ABSTRACT
In this paper, we represent a systematic review of stated preference studies examining the extent to which cycle infrastructure preferences vary by gender and by age. A search of online, English-language academic and policy literature was followed by a three-stage screening process to identify relevant studies. We found 54 studies that investigated whether preferences for cycle infrastructure varied by gender and/or by age. Forty-four of these studies considered the extent of separation from motor traffic. The remainder of the studies covered diverse topics, including preferred winter maintenance methods and attitudes to cycle track lighting. We found that women reported stronger preferences than men for greater separation from motor traffic. There was weaker evidence of stronger preferences among older people. Differences in preferences were quantitative rather than qualitative; that is, preferences for separated infrastructure were stronger in some groups than in others, but no group preferred integration with motor traffic. Thus, in low-cycling countries seeking to increase cycling, this evidence suggests focusing on the stronger preferences of under-represented groups as a necessary element of universal design for cycling.
KEYWORDS: Cycling, gender, age, equity, systematic review
Introduction
Within countries with a low cycling mode share (approximately 5% mode share or less, herein referred to as low-cycling countries), cycling is demographically unequal, notably by gender and age (Pucher & Buehler, 2008). A policy concern to diversify cycling has been accompanied by a growth in academic literature on this issue. Aldred, woodcock and Goodman (2015) explored whether increasing cycle commuting (between 2001 and 2011) was associated with greater age and gender diversity in England and Wales. The results suggest that increased cycling in Inner London and some other, largely metropolitan, areas has not yet been associated with an increase in diversity.
Part of the reason for this lack of diversification may lie in a lack of change in existing cycling environments. Increasingly, authors examine the extent to which experience of active travel environments may vary between groups (Asadi-Shekari, Moeinaddini, & Zaly Shah, 2013; Habib, Mann, Mahmoud, & Weiss, 2014; Oxley, Corben, Charlton, Fildes, & Rothengatter, 2005). For example, an ageing population generates new design challenges for cycle infrastructure (Fietsberaad, 2007), while the engineering requirements of three-wheeled cycles (used to carry children or other cargo, or ridden by some disabled cyclists) differ from that of bicycles (Transport for London [TfL], 2014).
Understanding under-represented groups’ views on infrastructure may help realise policy goals to diversify cycling. Specifically, authors have suggested that people from demographic groups under-represented in lower cycling contexts show greater aversion to sharing with motor traffic than do younger people and men (Chataway, Kaplan, Nielsen, & Prato, 2014; Davies, Halliday, Mayes, & Pocock, 1997). If so, this could be part of the explanation for observed inequalities in cycling, especially higher cycling countries, with better cycling infrastructure, have much greater gender and age equity (Aldred et al., 2015).
To date, however, no systematic review has examined gender and age similarities and differences in preferences for different types of cycling environments. This review helps to fill that gap by systematically synthesising the evidence on what people say they would prefer if given a choice. It does not consider the evidence on what people actually choose in existing cycling environments, in which they may have few options. Its findings have policy implications for building infrastructure for cycling in low-cycling countries. They speak to an ongoing debate between those who suggest that building more infrastructure that existing cyclists find acceptable will increase and diversify cycling (Office for National Statistics [ONS], 2014) and those who argue that this approach will reinforce existing inequalities (Horton & Jones, 2015).
Review focus
The paper complements systematic reviews already published in the field of active transport, which focus on intervention research to promote cycling (Yang, Sahlqvist, McMinn, Griffin, & Ogilvie, 2010) or cycle safety (e.g. Owen, Kendrick, Mulvaney, Coleman, & Royal, 2011). One central conclusion of these reviews is that it is hard to draw firm conclusions because of the limited number both of high-quality interventions and of high-quality studies. While several high quality studies have been published subsequently (e.g. Goodman, Sahlqvist, & Ogilvie, 2014; Heinen, Panter, Mackett, & Ogilvie, 2015), the literature remains relatively small.
