ArticlePDF Available

Assessing the impact of road traffic on cycling for leisure and cycling to work

DOI:10.1186/1479-5868-8-61
Authors:
Charles E Foster at University of Bristol
Charles E Foster
Jenna Panter at University of Cambridge
Jenna Panter
Nicholas J Wareham
Nicholas J Wareham
Nicholas J Wareham
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Download full-text PDFDownload full-text PDFDownload full-text PDF
Read full-text
Download citation

Abstract and Figures

To explore the relationship between leisure and commuter cycling with objectively measured levels of road traffic and whether any relationship was affected by traffic levels directly outside of home or in local neighbourhood. We conducted a secondary analysis of data from the UK European Prospective Investigation of Cancer (EPIC) Norfolk cohort in 2009. We used a geographical information system (GIS) and gender specific multivariate models to relate 13 927 participants' reported levels of cycling with an index of road traffic volume (Road Traffic Volume Index Score--RTVIS). RTVIS were calculated around each participants home, using four distance based buffers, (0.5 km, 1 km, 2 km and 3.2 km). Models were adjusted for age, social status, education, car access and deprivation. Both genders had similar decreases in leisure cycling as traffic volumes increased at greater distances from home (OR 0.42, (95% CI 0.32-0.52, p < 0.001) for women and OR 0.41, (95% CI 0.33-0.50, p < 0.001) for men in the highest quartile at 3.2 km). There was no effect of traffic volumes at any distance on commuter cycling. Traffic volumes appear to have greater impact on leisure cycling than commuter cycling. Future research should investigate the importance of traffic on different types of cycling and include psychosocial correlates.
Figures - uploaded by Charles E Foster
Author content
All figure content in this area was uploaded by Charles E Foster
Content may be subject to copyright.
1479-5868-8-
61.pdf
Content uploaded by Charles E Foster
Author content
All content in this area was uploaded by Charles E Foster
Content may be subject to copyright.
Assessing the impact of road traffic on cycling for leisure and cycling to wo
rk.pdf
Available via license: CC BY 2.0
Content may be subject to copyright.
Assessing the impact of road traffic on cycling for leisure and cycling to wo
rk.pdf
Available via license: CC BY 2.0
Content may be subject to copyright.
SHOR T PAPE R Open Access
Assessing the impact of road traffic on cycling
for leisure and cycling to work
Charlie E Foster
1*
, Jenna R Panter
2
and Nicholas J Wareham
2
Abstract
Background: To explore the relationship between leisure and commuter cycling with objectively measured levels
of road traffic and whether any relationship was affected by traffic levels directly outside of home or in local
neighbourhood.
Findings: We conducted a secondary analysis of data from the UK European Prospective Investigation of Cancer
(EPIC) Norfolk cohort in 2009. We used a geographical information system (GIS) and gender specific multivariate
models to relate 13 927 participants’reported levels of cycling with an index of road traffic volume (Road Traffic
Volume Index Score - RTVIS). RTVIS were calculated around each participants home, using four distance based
buffers, (0.5 km, 1 km, 2 km and 3.2 km). Models were adjusted for age, social status, education, car access and
deprivation. Both genders had similar decreases in leisure cycling as traffic volumes increased at greater distances
from home (OR 0.42, (95% CI 0.32-0.52, p < 0.001) for women and OR 0.41, (95% CI 0.33-0.50, p < 0.001) for men in
the highest quartile at 3.2 km). There was no effect of traffic volumes at any distance on commuter cycling.
Conclusions: Traffic volumes appear to have greater impact on leisure cycling than commuter cycling. Future
research should investigate the importance of traffic on different types of cycling and include psychosocial
correlates.
Keywords: Cycling, traffic, GIS
Background
Cycling is considered to be a healthy, low carbon and
sustainable physical activity behaviour [1]. Although
travel by bicycle does introduce health risks through
accidents and injuries [2] the health benefits of cycling
have been shown to outweigh these risks [3]. More
specifically, studies have suggested that commuter
cyclists have a lower mortality risk than non-cycling
commuters, independent of physical activity levels [4].
Across many international countries the numbers of
car trips are increasing and in some there are simulta-
neous declines in active travel trips by walking or
cycling [5].
Characteristics of the built environment are also sus-
pected to contribute to levels of walking and cycling [6],
although for active travel behaviors in particular the
evidence is inconsistent [7]. It could be hypothesized that
in areas which have both unsupportive built environments
for active travel and high traffic volumes, a double burden
of negative associations for active travel could be pro-
duced. International studies have focused on the negative
impact of perceptions of traffic levels and safety on walk-
ing and cycling but have not used objective measures of
traffic volume [8,9].
In our previous study, exposure to higher levels of
traffic around the home was associated with less leisure
cycling [10]. The aim of this study was to (i) explore the
relationship between leisure cycling and commuter
cycling with objectively measured levels of road traffic
derived using a geographical information system (GIS)
and (ii) investigate if any relationship was affected by
trafficlevelsdirectlyoutsideofthehomeorbetween
home and local destinations.
Methods
The UK EPIC-Norfolk study was designed as a prospec-
tive cohort study and the methods of recruitment, sam-
pling and overall sample representativeness have been
* Correspondence: charlie.foster@dphpc.ox.ac.uk
1
Department of Public Health, University of Oxford, UK
Full list of author information is available at the end of the article
Foster et al.International Journal of Behavioral Nutrition and Physical Activity 2011, 8:61
http://www.ijbnpa.org/content/8/1/61
© 2011 Foster et al; li censee BioMed Central Ltd. This is an Open Access article distributed under the terms of th e Creative Commons
Attribution License (http://creative commons.org/licenses/by/2.0), which permits unrestricted use, distribu tion, and reproduction in
any medium, pro vided the original work is properly cited.
described elsewhere [11]. Data on self-reported measures
of physical activity was collected from 15,786 adults
using EPAQ2 between 1998 and 2000. This questionnaire
asks about the frequency and duration of physical activity
at home, at work (including travel to work) and for
recreation, over the past 12 months. Ethical approval for
the EPIC-Norfolk study was given by the Norfolk
Research Ethics Committee.
