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International Journal of
Environmental Research
and Public Health
Article
Distance, Duration, and Velocity in Cycle Commuting:
Analyses of Relations and Determinants of Velocity
Peter Schantz 1,2 ID
1Research Unit for Movement, Health and Environment, The Åstrand Laboratory, The Swedish School of
Sport and Health Sciences, GIH, SE-114 86 Stockholm, Sweden; peter.schantz@gih.se; Tel.: +46-8-12053818
2Unit for Occupational and Environmental Medicine, Department of Public Health and Clinical Medicine,
Umeå University, SE-901 87 Umeå, Sweden
Received: 17 August 2017; Accepted: 26 September 2017; Published: 2 October 2017
Abstract:
Background: The distance, duration, and velocity of cycling for transport purposes are
used in health economic assessments, epidemiological studies, traffic modelling, and planning. It is
therefore of value to determine relevant levels for them, and analyze how they relate, as well as to
what extent other relevant variables may affect cycling velocities. 1661 cycle commuters (34% males)
in Greater Stockholm, Sweden have been studied for that purpose. Methods: The participants were
recruited with advertisements. They received questionnaires and individually adjusted maps to draw
their normal cycling route. Route distances were measured by a criterion method. Age, sex, weight,
height, and cycling durations to work were self-reported. The commuting routes were positioned in
relation to inner urban and/or suburban–rural areas. Linear multiple regression analyses were used.
Results: Cycling speeds were positively related to commuting distances or durations, being male,
of younger age, having higher body weight but lower body mass index (BMI), and using the last digits
1–4 or 6–9 in duration reports (as compared to 0 and 5), as well as cycling in suburban (versus inner
urban) areas. Conclusions: The study provides new knowledge about how distance and duration,
as well as other factors, relate to the velocity of commuter cycling. It thereby enables the use of
more appropriate input values in, for instance, health economic assessments and epidemiological
health studies.
Keywords: cycling; commuting; distance; duration; velocity; environment; sex; age; body weight; BMI
1. Introduction
Distance, duration, and speed are basic characteristics of mobility in every mode of transport.
To describe them is of interest in and of itself, but also from other perspectives. They are, for instance,
used in traffic modelling and planning, as well as in evaluations of environmental, health, and economic
effects. Cycling and walking have been less studied in these respects than the motorised forms of
transport. Still, these variables represent important inputs in, e.g., cost-benefit analyses and health
economic assessment tools for walking and cycling (e.g., [
1
] (p. 85) and [
2
]), as well as in selecting the
metabolic equivalent of task (MET) values [
3
] and the intensity category for epidemiological studies
on the relation between physical activity and health. Thus, it is important to develop more knowledge
about distance, duration, and velocity in walking and cycling, for instance determine relevant levels
for them, analyze how they relate, and to what extent other relevant variables may affect them. Here,
focus is on cycling.
A great variability in cycling velocities has been reported in the literature (cf. [
4
,
5
]). This may
be due to, for instance, the methodology used, the different cycling purposes, distances, durations,
and characteristics of the cyclists, and the environments in which they cycle. Thus, there is a need to
distinguish within both methodological issues and conceivable determinants of cycling speed.
Int. J. Environ. Res. Public Health 2017,14, 1166; doi:10.3390/ijerph14101166 www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2017,14, 1166 2 of 14
Distance is difficult to measure. Self-reports on distances are generally too long and wide-ranging [
6
–
8
].
This is also true for active commuters [
9
]. Furthermore, objective methods based on known origins and
destinations for trips and route choice modelling with geographic information systems (GIS) or global
positioning systems (GPS) have been shown to overestimate cycling distances by about 5–20% [
9
].
However, a method that has been shown to be valid for distance measurements in active commuting
is to let the commuters draw the routes taken and measure them using valid distance measuring
techniques [10].
It is also a challenge to obtain valid durations of trips. The phenomenon of a preferential rounding
off to the last digit values of 0 and 5 in self-reported trip durations (e.g., 10 and 25 min) (cf. [
11
])
indicates problems with validity in and of itself. However, this could be coupled to true duration
values being more or less symmetrically gathered around the last digits 0 and 5, respectively. But since
most studies indicate that self-reported durations are longer than those derived from GPS (cf. [
12
]),
a more reasonable interpretation is that there is a systematic over-reporting of durations associated
with rounding off to the last digits 0 and 5. This has also been noted when cyclists’ trips were followed
with cameras [
13
]. One’s behaviour when reporting durations might, however, differ depending on
the reason for the trip. It is possible that active commuting represents a type of trip in which both
departure and arrival times are better known, possibly leading to more accurate reports on duration.