This evidence gap partly reflects the fact that much transport evidence does not fit neatly into the “intervention” category. Within the topic of infrastructure and cycling uptake, other relevant study types include ecological studies (correlating area-based characteristics with cycling levels, drawing conclusions about the weight of different factors); route choice studies (exploring where current cyclists ride, and deriving “revealed” preferences from this); and stated preference surveys (asking people what infrastructure would encourage them to cycle). The latter form of evidence is the focus of this review which asks whether and how cycle infrastructure preferences vary by gender and age.
The paper joins a growing number of publications in the transport field (e.g. Jothi Basu, Subramanian, & Cheikhrouhou, 2015; Vieira, Kliemann Neto, & Amaral, 2014; Wang & Notteboom, 2014) using a systematic review approach. Although stated preference studies have been common in transport research for some time (Hensher, 1994), they have rarely been synthesised using systematic reviews. Such synthesis is, however, increasingly common in other disciplines that make use of stated preference data, such as health economics (Whitty, Lancsar, Rixon, Golenko, & Ratcliffe, 2014).
Our choice of a systematic review approach means the paper benefits from the increasing robustness that comes with a more comprehensive search. However, the systematic, in-depth approach meant we had to choose a narrower question than narrative reviews can adopt. We would argue that this paper helps to demonstrate the value of systematically reviewing stated preference evidence in transport. We hope that it will be complemented by future systematic reviews of other topics and other types of evidence, including ecological studies and route choice studies.
In this review, an inclusive definition of “stated preference” is used. Traditionally in transport research, stated preference studies refer to techniques specifically used to estimate utility functions, used within choice modelling to predict change in use of transport infrastructure or services and/or to calculate cost–benefit ratios (Kroes & Sheldon, 1988). However, with the field becoming more interdisciplinary, health and social researchers (e.g. Winters & Teschke, 2010) are also conducting research asking about people’s infrastructural preferences, although without the aim of creating utility models. Here, both types of study are included.
Methods for selection, appraisal and synthesis
Methods are outlined here: for more details on search terms, sources retrieved and screening procedures, please see Appendix. Two authors (RA and BE), the study appraisers, searched the academic databases (EBSCO, Web of Science, ProQuest, PubMed, TRID, ARRB) plus 11 websites (via Google) (end of March 2015), following a search protocol developed by the team with input from additional advisors at the Centre for Diet and Activity Research. We only included studies that covered preferences related to cycle routes and infrastructure; so not, for example, preferences for taking bicycles on trains. Studies were included that reported analysis of any similarities or differences by age and gender. BE screened abstracts and led initial study selection with RA checking wherever uncertainty was flagged. RA appraised the studies.
In the selected articles, separation from motor traffic was by far the most common infrastructural characteristic discussed (with a clear comparison made by age and/or gender in 44/54 studies). This mostly involved questions about the existence or not of some form of separate provision, but sometimes involved questions about motor traffic flows, where sharing takes place. Hence, in analysis, we focused on this issue. Other issues covered were diverse; for example, two studies covering preferences for winter maintenance of cycle infrastructure, and another covering preferences related to “quality of signage”. It was not possible to synthesise similarities and differences related to these issues.
There are no established reporting guidelines for stated preference studies. We extracted data from each study on (i) issues that affect internal validity, (ii) issues that affect external validity or generalisability, and (iii) sample size. For internal validity, we focused on how preferences were elicited. Where little detail is given, participants may imagine quite different kinds of infrastructure when responding. Specifically, a “cycle lane” may be imagined as being effectively shared with motor traffic (an advisory painted lane), or separated by bollards, kerb or other barriers. More detail may allow more discrimination between different levels of separation from motor traffic.
How situations were communicated to participants, for example, words only, images, video.
- How specific the situations presented to participants were categorised as follows:
- Low to very low specificity, for example, respondents choosing between “cycle lane present”, and “no cycle lane”.
- Medium specificity, for example, respondents asked to choose between on-road segregated infrastructure, painted cycle lanes, and off-road tracks.
- High specificity, for example, images of a range of different infrastructural types with differing degrees of separation from motorised traffic.
We considered external validity to refer to whether survey results represent broader population views about preferred cycling environments. We did not consider the wider issue of whether these stated preferences accurately predict subsequent behaviour change (Bradley, 1988) because we would argue that views about desired service provision are important in themselves.
Sampling methods were categorised as follows:
Convenience sample, for example, students, participants in cycle touring event.