Participants were asked to report how often they used a
bicycle to get to work using the response categories of
‘always’,‘usually’, occasionally’and ‘never or rarely’. Parti-
cipants were classified as commuter cycling if they
reported ‘always’travelling to work by bicycle. Leisure
cycling was assessed by three items which asked about the
number of occasions of cycling for pleasure, for racing and
rough terrain cycling. If participants reported at least 1
occasion of any of these activities they were classified as
engaging in ‘some’leisure cycling.
Objectivemeasuresofroadtrafficvolumewereesti-
mated using a GIS (ESRI ArcGIS 9.2). We calculated a
proxymeasureoftrafficvolume(RoadTrafficVolume
Index Score - RTVIS) for each participant using four
different distance based buffers around each indivi-
duals home postcode (0.5 km, 1 km, 2 km, 3.2 km). It
was calculated by computing the total lengths of four
different types of road (principal roads or motorways,
A-roads (major roads), B-roads (minor or local roads)
and unclassified roads) within these buffers (centred
on participants’homes) and weighting these based on
the average road speed for each classification [12].
Scores were divided into quartiles and the lowest quar-
tile was used as the reference group. Using a variety of
distance buffers allowed us to examine any potential
differences in the associations between cycling beha-
viourandtrafficvolumeatdifferent proximities, as
there is currently uncertainty about size of the area
from home which influences commuting or leisure
related activities. The choice of the largest radius size
reflected the UK government’s aim to encourage a
shift from car use to walking or cycling for short jour-
neys under 2 miles (3.2 km) [13]. We estimated that a
2 mile cycle journey (at 8 mph) should take an adult
approximately 15 minutes.
Possible confounders included age, gender, social sta-
tus, educational qualifications, area socioeconomic
deprivation, car ownership, ethnicity, and self-reported
health conditions.
We built a series of gender specific multivariate mod-
els to calculate the odds ratios of commuter and leisure
cycling associated with RTVIS adjusted for identified
confounders. We checked for effect modification and
interactions between variables at each stage of the
model [14].
Results
Physical activity data were available for 15 572 partici-
pants, however we excluded those who had incomplete
postcode data or had moved out of the study area
(5.9%), unusually high levels of self reported physical
activity (1.5%) and missing socio-demographic data
(3.1%). This left 13 927 participants for analysis.
Table 1 shows the characteristics of the sample.
A higher proportion of men reported any leisure cycling
compared to women, however we found a slightly
higher proportion of women reported any occasions of
commuter cycling than men (p < 0.05).
Table 2 presents the adjusted odds ratios for reporting
leisure and commuter cycling.
Using a 500 m buffer, an increasing RTVIS was asso-
ciated with higher odds of leisure cycling and women’s
commuter cycling. Yet, with larger buffer sizes the odds
of leisure cycling decreased with increasing RTVIS,
however there were no such associations observed for
commuter cycling for either genders.
Conclusions
Exposure to increasing level of traffic around home was
associated with a reduction in leisure cycling and not
for commuter cycling. Both genders had similar
decreases in leisure cycling as traffic volumes increased
between 500 m to 1000 m from home.
A few studies have reported conflicting associations
between characteristics of the built environment, traffic
and different types of cycling behaviour [8,15,16]. How-
ever direct comparison is difficult due to differences in
methods in construction of the outcome variable by
combining walking and cycling, or all cycling or cycle
path use. One case study of non-cyclists reported similar
impacts of traffic made cycling dangerous based on a
combination of the poor quality of the road environ-
ment, plus heavy and speeding traffic, and worries about
the dangers of cycling [17].
Triano and Freedson recently identified that a diver-
sity of non-comparable methods are a limitation to
environmental/behavioral research [18]. This study was
limited by use of a non-objective physical activity mea-
sure however data were collected before the possible
application of such measures could be realistically used
in such a large sample. Outcome variable data were col-
lected using appropriate methods for a large cohort
study, using a reliable and valid measure. The GIS
derived exposure measure is based on road transport
network and local road speed, which could easily be
adopted by other researchers to make cross-study and
country comparisons possible. The use of increasing
buffer areas for traffic also avoided any potential inter-
individual variation in the size of neighbourhood and
Foster et al.International Journal of Behavioral Nutrition and Physical Activity 2011, 8:61
http://www.ijbnpa.org/content/8/1/61
Page 2 of 5
Table 1 Characteristics of participants by socio-demographic, physical activity and environmental variables
Men (n = 6134) Women (n = 7793)
Occasions of leisure cycling Occasions of commuter cycling Occasions of leisure cycling Occasions of commuter cycling
None Any None Any None Any None Any
Percentage of Subjects 80.1 19.9 96.2 3.8 85.1 14.9 95.7 4.3
Age
41-50 years of age 63.9 36.1* 91.4 8.6* 70.1 29.9* 91.5 8.5*
51- 60 years of age 73.0 27.0 93.4 6.6 80.4 19.6 93.2 6.8
61-70 years of age 82.7 17.3 97.6 2.4 893. 10.7 97.9 2.1
71-80 years of age 91.6 8.4 99.5 0.5 95.5 4.5 99.4 0.6
Social status
Professional 75.4 24.6* 95.0 5.0* 82.8 17.2* 95.2 4.8*
Managerial & Tech. 80.0 20.0 97.7 2.3 84.0 16.0 97.0 3.0
Skilled non-manual 80.6 19.4 97.1 2.9 88.9 11.1 97.3 2.7
Skilled manual 80.1 19.9 95.1 4.9 83.9 16.1 94.9 5.1
Partly skilled 82.2 17.8 93.3 6.7 85.5 14.5 92.4 7.6
Unskilled 85.1 14.9 94.2 5.8 84.2 15.8 91.0 9.0
Educational qualifications
Degree or higher 76.1 23.9* 95.9 4.1 80.4 19.6* 95.3 4.7
Any qualifications 78.9 21.1 96.4 3.6 83.3 16.7 96.3 3.7
No qualifications 85.0 15.0 95.7 4.3 88.2 11.8 95.5 4.5
Car ownership
Yes 80.7 19.3* 97.3 2.7* 85.0 15.0 97.0 3.0*
No 75.7 24.3 88.2 11.8 85.2 14.8 91.0 9.0
Townsend index
Quintile 1 (most affluent) 80.2 19.8 97.8 2.2* 86.2 13.8* 96.9 3.1*
Quintile 2 79.3 20.7 96.3 3.7 86.4 13.6 97.1 2.9
Quintile 3 78.6 21.4 96.4 3.6 82.2 17.8 95.7 4.3
Quintile 4 80.5 19.5 96.3 3.7 83.9 16.1 95.9 4.1
Quintile 5 (most deprived) 81.9 18.1 93.9 6.1 86.5 13.5 93.1 6.9
Self-reported health
With condition 82.3 17.7* 97.4 2.6* 86.8 13.2* 96.1 3.9
Without condition 78.4 21.6 95.2 4.8 83.0 17.0 95.4 4.6
* Signifies significant trend across variable categories at P< .05.