Furthermore, these behaviours are normally repeated many times over the year [
14
]. To the best of my
knowledge, it has not been reported whether preferential rounding off of the last digits in duration
reports to multiples of 5 occurs among habitual cycle commuters, and, if so, to what extent. This issue
is therefore analysed in this study.
Given that multiples of 5 in stating durations is almost the rule in some transport contexts (cf. [
11
]),
it is hypothesized that individuals reporting durations with the last digits 1–4 and 6–9 (e.g., 13 and
28 min) may present more correct values. There is also support for this [
13
]. The hypothesis will be
further scrutinised here by checking whether there are systematic differences in the estimated velocity
of cycling depending on the last digit category in self-reported durations.
It was recently noted that a group of commuter cyclists, with longer distances than another group,
also had higher cycling velocities [
14
]. This opens up the possibility of there being a positive relation
between cycling distance and speed. In fact, another report indicates that this may be the case, but it
was based on a very small sample [
5
]. In order to further scrutinise this possible relationship, other
factors that may affect cycling velocity need to be considered. The power output in cycling is linearly
related to oxygen uptake (e.g., [
15
]) and, in competitive cycling, the maximal oxygen uptake is an
important predictor of physical performance [
16
,
17
]. The levels of that measure are related to sex and
age [
18
,
19
] and can be taken into account in this study. Furthermore, differences in body mass may
affect cycling velocity in various ways [
20
]. One of them is related to the fact that cycling involves
transport of both a body and a vehicle and that a given weight of a bicycle will lead to a greater relative
added weight for a light person than for a heavy one, which favours the cycling velocity of heavier
persons. A higher body weight and body mass index (BMI) will, on the other hand, act in the opposite
direction through increased energy demands at a given workload and pedalling frequency [
21
,
22
].
Also, the environment cycled in might induce different cycling velocities due to differences in the
levels of route environmental variables (e.g., [23]).
Given this background, the overall aim of this study was to analyse whether cycling velocity
was related to route distance or self-reported duration, sex, age, body weight, BMI, the last digit
category (1–4 and 6–9 vs. 0 and 5) in duration reports, and the cycling environment. The study was
undertaken in a stepwise explorative fashion, and is based on 1661 male and female cycle commuters
in the metropolitan area of Greater Stockholm, Sweden.
2. Materials and Methods
This study is part of a greater multidisciplinary research project, Physically Active Commuting in
Greater Stockholm (PACS), at the Swedish School of Sport and Health Sciences, GIH, in Stockholm,
Int. J. Environ. Res. Public Health 2017,14, 1166 3 of 14
Sweden [
24
]. In this study, only fully active cycling is analysed, i.e., no cycling with electrically
assisted bicycles (e-bikes) is considered. Approval to conduct this study was obtained from the Ethics
Committee North of the Karolinska Institute at the Karolinska Hospital (Dnr 03-637), Stockholm,
Sweden, and the participants gave their written informed consent.
2.1. Participants
Advertisements in two morning newspapers in Stockholm (Dagens Nyheter and Svenska
Dagbladet) in 2004 resulted in 2148 responders. No incentives were provided. The criteria for
inclusion were: a minimum age of 20 years, residency in the County of Stockholm (except for the
municipality of Norrtälje), and to, at least once a year, cycle or walk the whole way to one’s place
of work or study. It was emphasised that also persons with very short commuting distances were
welcome to participate.
2.2. Questionnaires, Administration, Response Rates, and Inclusion Criteria
The participants responded to a paper-based questionnaire created for the study: Physically
Active Commuting in Greater Stockholm (PACS Q1). It was pre-tested on a small convenience sample
of academic staff members as part of the development. PACS Q1 is self-administered, in Swedish, and
contains 35 items, only a few of which are used in this study, namely: sex, height, weight, year of birth
and cycle commuting durations to work.
Out of the original 2148 responders to the advertisement, 133 did not return the PACS Q1, giving
a response rate of 93.8%. Twenty-one of the responders did not meet the inclusion criteria, 276 were
single-mode pedestrians and therefore were not included in this study, and 57 were excluded due to
missing values in at least one of the studied variables. This left 1661 cycle commuting participants for
this study.