Purposeful convenience sampling, for example, potential cyclists, employees.
Representative survey, for example, randomly sampled national travel survey.
Studies with higher quality sampling methods of the general population are more likely to be representative of a potential cycling population. Inclusion of non-cyclists 1 was considered important as there are suggestions in the literature that cyclists’ preferences, particularly in low-cycling contexts, may not represent the views of potential cyclists (Horton & Jones, 2015).
Three rounds of screening were carried out to filter the evidence, with data extracted into a bespoke table in Excel. Analysis in Excel and SPSS explored both headline findings (similarities and differences in preferences) and the extent to which these were associated with study design. We attempted to record information that could be used for meta-analysis – quantitatively combining the results from multiple studies – but in general, information such as sub-group means was not provided, meaning that meta-analysis was not possible.
Results
Studies included and excluded
Our search strategy led to the identification of 54 separate studies, reported in 58 publications (Figure 1). 2
Figure 1.

Summary of evidence management strategy.
About the studies
Fifty studies examined stated preferences in relation to gender, with 33 covering age (adults) and only 2 discussing preferences related to child cycling (Table 1). A summary of study characteristics is presented in Table 3 and Figure 2.
Table 1. Characteristics of included studies.
| Number of studies | Percentage of studies | ||
|---|---|---|---|
| Sample size | Under 50 | 2 | 4 |
| 50–99 | 6 | 11 | |
| 100+ | 45 | 83 | |
| Not stated | 1 | 2 | |
| Study composition | All cyclists, or under 50% non-cyclists | 39 | 72 |
| At least 50% non-cyclists | 12 | 22 | |
| Not stated | 3 | 6 | |
| Country of origin | USA | 19 | 35 |
| UK | 8 | 15 | |
| Belgium | 3 | 6 | |
| Canada | 4 | 7 | |
| Other/more than one country | 20 | 37 | |
| Sampling method | Convenience sample | 34 | 63 |
| Purposive convenience sampling | 5 | 9 | |
| Random sampling | 14 | 26 | |
| Not stated | 1 | 2 | |
| Preference elicitation method | Description (only) | 26 | 48 |
| Existing infrastructure | 8 | 15 | |
| Images | 16 | 30 | |
| Video | 4 | 7 | |
| Situational specificity | Very low | 25 | 46 |
| Medium | 19 | 35 | |
| High | 10 | 19 |
Table 3. Gender, age and infrastructure preferences.
| Citation key | Country | Population | Statistically significant differences reported | Similarities |
|---|---|---|---|---|
| Akar, Fischer and Namgung (2013a)/Akar, Fischer and Namgung (2013b) | USA | Ohio State University students, faculty, staff |
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| Aldred (2015) | UK | Mostly cyclists in UK |
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| Antonakos (1995) | USA | 552 cyclists at four recreational bike tours in Michigan |
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| Antonakos (1995) | USA | 552 cyclists at four recreational bike tours in Michigan |
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| Berggren, Graves, Pickus, and Hand Wirtis (2012) | USA | Portland cyclists |
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| Bernhoft and Carstensen (2008) | Denmark | Pedestrians and cyclists aged 40–49 and 70+ in two provincial cities in Denmark |
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| Bernhoft and Carstensen (2008) | Denmark | Pedestrians and cyclists aged 40–49 and 70+ in 2 provincial cities in Denmark |
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| Börjesson and Eliasson (2012) | Sweden | Cyclists in Stockholm |
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| Börjesson and Eliasson (2012) | Sweden | Cyclists in Stockholm |
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| Brick, McCarthy, and Caulfield (2012) | Ireland | Cyclists and non-cyclists in Dublin |
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| Deenihan and Caulfield (2015) | Ireland | Tourists at two locations in Dublin |
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| Deenihan and Caulfield (2015) | Ireland | Tourists at two locations in Dublin |
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| Chataway et al. (2014) | Australia/Denmark | Cyclists targeted through university networks and cycling forums in Brisbane and Copenhagen. Fliers left on bikes in Copenhagen |
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| Chataway et al. (2014) | Australia/Denmark | As above |
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| dell’Olio, Ibeas, Bordagaray, and Ortúzar (2014) | Spain | 117 self-classified potential bike users in Santander |
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| Dickinson, Kingham, Copsey, and Pearlman Hougie (2003) | UK | Employees at three organisations in Hertfordshire |
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| Dill and McNeil (2014) | USA | Residents in Portland |
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| Dill and McNeil (2014) | USA | Residents in Portland |