Foster et al.International Journal of Behavioral Nutrition and Physical Activity 2011, 8:61
http://www.ijbnpa.org/content/8/1/61
Page 3 of 5
warrants further research into what is the size of the
local neighbourhood in conjunction with cycling [19].
Ideally it would be helpful also include measures of
cycle path availability, alongside data on route choices
by cyclists, who might choose to use roads with lower
traffic volumes while cycling.
Future research should investigate the importance of
traffic and include other possible psychological journey
related correlates (e.g. attitude, confidence to cycling),
by gender and age [20]. Models should consider the
impact of environmental correlates at different distance
around home for different types of cycling (commuting
or leisure).
Conflict of Interest
The authors declare that they have no competing
interests.
Acknowledgements & Funding
We would like to thank the participants of the EPIC-Norfolk cohort. We also
would like to thank the staff of Norfolk’s local authorities and organizations
as well the Ordnance Survey who provided us access to data.
The work was funded by the British Heart Foundation Project Grant (BHF/
PG/03/045) and our own institutions. We thank the Ordnance Survey/EDINA
service who provided Road Network and Code Point data used in the
analysis. CF is funded by the British Heart Foundation. JP is funded by
National Institute for Health Research and NW is funded by the Medical
Research Council. NW and JP work under the auspices of the Centre for Diet
and Activity Research (CEDAR), a UKCRC Public Health Research Centre of
Excellence. Funding from the British Heart Foundation, Department of
Health, Economic and Social Research Council, Medical Research Council
and the Wellcome Trust, under the auspices of the UK Clinical Research
Collaboration, is gratefully acknowledged.
Author details
1
Department of Public Health, University of Oxford, UK.
2
Medical Research
Council Epidemiology Unit and Centre for Diet and Activity Research
(CEDAR), Institute of Public Health, Cambridge, UK.
Authors’contributions
CF & JP conceived of the study, and participated in its design and
coordination and helped to draft the manuscript. NW participated in its
design and coordination and helped to draft the manuscript. All authors
read and approved the final manuscript.
Received: 3 March 2011 Accepted: 10 June 2011
Published: 10 June 2011
References
1. Woodcock J, Banister D, Edwards P, Prentice A, Roberts I: Energy and
transport. Lancet 2007, 370:1078-1088.
2. Thompson DC, Rivara FP, Thompson RS: Effectiveness of bicycle safety
helmets in preventing head injuries. A case-control study. Journal of the
American Medical Association 1996, 276:1968-1973.
3. Hillman M: Children, Transport and Quality of Life. 1993.
4. Andersen LB, Schnohr P, Schroll M, HO H: All-cause mortality associated
with physical activity during leisure time, work, sports and cycling to
work. Arch Intern Med 2000, 160:1621-1628.
Table 2 Odds ratios (95% CI) for reporting leisure and commuter cycling in past month by quartiles of Road Traffic
Volume Index Score
Odds of leisure cycling (95% CI) a Odds of commuter cycling (95% CI) b
Men Women Men Women
RTVIS within 500 m
Quartile 1 (Light Traffic) 1.00 1.00 1.00 1.00
Quartile 2 1.11 (0.92-1.34) 1.11 (0.92-1.34) 0.73 (0.50-1.07) 1.14 (0.82-1.57)
Quartile 3 1.80 (0.98-1.42) 1.16 (0.97-1.40) 0.71 (0.48-1.04) 1.06 (0.76-1.50)
Quartile 4 (Heavy Traffic) 1.23 (1.02-1.48) 1.05 (0.88-1.26) 0.76 (0.53-1.12) 1.20 (0.87-1.66)
RTVIS within 1000 m
Quartile 1 (Light Traffic) 1.00 1.00* 1.00 1.00
Quartile 2 0.89 (0.74-1.06) 0.71 (0.60-0.85) 0.80 (0.50-1.28) 1.27 (0.89-1.81)
Quartile 3 0.73 (0.61-0.88) 0.47 (0.39-0.57) 1.60 (1.06-2.39) 1.37 (0.97-1.94)
Quartile 4 (Heavy Traffic) 0.49 (0.40-0.60) 0.48 (0.39-0.58) 1.27 (0.83-1.93) 1.01 (0.70-1.46)
RTVIS within 2000 m
Quartile 1 (Light Traffic) 1.00* 1.00* 1.00 1.00
Quartile 2 0.83 (0.70-0.99) 0.68 (0.57-0.80) 1.08 (0.69-1.70) 1.44 (1.01-2.04)
Quartile 3 0.68 (0.56-0.81) 0.51 (0.42-0.61) 1.64 (1.07-2.50) 1.34 (0.94-1.91)
Quartile 4 (Heavy Traffic) 0.43 (0.35-0.53) 0.43 (0.35-0.52) 1.53 (1.00-2.34) 1.10 (0.76-1.60)
RTVIS within 3200 m
Quartile 1 (Light Traffic) 1.00* 1.00* 1.00 1.00
Quartile 2 0.77 (0.65-0.92) 0.71 (0.60-0.84) 0.86 (0.54-1.36) 1.67 (0.17-2.36)
Quartile 3 0.61 (0.51-0.74) 0.47 (0.39-0.57) 1.58 (1.04-2.37) 1.14 (0.78-1.65)
Quartile 4 (Heavy Traffic) 0.41 (0.33-0.50) 0.42 (0.35-0.52) 1.31 (0.86-2.00) 1.11 (0.76-1.61)
*p value test for trend < 0.001
a Models adjusted for age, social status, education, car ownership, travel mode to work, occupational physical activity.
b Models adjusted for age, social status, car ownership, area deprivation, occupational physical activity and recreational physical activity
CI, Confidence Intervals; RTVIS Road Traffic Volume Index Score.