2.3. Maps and Route Distance Measurements
An individually adjusted map was prepared based on each respondent
´
s written home and place
of work or study addresses. The map was a black and white copy of the maps in the official telephone
directory of Stockholm (scale 1:25,000). In a few cases, however, the relevant area was not found in that
collection of maps, so the national outdoor map (scale 1:50,000) was used instead. The respondents
were given instructions on how to draw their most usual cycle commuting route to and from their
place of work or study on the map. In case of two or more places of work or study, they were asked to
pick the one they spent the most time at, and, in the case of equal time spent, to choose one of them.
However, before filling in the routes, they were asked to commute along their route once, and to note
the street names. Finally, in case their routes included places outside the printed street grid network,
such as parkways or tunnels, the cyclists were asked to mark their routes with specific carefulness.
Furthermore, the homes were marked with the capital letter B, and a small box marking indicated their
places of work or study. For further descriptions of the method, see Schantz & Stigell [10].
The maps were used to measure route distances using a criterion method with high validity and
reproducibility [
10
]. For that purpose, a digital curvimetric instrument (Run Mate Club, CST/Berger,
Watseka, IL, USA) was used by a technical assistant in two consecutive distance measurements. A third
measurement was undertaken in 151 cases when differences between the first two measurements were
greater than 5.8
±
3.3 mm (mean
±
SD), corresponding to 145
±
82.5 m in real distance. The mean
value of the two closest values of the three was used.
2.4. Commuting Durations and Estimations of Cycling Velocity
Participants were asked to record the times for their commuting trips on a normal day and when
no other errands were undertaken. They wrote down their commuting time to their place of work or
study in hours and minutes. The cycling velocity was calculated as a function of the map-measured
trip distances and the self-reported durations.
Int. J. Environ. Res. Public Health 2017,14, 1166 4 of 14
2.5. Localisation of Trip Origins and Destinations in Relation to Inner Urban and Suburban Areas
Perceived levels of route environmental variables differ in many cases between the inner urban
and suburban areas [
23
], which may affect the velocity of cycling (see also the study area description
below). A distinction was therefore made concerning where within the study area the cyclists’ routes
were located. It was based on the postal area codes in the participants’ home addresses and places
of work or study. In that way, the origins and destinations of the trips were categorised in relation to
their being in the inner urban or the suburban areas.
2.6. Study Area
The study area is the County of Stockholm, Sweden, except for the municipality of Norrtälje. In it,
cyclists represent about 5% of the commuting trips, and the share of car drivers is about 40% [
25
].
This metropolitan area, with about 1.9 million inhabitants at the time of the study, consists of inner
urban, suburban, and rural areas. The latter two areas will be referred to in the following as ‘suburban
areas’. The boundary between the inner urban and suburban areas is shown in Wahlgren et al. [
26
].
In the year of data collection for this study, about 285,000 people lived in the inner urban area [
27
].
It also had a high density of workplaces and employees and is socio-economically linked with the
surrounding territory by, for instance, commuting. For a detailed environmental description of the
study area, see Wahlgren and Schantz [
23
]. Here, the topography, urban form, and residential density
are described since these variables can affect cycle commuting speeds.
The natural landscape in the region is basically rather flat. The road system includes, however,
rather frequently smaller and gentle slopes, and occasionally also more demanding hills of up to about
15 m in height. The inner urban area is a predominantly built-up area, with areal blocks in a grid-like
pattern and a high intersection density. The residential density was approximately 13,000 residents
per square km in the inner urban area [
27
]. The suburban and rural areas contained a mixture of
residential areas, smaller industrial areas, and managed forests, as well as agricultural land. The streets
are not normally laid out in a grid-like pattern. Instead, the main roads often follow old road networks
formed during the agricultural period of the landscape, and the density of intersections, as well
as traffic regulatory signaling systems (traffic lights), is clearly lower than in the inner urban area.
The residential density of the suburban parts of the study area is indicated by two representative
examples: the southern and western suburbs of the Municipality of Stockholm with approximately
3500 and 2900 residents per square km, respectively [27].
2.7. Characteristics of the Participants
The characteristics of the participants with regard to age, height, weight, BMI, duration of cycling,
distance, velocity, and cycling environment are described for the male and female commuter cyclists in
Table 1.
Table 1. Participant characteristics (median and 1st–3rd quartile) (n= 1661).
Sex Age
Years
Height
cm
Weight
kg
BMI
kg·m−1
Distance
m
Duration
min
Velocity
km·h−1Cycling Environment *
Male
(n= 562) 47
38–57 180
176–185 78
72–84 23.9
22.4–25.4
7794
4594–12,790
25
19–40 17.9
14.7–21.3
I = 94
I–S = 277
S = 191
Female
(n= 1099)
47
39–55 168
164–172 64
59–70 22.6
21.0–24.4 4900
3000–8050
20
15–33 14.0
11.6–16.5
I = 242
I–S = 388
S = 469
Note: * Number of cyclists in: I = inner urban; I–S = inner urban–suburban; S = suburban.