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| Dill, Goddard, Monsere, and McNeil (2015) | USA | Cyclists and residents in five large US cities (Cyclists and non cyclists) |
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| Emond, Tang, and Handy (2009) | USA | Random sample of residents in six small cities in the USA |
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| Gardner (1998) | UK | Leisure cyclists, non-cyclists and utility cyclists in different areas of England |
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| Heesch, Sahlqvist, and Garrard (2012) | Australia | Adult cyclists in Queensland who were members of Bicycle Queensland (BQ) club |
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| Hughes and Harkey (1997) | USA | Twenty-three casual and 12 experienced cyclists |
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| Hunt and Abraham (2007) | Canada | Cyclists in Edmonton |
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| Krizek, Johnson and Tilahun (2005) | USA | 292 current and potential cyclists in Minnesota |
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| Landis, Vattikuti, and Brannick (1997) | USA | 150 cyclists aged 13+ in Tampa, Florida |
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| Landis et al. (2003) | USA | 60 cyclists aged 13+ in Orlando, Florida |
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| Lawson, Pakrashi, Ghosh, and Szeto (2013) | Ireland | 1954 cyclists, who regularly cycled in Dublin within the previous 12 months |
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| Lawson et al. (2013) | Ireland | As above |
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| Li, Wang, Liu, Schneider, and Ragland (2012) | China | 805 cyclists in the metropolitan area of Nanjing |
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| Li et al. (2012) | China | 805 cyclists in the metropolitan area of Nanjing |
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| Lusk, Wen, and Zhou (2014) | China | 1150 adults in Hangzhou |
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| Ma and Dill (2015) | USA | Random phone survey of 902 adults in Portland, Oregon region |
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| Ma and Dill (2015) | USA | Random phone survey of 902 adults in Portland, Oregon region |
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| Majumda, Mitra, and Pareekh (2015) | India | Residents of two small Indian cities |
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| Majumda et al. (2015) | India | Residents of two small Indian cities |
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| Mertens et al. (2014) | Belgium | 66 Flemish adults (45–64 years) living in an urban (>600 inhabitants/km2) or semi-urban (300–600 inhabitants/km2) municipality in Flanders or Brussels Capital Region |
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| Misra et al. (2015) | USA | 127 users of Cycle Atlanta smartphone application |
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| Misra et al. (2015) | USA | As above |
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| Parkin, Wardman, and Page (2007) | UK | 144 cyclist and non-cyclists from Bolton Metropolitan Borough Council, the University of Bolton and Bolton Royal Hospital |
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| Parkin et al. (2007) | UK | 144 cyclist and non-cyclists from Bolton Metropolitan Borough Council, the University of Bolton and Bolton Royal Hospital |
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| Petritsch, Ozkul, McLeod, Landis, and McLeod (2009) | USA | 80 cyclists at the Ride for Science 2009 in Tampa, Florida |
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| Petritsch et al. (2009) | USA | 80 cyclists at the Ride for Science 2009 in Tampa, Florida |
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| Ryley (2005, 2006) | UK | Cyclists and non-cyclists in West Edinburgh |
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| Sallis et al. (2013) | USA | 1780 adults aged 20–65 in Seattle, Washington and Baltimore, Maryland regions |
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| Sallis et al. (2013) | USA | As above |
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| Sanders (2014) | USA | 263 people who drive and/or cycle, in the San Francisco Bay Area |
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| Segadilha et al. (2014) | Brazil | 65 (80% male) cycle commuters in a medium-sized Brazilian city (São Carlos, SP) |
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| Segadilha et al. (2014) | Brazil | As above |
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| Sener, Eluru, and Bhat (2009) | USA | 1605 cyclists across more than 100 cities in Texas |
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| Sener et al. (2009) | USA | 1605 cyclists across more than 100 cities in Texas |
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| Steer Davies Gleave (2010a, 2010b) | UK | Cyclists and non-cyclists in London |
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| Steer Davies Gleave (2010a, 2010b) | UK | Cyclists and non-cyclists in London |
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| Steer Davies Gleave (2012) | UK | 2307 cyclists in London |