Foster et al.International Journal of Behavioral Nutrition and Physical Activity 2011, 8:61
http://www.ijbnpa.org/content/8/1/61
Page 4 of 5
5. Bassett DR, Pucher J, Buehler R, Thompson DL, Crouter SE: Walking, cycling
and obesity rates on Europe, North America and Australia. Journal of
Physical Activity and Health 2008, 5:795-814.
6. Heath G, Brownson RC, Kruger J, Miles R, Powell KE, Ramsey L: The
effectiveness of urban design and land use and transport policies and
practices to increase physical activity: A systematic review. Journal of
Physical Activity and Health 2006, 3:55-76.
7. Panter JR, Jones AP: Attitudes and the environment: What do and don’t
we know? Journal of Physical Activity & Health 2010, 7:551-561.
8. Troped PJ, Saunders RP, Pate RR, Reininger B, Ureda JR, Thompson SJ:
Associations between Self-Reported and Objective Physical
Environmental Factors and Use of a Community Rail-Trail. Preventive
Medicine 2001, 32:191-200.
9. Titze S, Stronegger WJ, Janschnitz S, Oja P: Association of built-
environment, social-environment and personal factors with bicycling as
a mode of transportation among Austrian city dwellers. Preventive
Medicine 2008, 47:252-259.
10. Foster C, Hillsdon M, Jones A, Grundy C, Wilkinson P, White M, Sheehan B,
Wareham N, Thorogood M: Objective Measures of Physical Activity -
Results of the Environment and Physical Activity Study in English adults.
Journal of Physical Activity & Health 2009, 6:S70-80.
11. Wareham NJ, Jakes RW, Rennie KL, Mitchell J, Hennings S, Day NE: Validity
and repeatability of the EPIC-Norfolk Physical Activity Questionnaire. Int
J Epidemiol 2002, 31:168-174.
12. Department for Transport: A bulletin of public transport statistics: Great
Britain: 2003 Edition. London: The Stationery Office; 2003.
13. Department for Transport: Walking and Cycling: An Action Plan. The
Stationery Office: London; 2004.
14. Altman D: Practical statistics for medical research. London: Chapman &
Hall/CRC; 1999.
15. Cochrane T, Davey R, Gidlow C, Smith G, Fairburn J, Armitage CJ,
Stephansen H, Speight S: Small area and individual level predictors of
physical activity in urban communities: a multi-level study in Stoke on
Trent, England. International Journal of Environmental Research and Public
Health 2009, 6:654-677.
16. Wendel-Vos WGC, Schuit AJ, de Niet R, Bossuizen HC: Factors of the
physical environment associated with walking and bicycling. Medicine
and Science in Sports and Exercise 2004, 36:725-730.
17. Ogilvie D, Mitchell R, Mutrie N, Petticrew M, Platt S: Shoe leather
epidemiology: active travel and transport infrastructure in the urban
landscape. International Journal of Behavioural Nutrition and Physical Activity
2010, 7.
18. Troiano RP, Freedson PS: Promises and pitfalls of emerging measures of
physical activity and the environment. American Journal of Preventive
Medicine 2010, 38.
19. Smith G, Gidlow C, Davey R, Foster C: A pilot study of English adults’
definitions of their local walking neighbourhoods - What is my walking
neighbourhood? International Journal of Behavioural Nutrition and Physical
Activity 2010, 276:1974.
20. Titze S, Giles-Corti B, Knuiman MW, Pikora TJ, Timperio A, Bull FC, van
Niel K: Associations between intrapersonal and neighborhood
environmental characteristics and cycling for transport and recreation in
adults: baseline results from the RESIDE study. Journal of Physical Activity
& Health 2010, 7:423-431.
doi:10.1186/1479-5868-8-61
Cite this article as: Foster et al.: Assessing the impact of road traffic on
cycling for leisure and cycling to work. International Journal of Behavioral
Nutrition and Physical Activity 2011 8:61. Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit
Foster et al.International Journal of Behavioral Nutrition and Physical Activity 2011, 8:61
http://www.ijbnpa.org/content/8/1/61
Page 5 of 5
... (Kaplan et al., 2015) found that individual use of bicycles on holidays was associated with collective behavior, especially among family and friends. When the traffic volume around the residence increases, cycling for leisure declines, while cycling commuting does not (Foster et al., 2011). Furthermore, students may have been influenced by the content of the questionnaire to change their behaviors. ...
... The reason for this may be that some high-grade roads (e.g. trunk roads) gather more traffic Liu et al., 2014), so the corresponding traffic noise and PM 2.5 are higher (Foster et al., 2011;Wu et al., 2020). The government departments then allocate a lot of greenness and even form green barriers (You, 2016). ...