Int. J. Environ. Res. Public Health 2017,14, 1166 5 of 14
2.8. Analytical Approach and Statistical Analyses
Questionnaire data were entered in the Statistical Package for the Social Sciences and analysed
in version 24.0 (IBM SPSS Inc., Somers, NY, USA). Distributions were checked for normality with
the Kolmogorov-Smirnov test, and, based on the outcomes and the issues addressed, the values are
stated as median values and first–third quartile, or means and 80% confidence intervals. Expected
and detected distributions of last digits in self-reported durations (0 and 5 versus 1–4 and 6–9) were
evaluated using chi-square analyses for each sex. For evaluating the effect of the last digit category
on the estimated cycling velocities, the individual duration values were grouped in consecutively
numbered clusters based on whether the last digits were 1–4 (=cluster 1), 5 (=cluster 2), 6–9 (=cluster
3), and 10 (=cluster 4) and so forth. The first two clusters (1–4 and 5 min) were omitted in the analyses
since there were few individuals in the first cluster, and stating trip durations using full-minute reports
can represent greater relative errors in these first two clusters compared to those that follow thereafter.
Given that the number of participants became less than 11 in duration clusters representing more than
50 min and durations with the last digits 1–4 or 6–9, only durations
≤
50 min were used. After these
analyses, the relations between age, body weight, BMI, and category of cycling environment were
plotted for each sex as error bars for the different duration clusters. This was done to check for any
systematic differences among the clusters in variables that could affect the cycling velocity. It provided
motives for using multiple regression analyses.
Before they were applied, linearity between the outcome cycling velocity and predictors with
continuous variables was checked using scatter dots and lines fitting the values. Interrelations
between the continuous variables were assessed with Pearson’s correlation coefficient. The variables
distance and duration were highly correlated (r = 0.89), but they were not included in the same
multiple regression analysis. The correlation coefficients for the remainder of the predictors were, in
absolute values, r
≤
0.76, thus indicating no problems regarding multicollinearity. As a limit, we used
r > 0.80 [28] (p. 175).
Linear multiple regression models were used, and the predictors were entered simultaneously
as a group. All durations
≤
50 min were used in these analyses. Cycling speed was the outcome
in both models, whereas the predictors were distance (model 1) or duration (model 2), and sex
(reference = females, 1 = males), age, body weight, body mass index, the categories of the last
digit in the self-reported durations (dichotomous categorical responses: reference = last digits 0
or 5; 1 = last digits 1–4 or 6–9), as well as cycling environment (three mutually exclusive dummy
categories: suburban; suburban and inner urban; and inner urban, with suburban as a reference) (both
model 1 and 2). The variance inflation factor (VIF) was used to check multicollinearity. The VIFs
(all values
≤
4.67; mean, 2.02) indicated no problems. Possible extreme data cases were identified
using Cook’s distance. No such cases were found in either one of the models (all values
≤
0.018;
mean, 0.0007). Thus, conditions for multiple regression analyses existed. The results are presented as
unstandardised regression coefficients B, 95% confidence interval (CI), level of significance and partial
correlations, as well as R-squared (R
2
) for the overall models. To indicate significance, a statistical level
corresponding to at least p< 0.05 was used.
3. Results
3.1. The Relation between Distance and Duration for Commuter Cycling Velocity
The overall relations between distance and duration for commuter cycling velocity are illustrated
in Figures 1and 2, respectively. Lines fitted to the values show that the estimated cycling velocities
increased with both distance and duration. These positive relations appear to be steeper from low
levels up to distances and durations of about 15 km and 45 min, respectively, whereas those after that
are lower.
Int. J. Environ. Res. Public Health 2017,14, 1166 6 of 14
Int. J. Environ. Res. Public Health 2017, 14, 1166 6 of 14
Figure 1. The relation between criterion-measured route distances and estimated velocities for
commuter cyclists (n = 1661, 34% males). The line fits 85% of the individual values.
Figure 2. The relation between self-reported durations and estimated velocities for commuter cyclists
(n = 1661, 34% males). The line fits 85% of the individual values.