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| Steer Davies Gleave, 2012 | UK | 2307 cyclists in London |
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| Stinson and Bhat (2003) | USA | 3145 individuals in Texas (mostly avid bicyclists who use computers) |
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| Tilahun, Levinson, and Krizek (2007) | USA | 167 employees from University of Minnesota, excluding students and faculty |
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| Tilahun et al. (2007) | USA | As above |
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| Tin Tin et al. (2010) | New Zealand | 2469 cyclists, aged 16+, enrolled in the 2006 Wattyl Lake Taupo Cycle Challenge |
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| Tin Tin et al. (2010) | New Zealand | 2469 cyclists, aged 16 years or over, who had enrolled in the 2006 Wattyl Lake Taupo Cycle Challenge |
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| Tiwari (2014) | India | Review including report of survey of current bicyclists and potential bicyclists in Pune, India |
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| Twaddle, Hall, and Bracic (2010) | Canada | Staff and students at University of Calgary, particularly potential or current cyclists |
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| Twaddle et al. (2010) | Canada | Staff and students at University of Calgary, particularly potential or current cyclists |
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| Van Holle et al. (2014) | Belgium | 59 middle-aged adults living in urban or semi-urban areas across Flanders and the Brussels Capital region |
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| Vliet (2014) | The Netherlands | 200 respondents from various parts of the Netherlands; mixed recruitment methods |
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| Wardman, Tight, and Page (2007) | UK | 1996 commuters in four English cities, having removed 60% judged never likely to contemplate cycling |
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| Westerdijk (1990) | UK/Sweden/Netherlands | 284 cyclists and pedestrians aged 20+ in 3 countries (50 in Great Britain, 121 in Sweden and 113 in the Netherlands) |
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| Winters and Teschke (2010) | Canada | 1402 adult current and potential cyclists, that is, the “near market” for cycling in Vancouver, Canada |
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| Winters and Teschke (2010) | Canada | As above |
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| Wooliscroft and Ganglmair-Wooliscroft (2014) | New Zealand | 573 residents of New Zealand aged 18+ |
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| Wooliscroft and Ganglmair-Wooliscroft (2014) | New Zealand | 573 residents of New Zealand aged 18+ |
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Figure 2.

Articles by year.
It can be seen that this is a growing field, with 2009–2010 onwards providing a steady increase in the numbers of studies published. There is the potential to benefit from this growth by developing more consistent measures and/or sharing data for meta-analysis.
As noted above, the synthesis below includes 44 of 54 studies, with patterns similar to those for all 54 studies in terms of study composition and so on.
Country of origin
Over one-third of all studies were conducted in the U.S.A (19 studies), with eight from the UK, followed by Belgium and Canada (four each). Over two-thirds (39 of 56) were carried out only in high-income countries with low cycling rates. In terms of classifications, Australia, Brazil, Canada, Ireland, New Zealand, Spain, UK and U.S.A were judged to be low cycling. Other countries were judged to be medium or high cycling (Belgium, China, Denmark, India, the Netherlands and Sweden).
Study size and populations
Sample size varied considerably (35–3494, with one not stated). Most studies included more men than women. This was particularly true in studies set in low-cycling countries and drawing their sample from existing cyclists (in one study, “avid cyclists”). Only in two-fifths of the studies, at least 20% of the sample were non- or infrequent cyclists. A little over one-third of studies only sampled cyclists, while one only exclusively sampled non-cyclists. Overall, the proportion of regular cyclists included was far higher than for the general population, this being particularly true in studies in low cycling countries using convenience samples.
Reporting of results
Results were reported in diverse ways; for example, scores given out of five to different infrastructure types, or percentage of people agreeing that they would use a particular type of cycle route. Given the information available, a meta-analysis was not possible. For example, 13 of the 17 studies that reported no statistically significant gender differences in preferences for separation did not give subgroup means. 3
Sampling and elicitation methods
Sampling methods varied widely from household surveys to convenience samples of cyclists attending specific rides. Nearly two-thirds used convenience sampling with around a quarter of studies using random sampling.