The effect of eye-level street greenness exposure on walking satisfaction: The mediating role of noise and PM2.5
Article
  • Oct 2022
While there are plenty of studies on the effects of neighborhood and park greenness on personal overall satisfaction and walking behavior, the relationship between street greenness exposure and walking satisfaction has received limited attention. Also, the possible pathways by which street greenness exposure affects walking satisfaction need to be further examined. To fill these research gaps, we measured eye-level street greenness using street view images, machine learning techniques and global position systems. A structural equation model was used to examine the mediating effects of objective noise and PM2.5 exposure and related subjective annoyance, on the relationship between street greenness exposure and people’s walking satisfaction. The results showed that street greenness exposure not only had a significant direct effect on walking satisfaction, but also has a significant indirect effect on walking satisfaction through subjective environmental annoyances (including noise and PM2.5 annoyances) rather than through objective noise and PM2.5 exposures. Besides physical activity and social interaction, the indirect effect of street greenness exposure on walking satisfaction through subjective environmental pollution annoyance accounted for about 17.39% of the total effect and cannot be ignored. These results suggest that the urban greenness layout policy should not only consider residential greenness but should improve people’s environmental perception and walking satisfaction by allocating more greenness on streets with high noise and PM2.5 levels.
... There are various ways to operationalize the predominance of motorized traffic on different roads. Previous studies, using the number of car lanes (Evans-Cowley & Akar, 2013), traffic volumes (Foster, Panter, & Wareham, 2011;Vandenbulcke et al., 2011;Wahlgren & Schantz, 2014), permitted speed (Rowangould & Tayarani, 2016), road categories (Abraham, et al., 2002), or a combination of several characteristics (Birk et al., 2010), deliver varying results. Many interventional or cross-sectional studies prove the importance of calmer streets (Caulfield, 2014;Hou et al., 2010; while others do not (Cairns, Warren, Garthwaite, Greig, & Bambra, 2015). ...
Exploring Bikeability - Urban Infrastructure and Bicycle Transport
Thesis
  • Jan 2021
A broad combination of different methods is used in an integrated approach to evaluate interrelations between infrastructure and bicycle transport. First, the bike-friendliness of the urban environment (bikeability) is defined via a literature analysis in combination with an interactive expert survey. This definition of bikeability is then operationalized using open geodata, ensuring transferability. In addition, the effects of bikeability on mode choice are evaluated using a multinomial logit model. On the detailed level of route choice, the influencing parameters are further differentiated in a graphical online stated preferences survey. Mixed logit discrete choice models are then developed to quantify the trade-offs of interest. Furthermore, extensive data retrieved from a bike routing engine are clustered and analysed to reveal underlying route preferences, without the potential effects of an overt survey situation. Results show a consensus in understanding of bikeability, as provided by experts. This is defined by a stable interaction of the components composing bikeability. The mode choice model proves the strong positive effect of high bikeability on choosing the bike as a mode of transport. On the detailed level of route choice, the particular influence of cycling infrastructure along main streets is confirmed, and differentiated according to the specific design. Aside from specific individual and structural implications, a greater separation from motorized transport generally corresponds with a higher utility for cyclists. Regarding side streets, the results reveal the general importance of minor roads and the enormous benefit of cycle streets prioritizing cyclists. The presented findings may be used for further research and deliver recommendations for planning, which are discussed in the present study. Zur Analyse von Zusammenhängen zwischen Radverkehr und Infrastruktur kommt eine breite Kombination unterschiedlicher Methoden in einem integrierten Gesamtansatz zum Einsatz. An die Herleitung der radfahrtauglichen Umgebung (Bikeability) über eine Literaturanalyse und einen interaktiven Expertenprozess schließen sich die Operationalisierung dieser Definition mittels offener Geodaten sowie die Bewertung der Einflüsse auf die Verkehrsmittelwahl in einem multinomialen Verkehrsmittelwahlmodell an. Auf der Ebene der Routenwahl werden dann die Einflussgrößen in einem diskreten Entscheidungsexperiment differenziert. Dabei kommen logistische Regressionsmodelle zum Einsatz. Des Weiteren werden Daten aus der Fahrradnavigation in einem Clusterverfahren genutzt. Im Ergebnis zeigt sich ein konsensuales Verständnis von Bikeability unter Abbildung des Zusammenspiels der fünf wichtigsten infrastrukturellen Parameter. Durch Nutzung offener Geodaten ist der entwickelte Ansatz uneingeschränkt räumlich übertragbar und thematisch adaptierbar. Das Verkehrsmittelwahlmodell belegt den stark positiven Einfluss der Bikeability auf die Wahl des Fahrrades als Verkehrsmittel. Auf der differenzierten Ebene der Routenwahl bestätigt sich der besondere Einfluss der Radinfrastruktur an Hauptverkehrsstraßen. Die Ergebnisse zeigen dabei eine Abstufung im Nutzen für den Radverkehr, die dem Ausmaß der baulichen Trennung vom motorisierten Individualverkehr entspricht, sowie spezifische individuelle und strukturelle Implikationen. Neben Infrastrukturen an Hauptstraßen wird durch die angewandten Methoden auch die generelle Bedeutung von Nebenstraßen verdeutlicht und weiter differenziert. Die Ergebnisse zeigen dabei den enormen Nutzen von Fahrradstraßen aus Sicht der Nutzenden. Die Erkenntnisse bieten spezifische Anknüpfungspunkte, sowohl für weitere Forschung als auch für Planung und Praxis, die in der Arbeit diskutiert werden.
... There are various ways to operationalize the predominance of motorized traffic on different roads. Previous studies, using the number of car lanes [49], traffic volumes [50][51][52], permitted speed [53], road categories [54] or a combination of several characteristics [55], deliver varying results. Many interventional or cross-sectional studies prove the importance of calmer streets [56][57][58] while others do not [59]. ...
More Than Bike Lanes — A Multifactorial Index of Urban Bikeability
Article
  • Oct 2021
The present study aims to deduce bikeability based on a collective understanding and provides a methodology to operationalize its calculation based on open data. The approach contains four steps building on each other and combines qualitative and quantitative methods. The first three steps include the definition and operationalization of the index. First, findings from the literature are condensed to determine relevant categories influencing bikeability. Second, an expert survey is conducted to estimate the importance of these categories to gain a common understanding of bikeability and merge the impacting factors. Third, the defined categories are calculated based on OpenStreetMap data and combined to a comprehensive spatial bikeability index in an automated workflow. The fourth step evaluates the proposed index using a multinomial logit mode choice model to derive the effects of bikeability on travel behavior. The expert process shows a stable inter-action between the components defining bikeability, linking specific spatial characteristics of bikeability and associated components. Applied components are, in order of importance, biking fa-cilities along main streets, street connectivity, the prevalence of neighborhood streets, green path-ways and other cycle facilities, such as rental and repair facilities. The mode choice model shows a strong positive effect of a high bikeability along the route on choosing the bike as the preferred mode. This confirms that the bike friendliness on a route surrounding has a significant impact on the mode choice. Using universal open data and applying stable weighting in an automated work-flow renders the approach of assessing urban bike-friendliness fully transferable and the results comparable. It, therefore, lays the foundation for various large-scale cross-sectional analyses.