3.2. The Distribution of Self-Reported Cycling Durations
The accumulated number of self-reported commuting durations is illustrated in Figure 3. A
preferential use of 0 and 5 as the last digits is evident. A comparison between the expected and
detected proportions of the last digits 0 and 5 versus 1–4 and 6–9 reveal disproportionately high
distributions of the last digits 0 and 5 in both males and females (p < 0.001) (Table 2).
Table 2. Expected and detected distributions of last digit categories in male and female commuter
cyclists (n = 1661). The absolute number in each group is also indicated.
Last Digits in Self-
Reported Cycle
Trip Durations
Males
Females
Expected Distribution
% (Number)
Detected Distribution
% (Number)
Expected Distribution
% (Number)
Detected Distribution
% (Number)
0 or 5
20% (112)
69% (387)
20% (220)
75% (819)
1–4 or 6–9
80% (450)
31% (175)
80% (879)
25% (280)
Figure 1.
The relation between criterion-measured route distances and estimated velocities for
commuter cyclists (n= 1661, 34% males). The line fits 85% of the individual values.
Int. J. Environ. Res. Public Health 2017, 14, 1166 6 of 14
Figure 1. The relation between criterion-measured route distances and estimated velocities for
commuter cyclists (n = 1661, 34% males). The line fits 85% of the individual values.
Figure 2. The relation between self-reported durations and estimated velocities for commuter cyclists
(n = 1661, 34% males). The line fits 85% of the individual values.
3.2. The Distribution of Self-Reported Cycling Durations
The accumulated number of self-reported commuting durations is illustrated in Figure 3. A
preferential use of 0 and 5 as the last digits is evident. A comparison between the expected and
detected proportions of the last digits 0 and 5 versus 1–4 and 6–9 reveal disproportionately high
distributions of the last digits 0 and 5 in both males and females (p < 0.001) (Table 2).
Table 2. Expected and detected distributions of last digit categories in male and female commuter
cyclists (n = 1661). The absolute number in each group is also indicated.
Last Digits in Self-
Reported Cycle
Trip Durations
Males
Females
Expected Distribution
% (Number)
Detected Distribution
% (Number)
Expected Distribution
% (Number)
Detected Distribution
% (Number)
0 or 5
20% (112)
69% (387)
20% (220)
75% (819)
1–4 or 6–9
80% (450)
31% (175)
80% (879)
25% (280)
Figure 2.
The relation between self-reported durations and estimated velocities for commuter cyclists
(n= 1661, 34% males). The line fits 85% of the individual values.
3.2. The Distribution of Self-Reported Cycling Durations
The accumulated number of self-reported commuting durations is illustrated in Figure 3.
A preferential use of 0 and 5 as the last digits is evident. A comparison between the expected
and detected proportions of the last digits 0 and 5 versus 1–4 and 6–9 reveal disproportionately high
distributions of the last digits 0 and 5 in both males and females (p< 0.001) (Table 2).
Int. J. Environ. Res. Public Health 2017,14, 1166 7 of 14
Int. J. Environ. Res. Public Health 2017, 14, 1166 7 of 14
Figure 3. The frequency distribution of individual self-reported cycle commuting durations (n = 1661).
The duration values were divided into consecutively numbered clusters of last digit categories:
cluster 1 = last digits 1–4; cluster 2 = 5; cluster 3 = 6–9; cluster 4 = 10; cluster 5 = 11–14, and so forth up
to cluster 20, which represented the 50-min duration. The sex distribution was analysed in clusters
3–20 (Figure 4). It showed that males in general report durations with the last digits 1–4 and 6–9 to a
greater extent than females, and that this is even clearer with increasing trip duration.
Figure 4. The relative proportions of data from male participants in clusters of self-reported cycling
durations (n = 1516). The range of clusters (3–20) represents durations from 6–9 min to 50 min. The
even-numbered clusters represent durations with the last digits being 0 or 5 (red symbols connected
with continuous lines). The uneven-numbered clusters represent durations with the last digits being
1–4 or 6–9 (blue symbols connected with dashed lines). Cluster 4 = 10 min; 8 = 20 min; 12 = 30 min; 16
= 40 min; and 20 = 50 min. For further explanations, see Methods.
Figure 3.
The frequency distribution of individual self-reported cycle commuting durations (n= 1661).
Table 2.
Expected and detected distributions of last digit categories in male and female commuter
cyclists (n= 1661). The absolute number in each group is also indicated.
