a review of evidence on impacts and processes of implementation and operation

    Res. Transp. Bus. Manag.

    (2015)
  • J. Panter et al.

    Impact of new transport infrastructure on walking, cycling, and physical activity

    Am. J. Prev. Med.

    (2016)
  • C. Perchoux et al.

    Conceptualisation and measurement of environmental exposure in epidemiology: accounting for activity space related to daily mobility

    Health Place

    (2013)
  • C.P. Wen et al.

    Minimum amount of physical activity for reduced mortality and extended life expectancy: a prospective cohort study

    Lancet

    (2011)
  • T. Wuerzer et al.

    Retail gravitation and economic impact: a market-driven analytical framework for bike-share station location analysis in the United States

    Int. J. Sustain. Transp.

    (2016)
  • J. Adams et al.

    Why don't stage-based activity promotion interventions work?

    Health Educ. Res.

    (2005)
  • Australian Bureau of Statistics, 2016. Census. via:...
  • J. Bachand-Marleau et al.

    Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use

    Transp. Res. Rec.: J. Transp. Res. Board

    (2012)
  • Benarbia, T., Labadi, K., Omari, A., Barbot, J.P., 2013. Balancing dynamic bike-sharing systems: A Petri nets with...
  • Brisbane City Council, 2016. via:...
  • D. Buck et al.

    Are bikeshare users different from regular cyclists? First look at short-term users, annual members, and area cyclists in the Washington, D.C., Region

    Transp. Res. Rec.: J. Transp. Res. Board

    (2013)
  • N.W. Burton et al.

    HABITAT: a longitudinal multilevel study of physical activity change in mid-aged adults

    BMC Public Health

    (2009)
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