... There are various ways to operationalize the predominance of motorized traffic on different roads. Previous studies, using the number of car lanes (Evans-Cowley & Akar, 2013), traffic volumes (Foster, Panter, & Wareham, 2011;Vandenbulcke et al., 2011;Wahlgren & Schantz, 2014), permitted speed (Rowangould & Tayarani, 2016), road categories (Abraham, et al., 2002), or a combination of several characteristics (Birk et al., 2010), deliver varying results. Many interventional or cross-sectional studies prove the importance of calmer streets (Caulfield, 2014;Hou et al., 2010; while others do not (Cairns, Warren, Garthwaite, Greig, & Bambra, 2015). ...
Exploring Bikeability
Thesis
  • Oct 2021
Zur Analyse von Zusammenhängen zwischen Radverkehr und Infrastruktur kommt eine breite Kombination unterschiedlicher Methoden in einem integrierten Gesamtansatz zum Einsatz. An die Herleitung der radfahrtauglichen Umgebung (Bikeability) über eine Literaturanalyse und einen interaktiven Expertenprozess schließen sich die Operationalisierung dieser Definition mittels offener Geodaten sowie die Bewertung der Einflüsse auf die Verkehrsmittelwahl in einem multinomialen Verkehrsmittelwahlmodell an. Auf der Ebene der Routenwahl werden dann die Einflussgrößen in einem diskreten Entscheidungsexperiment differenziert. Dabei kommen logistische Regressionsmodelle zum Einsatz. Des Weiteren werden Daten aus der Fahrradnavigation in einem Clusterverfahren genutzt. Im Ergebnis zeigt sich ein konsensuales Verständnis von Bikeability unter Abbildung des Zusammenspiels der fünf wichtigsten infrastrukturellen Parameter. Durch Nutzung offener Geodaten ist der entwickelte Ansatz uneingeschränkt räumlich übertragbar und thematisch adaptierbar. Das Verkehrsmittelwahlmodell belegt den stark positiven Einfluss der Bikeability auf die Wahl des Fahrrades als Verkehrsmittel. Auf der differenzierten Ebene der Routenwahl bestätigt sich der besondere Einfluss der Radinfrastruktur an Hauptverkehrsstraßen. Die Ergebnisse zeigen dabei eine Abstufung im Nutzen für den Radverkehr, die dem Ausmaß der baulichen Trennung vom motorisierten Individualverkehr entspricht, sowie spezifische individuelle und strukturelle Implikationen. Neben Infrastrukturen an Hauptstraßen wird durch die angewandten Methoden auch die generelle Bedeutung von Nebenstraßen verdeutlicht und weiter differenziert. Die Ergebnisse zeigen dabei den enormen Nutzen von Fahrradstraßen aus Sicht der Nutzenden. Die Erkenntnisse bieten spezifische Anknüpfungspunkte, sowohl für weitere Forschung als auch für Planung und Praxis, die in der Arbeit diskutiert werden.
... Many studies have demonstrated that the built environment plays an important role in different types of cycling behavior, such as recreational and commuter cycling (Foster et al., 2011;Fraser and Lock, 2010;Mateo-Babiano et al., 2016;Pengjun Zhao, 2013). However, the effects of the built environment may vary with the type of cycling behavior (Sener et al., 2009). ...
The impacts of the built environment on bicycle-metro transfer trips: A new method to delineate metro catchment area based on people's actual cycling space
Article
Bicycle-metro integration is an efficient method of solving the “last mile” issue around metro stations. Built environment is believed to have a significant effect on cycling behavior. However, transfer cycling around metro stations, as a specific type of cycling behavior, has often been overlooked in transport research. In addition, static contextual units such as circular or street-network buffers are typically used to delineate metro catchment areas of transfer cycling trips. These methods are inaccurate to represent the actual geographic contexts of cycling trips, according to the uncertain geographic context problem (UGCoP). Thus, in this study, bicycle-metro catchment areas are delineated based on aggregating the end points of over three million transfer cycling trips. The impact of the built environment on transfer cycling behavior is also explored. First, we find that the aggregate-points buffer outperforms traditional static buffers in predicting transfer cycling trips. Second, we also identify a high level of spatial heterogeneity in catchment area and transfer cycling density between urban and suburban areas. Third, residential and working population density, bus stop density, and metro stations accessibility have a significant effect on bicycle-metro transfer cycling.
... We suspect this is related to occupation and recreational heat exposure. People less than 65 years old tend to be more active in outdoor activities like cycling, hiking, fishing, and jogging than other age groups (Foster et al., 2011;Lee et al., 2016). This paper has several key limitations. ...