Various study methods were used to elicit preferences (see Table 1). Almost half gave a text-based description of an infrastructure type (e.g. “painted lane”), conducted either using a paper questionnaire, on the phone, in person, or online. The participant would then be asked to rate the infrastructure type, although the type of rating would depend on the survey: including ranking preferences, assigning hypothetical monetary values, or asking people whether they would feel comfortable or safe.
The second most common type of elicitation method was to use images, either real or computer-generated. These were accompanied by questions about the desirability of the infrastructure type, as with studies using text-based elicitation. A less common method referenced existing infrastructure; for example, one study stopped cyclists in a series of sampled cycle lanes and asked them to rate the lane compared to other types of infrastructure. In other cases, researchers showed participants videos of infrastructure types, and then asked about preferences.
Finally, the situational specificity of the survey questions varied (see Table 1). Nearly half were very general (e.g. asking about “cycle lanes”) with one in five very specific, for example, testing a range of infrastructure types with differing extents of segregation. The remainder were in between, for example, making trade-offs between different infrastructure situations and trip times; with situations including bus/cycle lanes, parks/quiet residential streets (combined option), on road cycle lane, and off-road track.
Infrastructural preferences
Findings: gender and preferences for greater segregation from motor vehicles
Forty studies provided evidence as to whether preferences for separation from motor traffic differed by gender. Of these, 23 (57.5%) said women expressed stronger preferences for segregation from motor vehicles than did men (Table 2). Seventeen studies (42.5%) reported no statistically significant differences in gender preferences. No studies reported that men had stronger preferences than women for greater segregation from motor vehicles. Most studies that found no gender difference were small, and likely to have been insufficiently powered (see Figure 3) to detect a relevant difference. Among studies containing at least 200 participants, 20/29 (69%) reported stronger preferences in women than in men, whilst amongst studies containing fewer than 200 participants, only 3/10 (30%) did so. 4
Table 2. Preferences for separated infrastructure by age and gender.
| Preferences for separated infrastructure by gender and age | Number of studies | Percentage of studies | |
|---|---|---|---|
| Gender | Women’s preferences are stronger | 23 | 57.5 |
| No statistically significant differences | 17 | 42.5 | |
| Men’s preferences are stronger | 0 | 0 | |
| Age | Older people’s preferences are stronger | 9 | 38 |
| No statistically significant differences | 12 | 50 | |
| Younger people’s preferences are stronger | 3 | 13 |
Figure 3.

Gender and preferences for separated infrastructure, by sample size (minus one study with missing sample size).
Four-fifths of studies that found differences in preferences between men and women (19/24) also highlighted overall similarity in preferences across genders. For example, for both sexes, more people preferred complete separation from motor traffic compared with the presence of a painted lane but the gap in women was larger.
Differences were found by study type and composition. Smaller studies were less likely to report a gender difference, and some may have been underpowered to detect a meaningful difference. Of studies with larger sample sizes (> 100) and at least 20% non- or infrequent cyclists, 76.5% (17) found gender differences against 23.5% (4) who did not.
Of studies providing a high level of specificity, 78% (n =7/9) found a gender difference; this proportion was lower for studies with medium or low specificity (54% and 50%, respectively), but the difference was not statistically significant (p = .37 for trend). Studies that contained at least 50% non-cyclists found gender differences in 58% (7/12) of cases, while studies that did not found gender differences in 62% (16/26) of cases (p = .85 for difference). 5
By contrast, study context made a difference to findings. Among studies conducted in low-cycling countries, 69% (n = 20/29) found gender differences in preferences for separated infrastructure, while only 27% (3/11) found differences in studies where some or all participants lived in medium- or high-cycling countries (chi-squared p = .02 for association).
Findings: age and greater segregation from motor vehicles
Only 25 studies reported on age, with findings less consistent than for gender. While 9 studies (36% of those reporting on preferences for greater segregation and age) found that older people expressed stronger preferences for separation from motor vehicles, 13 (52%) found no differences, and 3 (12%) reported that older people had less strong preferences for separation from motor vehicles than younger people (Figure 4). Twenty-two out of 25 studies covering older people’s preferences highlighted overall similarity in preferences across age groups.