Using social security number to identify sub-populations vulnerable to the health impacts from extreme heat in Florida, U.S
Article
Background Some socioeconomically vulnerable groups may experience disproportionately higher risk of extreme heat illness than other groups, but no study has utilized the presence/absence of a social security number (SSN) as a proxy for vulnerable sub-populations. Methods This study focused on the warm season from 2008 to 2012 in Florida, U.S. With a total number of 8,256,171 individual level health outcomes, we devised separate case-crossover models for five heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease), type of health care visit (emergency department (ED) and hospitalization), and patients reporting/not reporting an SSN. Each stratified model also considered potential effect modification by sex, age, or race/ethnicity. Results Mean temperature raised the odds of five heat-sensitive health outcomes with the highest odds ratios (ORs) for heat-related illness. Sex significantly modified heat exposure effects for dehydration ED visits (Males: 1.145, 95 % CI: 1.137–1.153; Females: 1.110, 95 % CI: 1.103–1.117) and hospitalization (Males: 1.116, 95 % CI: 1.110–1.121; Females: 1.100, 95 % CI: 1.095–1.105). Patients not reporting an SSN between 25 and 44 years (1.264, 95 % CI: 1.192–1.340) exhibited significantly higher dehydration ED ORs than those reporting an SSN (1.146, 95 % CI: 1.136–1.157). We also observed significantly higher ORs for cardiovascular disease hospitalization from the no SSN group (SSN: 1.089, 95 % CI: 1.088–1.090; no SSN: 1.100, 95 % CI: 1.091–1.110). Conclusions This paper partially supports the idea that individuals without an SSN could experience higher risks of dehydration (for those 25–45 years), renal disease, and cardiovascular disease than those with an SSN.
How do design aspects influence the attractiveness of cycling streetscapes: Results of virtual reality experiments in the Netherlands
Article
Understanding the determinants of cycling and thus creating optimal cycling conditions is still a challenge. The current study addresses this challenge by providing in-depth exploration of attributes of bicycle infrastructure, traffic volume, gradients, urbanisation degrees in stimulating cycling for various population categories. Participants had to cycle in a simulated VR environment mirroring the streetscape of a real Dutch city. The cognitive (e.g., safety perception) and affective (e.g., enjoyment, attractiveness) response was measured, real time. The results suggest that various attributes impact the cognitive and affective components to different extents. In particular, bicycle path presence and intersection absence had a positive impact on safety perception. Greenness of the environment contributed for lifting the attractiveness of the cycling experience. Hight car traffic had a negative impact on the way safety, enjoyment and attractiveness of cycling was perceived. Current outcomes should be implemented in creating bicycle infrastructure that appropriately meets the demand for attractive cycling experience that is safe and enjoyable for all.
Infrastructure and Injury Prevention in Cycling
Article
The focus of this article centers on bicycle injury prevention and related infrastructure. The article discusses the current epidemiology of cycling injuries, and known prevention strategies, specifically individual recommended practices related to helmet use in both adult and pediatric populations. The article also discusses different ways in which the environment plays a role in protecting cyclists from injuries, and what environmental changes have been adopted to reduce the likelihood for cycling injuries.
Facilitators and barriers to cycling in older residents of New South Wales, Australia
Article
  • Jun 2021
Objective To investigate facilitators and barriers to all types of cycling in adults aged ≥50 years. Methods An online survey of 1335 cyclists aged ≥50 years residents of New South Wales (NSW), Australia. Results Almost all participants (98.5%) reported physical health and fitness as a reason for riding a bicycle, followed by mental health (68.1%), social (58.3%) and environmental reasons (44%). Top reported barriers to cycling included motorist behaviour or aggression (34.4%), speed and volume of traffic (27.1%), proximity to motor vehicle traffic (26%) and not enough separated bike lanes (22.7%). Females and occasional riders were significantly more likely to report these barriers than men and regular riders respectively. Key facilitators included improved attitudes towards cyclists compared to current attitudes (69.5%), separate bike lanes (63.4%), education and training of motorists (57.5%). Discussion Strategies designed to improve cycling participation in older adults need to address barriers to cycling and to tailor interventions for under-represented groups such females.
The Effectiveness of Urban Design and Land Use and Transport Policies and Practices to Increase Physical Activity: A Systematic Review
Article
Full-text available
  • Feb 2006
Background: Although a number of environmental and policy interventions to promote physical activity are being widely used, there is sparse systematic information on the most effective approaches to guide population-wide interventions. Methods: We reviewed studies that addressed the following environmental and policy strategies to promote physical activity: community-scale urban design and land use policies and practices to increase physical activity; street-scale urban design and land use policies to increase physical activity; and transportation and travel policies and practices. These systematic reviews were based on the methods of the independent Task Force on Community Preventive Services. Exposure variables were classified according to the types of infrastructures/policies present in each study. Measures of physical activity behavior were used to assess effectiveness. Results: Two interventions were effective in promoting physical activity (community-scale and street-scale urban design and land use policies and practices). Additional information about applicability, other effects, and barriers to implementation are provided for these interventions. Evidence is insufficient to assess transportation policy and practices to promote physical activity. Conclusions: Because community- and street-scale urban design and land-use policies and practices met the Community Guide criteria for being effective physical activity interventions, implementing these policies and practices at the community-level should be a priority of public health practitioners and community decision makers.
Shoe leather epidemiology: Active travel and transport infrastructure in the urban landscape
Article
Full-text available
Building new transport infrastructure could help to promote changes in patterns of mobility, physical activity, and other determinants of population health such as economic development. However, local residents may not share planners' goals or assumptions about the benefits of such interventions. A particularly contentious example is the construction of major roads close to deprived residential areas. We report the qualitative findings of the baseline phase of a longitudinal mixed-method study of a new urban section of the M74 motorway in Glasgow, Scotland, that aims to combine quantitative epidemiological and spatial data with qualitative interview data from local residents. We interviewed 12 residents purposively sampled from a larger study cohort of 1322 to include men and women, different age groups, and people with and without cars, all living within 400 metres of the proposed route of the new motorway. We elicited their views and experiences of the local urban environment and the likely impact of the new motorway using a topic guide based on seven key environmental constructs (aesthetics, green space, convenience of routes, access to amenities, traffic, road danger and personal danger) reflecting an overall ecological model of walking and cycling. Traffic was widely perceived to be heavy despite a low local level of car ownership. Few people cycled, and cycling on the roads was widely perceived to be dangerous for both adults and children. Views about the likely impacts of the new motorway on traffic congestion, pollution and the pleasantness of the local environment were polarised. A new motorway has potential to cause inequitable psychological or physical severance of routes to local amenities, and people may not necessarily use local walking routes or destinations such as parks and shops if these are considered undesirable, unsafe or 'not for us'. Public transport may have the potential to promote or discourage active travel in different socioeconomic contexts. Altering the urban landscape may influence walking and cycling in ways that vary between individuals, may be inequitable, and may not be predictable from quantitative data alone. A more applied ecological behavioural model may be required to capture these effects.
Validity and repeatability of the EPIC-Norfolk Physical Activity Questionnaire
Article
Background Physical activity is an important lifestyle which is often poorly assessed in epidemiological studies. The European Prospective Investigation into Cancer Study-Norfolk cohort (EPIC-Norfolk), a large population-based cohort study, has developed a comprehensive questionnaire to assess activity in different domains of life aimed at assessing total energy expenditure. We report the repeatability of this instrument and its validity against repeated objective measures of fitness and energy expenditure undertaken throughout the time frame of reference of the questionnaire. Methods The validity of the instrument was measured in 173 individuals randomly selected from a continuing population-based cohort study. Energy expenditure was assessed by four separate episodes of 4-day heart-rate monitoring, a method previously validated against whole body calorimetry and doubly-labelled water. Cardio-respiratory fitness was assessed by four repeated measures of sub-maximum oxygen uptake. At the end of the 12-month period, participants completed the physical activity questionnaire that assesses past-year activity at home, work and in recreation. Repeatability was assessed in a separate group of 399 randomly selected participants in EPIC who completed the physical activity questionnaire twice with a 3-month interval. Results The age- and sex-adjusted correlation between the objective measure of daytime energy expenditure and the sum of recreational and occupational reported physical activity (in MET h per week) was 0.28 (P , 0.001). The reported time spent in vigorous activity was correlated with cardio-respiratory fitness (0.16, P , 0.05) and with the proportion of time when energy expenditure was more than five times basal (0.17, P , 0.05). The repeatability of the sum of recreational and occupational reported activity was high, r = 0.73. Conclusions The indices of physical activity derived from this questionnaire have levels of validity and repeatability comparable to other physical activity instruments that are used in large epidemiological studies and which have undergone such intense development and testing.
Children, Transport and the Quality of Life
Article
  • Feb 1994
Road traffic has doubled in the past 20 years but far fewer children have accidents. Yet this book argues that roads have become increasingly dangerous: improvement has come by withdrawing children from exposure to traffic, thereby impoverishing their lives. The book is based on a Policy Studies Institute conference that debated the study One False Move: A Study of Children's Independent Mobility. Surveys showed that children walked, cycled, and used public transport strikingly less and relied on the car more, and also had fewer leisure activities, in 1990 than 1971. Is traffic to …
Associations between Self-Reported and Objective Physical Environmental Factors and Use of a Community Rail-Trail
Article
  • Mar 2001
Background: To effectively promote physical activIty, researchers and policy makers have advocated for greater use of environmental approaches, such as the construction of community paths and trails. However, research on the use of these facilities is limited. Methods: In this cross-sectional community study, we examined associations between self-reported and objective physical environmental variables and use of the Minuteman Bikeway (Arlington, MA) in a random sample of 413 adults. Sociodemographic and perceived environmental variables were measured with a mail survey during September 1998. Geographic information system (GIS) data were used to geocode survey respondents' homes and create three objective environmental variables: distance to the Bikeway, steep hill barrier, and a busy street barrier. Results: In logistic models, age and female gender showed statistically significant inverse associations with Bikeway use over the previous 4-week period. Increases in self-reported (OR = 0.65) and GIS distance (OR = 0.57) were associated with decreased likelihood of Bikeway use. Absence of self-reported busy street (OR = 2.01) and GIS steep hill barriers (OR = 1.84) were associated with Bikeway use. Conclusions: Environmental barriers such as travel distance and hilly terrain should be considered when planning community trails. A better understanding of such factors may lead to more effective promotion of trail use.
Attitudes and the Environment as Determinants of Active Travel in Adults: What Do and Don't We Know?
Article
  • Jul 2010
Walking and cycling for transport, or 'active travel,' has the potential to contribute to overall physical activity levels. However, a wide range of factors are hypothesized to be associated with adult's active travel behavior. This paper describes current knowledge of the psychological and environmental determinants of active travel in adults, and considers ways in which the 2 domains can be better integrated. Quantitative studies were reviewed which examined psychological and environmental influences on active travel in an adult population. Studies were classified according to whether they examined psychological, environmental or both types of factor. Fourteen studies were identified which examined psychological correlates of active travel behavior in adults, and 36 which examined environmental correlates. Seven studies were identified which considered both domains, of which only 2 of explored the interactions between personal, social and environmental factors. The majority of the evidence is helpful in identifying correlates rather than determinants of active travel behavior. To further our understanding of the influences of active travel, there is a need for more research which integrates both individual and environmental domains and examines how they interact.
Associations Between Intrapersonal and Neighborhood Environmental Characteristics and Cycling for Transport and Recreation in Adults: Baseline Results From the RESIDE Study
Article
  • Jul 2010
This study investigated the relationship between individual and neighborhood environmental factors and cycling for transport and for recreation among adults living in Perth, Western Australia. Baseline cross-sectional data from 1813 participants (40.5% male; age range 18 to 78 years) in the RESIDential Environment (RESIDE) project were analyzed. The questionnaire included information on cycling behavior and on cycling-specific individual, social environmental, and neighborhood environmental attributes. Cycling for transport and recreation were dichotomized as whether or not individuals cycled in a usual week. Among the individual factors, positive attitudes toward cycling and perceived behavioral control increased the odds of cycling for transport and for recreation. Among the neighborhood environmental attributes, leafy and attractive neighborhoods, access to bicycle/walking paths, the presence of traffic slowing devices and having many 4-way street intersections were positively associated with cycling for transport. Many alternative routes in the local area increased the odds of cycling for recreation. Effective strategies for increasing cycling (particularly cycling for transport) may include incorporating supportive environments such as creating leafy and attractive neighborhood surroundings, low traffic speed, and increased street connectivity, in addition to campaigns aimed at strengthening positive attitudes and confidence to cycle.