Figure 4.

Age and preferences for separated infrastructure, by sample size.
The relationship between sample size and findings is less clear-cut than for gender, although smaller studies were more likely to find “no difference” (4 out of 5 of studies with a sample size of below 200, compared to 9 out of 20 for the larger studies).
It might be thought that (among participants who cycle) older cyclists’ preferences do not stem from age per se, but from their having likely cycled longer than younger participants. We did not find support for this: among studies that mentioned controlling for cycling experience, an independent “age effect” remained in at least some of these, although reporting was sometimes unclear. However, some studies examining experience found an independent “experience effect” shaping perceptions of cycling infrastructure instead of, or as well as, age and gender effects (e.g. Ma & Dill, 2015; Ma, Dill, & Mohr, 2014).
Findings on child preferences or adult preferences for riding with or by children
Because we only found two studies addressing preferences for infrastructure involving children (Aldred, 2015 and Ghekiere et al., 2015), no attempt was made to quantitatively synthesise these. The former compared adults’ preferences for infrastructure for themselves when riding alone, to their preferences for themselves when riding with children, or when deciding whether a child should cycle. The latter compared adults’ preferences for child cycling with the children’s own preferences. Both studies point to a stronger preference for separation from motor traffic where children are cycling. This goes beyond barrier separation and covers issues such as, in Aldred (2015), protection at crossings and reduction in rat-running (when drivers use residential streets as a short cut avoiding main roads), and in Ghekiere et al. (2015), the need for wide and even cycle paths.
The Table 3 summarises some key points from the literature on preferences for infrastructure separated from motor traffic, as synthesised in the tables above:
Other preferences
Studies highlighted some other similarities and differences by age and gender, but these proved too diverse to synthesise within the constraints of a systematic review. Research covered topics including preferences for cycling environments that minimise the impact of winter conditions (e.g. use of higher quality brine to maintain infrastructure), routes that are direct and ideally avoid hills, and routes that are well lit and overlooked.
Discussion
We have found good evidence that women express stronger preferences for greater segregation from motor vehicles than men. This is within a context of similar overall types of preference, that is, typically very similar hierarchies of preference across genders. As stated by Misra, Watkins, and Le Dantec (2015): “Riders across all cyclist types prefer dedicated cycling facilities and are opposed to high speed traffic and high volume traffic, with little variation based on the classification of the cyclist”. In terms of age, again, there is an overall qualitative similarity between groups, but with some evidence suggesting that older people may have stronger preferences for separated infrastructure.
Gender differences were clearer among studies in low-cycling countries. In such settings, cycling is often perceived or experienced as risky, suitable only for the brave and confident (Horton & Jones, 2015). Men may be less concerned about risks than women, or more reticent about voicing their fears because these do not fit with dominant constructions of masculinity (Steinbach, Green, Datta, & Edwards, 2011). As such, the findings concerning gender differences in this review are arguably particularly relevant to places seeking to increase cycling from a low base.
Cycling speed may influence how views differ by age and gender. Cyclecraft, the UK’s national guide to cycling (Franklin, 2009), recommends a speed of 20 mph (32 kph) in challenging traffic situations. This is far faster than the average cycling speed, the gap being even greater for women and older people. Our analysis of National Travel Survey data 6 indicates that in England, among those aged 18–29, the average speed was 11.3 mph men and 10.5 mph for women, while among those aged 60–69, it was 9.6 mph for men and 9 mph for women. Slower cyclists report more near misses per mile (Aldred and Crosweller, 2015). A stronger preference for separated infrastructure among older people could also stem from greater vulnerability to injury.
A few studies suggest that women may be more likely to be affected by barriers including the need to carry items, winter conditions, hills, and personal safety concerns (see also Damant-Sirois & El-Geneidy, 2015; Heinen, van Wee, & Maat, 2010). These issues merit further research, including how these factors might interact with infrastructural characteristics. Future stated preference work on gender could focus on the detail of infrastructure types (e.g. verge vs. kerb separated) and on how other factors, for example, cycling experience and cycling speed, affect preferences by gender. Another recommendation is for more research both on children’s own views, and on adult views about infrastructure for child cycling. Understanding how infrastructural change might impact child cycling is crucial not just for children but also for carers, disproportionately affecting trips made by women (Aldred et al., 2015).
Among studies covering age, definitions of older age varied considerably as did methods for evaluating its effects. While some studies used age in years as an independent variable within linear regression, or considered 3–6 categories, others used very different cut-offs for “older” cyclists. The use of harmonised age categories and/or treatment of age as a variable would have improved our ability to assess its impact. A recommendation that follows from this would be for stated preference studies to more routinely publish simple anonymised data sets (e.g. on the UK Data Archive or, given the non-sensitive nature of the data, on journal websites) suitable for individual-level meta-analysis. Comparability would also be enhanced by development of reporting guidelines.
The level of situational specificity varied substantially and this is worthy of further methodological investigation. Higher specificity potentially introduces more unobserved variation (e.g. related to path width or adjacent motor traffic), although this can be minimised or reduced (e.g. using manipulated photographs). However, higher specificity enables greater consistency in what people understand they are being asked to compare. Many less specific studies simply reference a “cycle lane”, which could be assumed to be either a painted on-road lane or track with varying levels of segregation (Steer Davies Gleave, 2010a, 2012). More realistic representation of infrastructure allows greater discrimination between options and may help us estimate more realistically the type of infrastructure that may be required to substantially grow cycling levels. While there is not one right way to do things, future research should aim for comparability with published methods wherever possible. This is not to deny the need for innovation. Future research into infrastructure preferences may want to consider combining qualitative and quantitative approaches, and make greater use of video methods (see e.g. Ghekiere et al., 2014, 2015).
Finally, policy should focus on the infrastructural needs and preferences of under-represented groups, including older people, women, children and those cycling with children or making decisions about child cycling. Younger people, men, and those travelling without children also generally prefer separation from motor traffic, so building for under-represented groups should, if done well, suit others. Inclusive infrastructure is particularly important given evidence that some other barriers to cycling may be stronger for under-represented groups (van Bekkum, 2011; Bergström & Magnusson, 2003; Daley, Rissel, & Lloyd, 2007; Damant-Sirois & El-Geneidy, 2015; Finch et al., 1985; Steinbach et al., 2011). For example, women may have stronger concerns than men about safety from crime, while older people may struggle to cycle longer distances. Focusing on the needs and preferences of under-represented groups should be sensitive to these issues and, for example, take account of concerns about crime and route directness when planning the location of high-quality infrastructure.
Supplementary Material
Acknowledgements
We would like to thank David Ogilvie and Jenna Panter for advice on the scope of this review, and also to thank the anonymous peer reviewers and journal editors.
The views reported in this paper are those of the authors and do not necessarily represent those of the DfT, Brook Lyndhurst, the NIHR, the NHS or the Department for Health. None of the funders played any role in the conduct of this systematic review, in the interpretation of its outputs, in the writing of this report, or the decision to submit this article for publication.
Funding Statement
The work presented was funded by the Department for Transport (contract no. RM5019SO7766: “Provision of Research Programme into Cycling: Propensity to Cycle”), with project management by Brook Lyndhurst. JW’s contribution was supported by an MRC Population Health Scientist Fellowship. JW’s contribution was also supported by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence funded by the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research (NIHR), and the Wellcome Trust. AG’s contribution was supported by an NIHR post-doctoral fellowship.
Notes
Definitions of “non-cyclists” varied between studies; here, we mean someone who did not cycle in the last week (or longer). It was not always possible to determine whether there were over 20% non-cyclists or not.
Note: in the case of the 17 publications where full text could not be found, both reviewers read the abstracts and considered almost all unlikely to be relevant: most were conference papers or policy reports, and often seemed relatively tangentially connected to the research question (we had erred on the side of inclusivity in Stage 3 where we could not initially find publications).
In two cases, graphs were given illustrating this, but not the precise figures.
One study did not report sample size.
Not all studies could be included due to difficulty in determining numbers of cyclists and non-cyclists.
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