Last Digits in
Self-Reported Cycle
Trip Durations
Males Females
Expected Distribution
% (Number)
Detected Distribution
% (Number)
Expected Distribution
% (Number)
Detected Distribution
% (Number)
0 or 5 20% (112) 69% (387) 20% (220) 75% (819)
1–4 or 6–9 80% (450) 31% (175) 80% (879) 25% (280)
The duration values were divided into consecutively numbered clusters of last digit categories:
cluster 1 = last digits 1–4; cluster 2 = 5; cluster 3 = 6–9; cluster 4 = 10; cluster 5 = 11–14, and so forth up
to cluster 20, which represented the 50-min duration. The sex distribution was analysed in clusters
3–20 (Figure 4). It showed that males in general report durations with the last digits 1–4 and 6–9 to
a greater extent than females, and that this is even clearer with increasing trip duration.
Int. J. Environ. Res. Public Health 2017, 14, 1166 7 of 14
Figure 3. The frequency distribution of individual self-reported cycle commuting durations (n = 1661).
The duration values were divided into consecutively numbered clusters of last digit categories:
cluster 1 = last digits 1–4; cluster 2 = 5; cluster 3 = 6–9; cluster 4 = 10; cluster 5 = 11–14, and so forth up
to cluster 20, which represented the 50-min duration. The sex distribution was analysed in clusters
3–20 (Figure 4). It showed that males in general report durations with the last digits 1–4 and 6–9 to a
greater extent than females, and that this is even clearer with increasing trip duration.
Figure 4. The relative proportions of data from male participants in clusters of self-reported cycling
durations (n = 1516). The range of clusters (3–20) represents durations from 6–9 min to 50 min. The
even-numbered clusters represent durations with the last digits being 0 or 5 (red symbols connected
with continuous lines). The uneven-numbered clusters represent durations with the last digits being
1–4 or 6–9 (blue symbols connected with dashed lines). Cluster 4 = 10 min; 8 = 20 min; 12 = 30 min; 16
= 40 min; and 20 = 50 min. For further explanations, see Methods.
Figure 4.
The relative proportions of data from male participants in clusters of self-reported cycling
durations (n= 1516). The range of clusters (3–20) represents durations from 6–9 min to 50 min.
The even-numbered clusters represent durations with the last digits being 0 or 5 (red symbols connected
with continuous lines). The uneven-numbered clusters represent durations with the last digits being
1–4 or 6–9 (blue symbols connected with dashed lines). Cluster 4 = 10 min; 8 = 20 min; 12 = 30 min;
16 = 40 min; and 20 = 50 min. For further explanations, see Methods.
Int. J. Environ. Res. Public Health 2017,14, 1166 8 of 14
3.3. The Relation between the Self-Reported Duration Last Digit Category and the Estimated Cycling Velocities
with Gender
Given that the proportion of responses in last digit categories differed between the sexes (Figure 4),
the analysis was furthered through sex-specific analyses. An overall trend towards higher estimated
cycling speeds with longer commuting durations was noted in both sexes, as well as in the last digit
duration clusters with 1–4 or 6–9, as compared to 0 or 5 (Figures 5and 6).
Int. J. Environ. Res. Public Health 2017, 14, 1166 8 of 14
3.3. The Relation between the Self-Reported Duration Last Digit Category and the Estimated Cycling
Velocities with Gender
Given that the proportion of responses in last digit categories differed between the sexes (Figure
4), the analysis was furthered through sex-specific analyses. An overall trend towards higher
estimated cycling speeds with longer commuting durations was noted in both sexes, as well as in the
last digit duration clusters with 1–4 or 6–9, as compared to 0 or 5 (Figures 5 and 6).
Figure 5. The relation between consecutive clusters of self-reported cycling durations and the
estimated cycling velocities for males (mean value and 80% confidence interval) (n = 498). The even-
numbered clusters represent durations with the last digits being 0 or 5 (red symbols connected with
continuous lines). The uneven-numbered clusters represent durations with the last digits being 1–4
or 6–9 (blue symbols connected with dashed lines). For further explanations, see legend to Figure 4
and Methods.
Figure 6. The relation between consecutive clusters of self-reported cycling durations and the
estimated cycling velocities in females (mean value and 80% confidence interval) (n = 1018). The even-
numbered clusters represent durations with the last digits being 0 or 5 (red symbols connected with
continuous lines). The uneven-numbered clusters represent durations with the last digits being 1–4
or 6–9 (blue symbols connected with dashed lines). For further explanations, see legend to Figure 4
and Methods.
Figure 5.
The relation between consecutive clusters of self-reported cycling durations and the estimated
cycling velocities for males (mean value and 80% confidence interval) (n= 498). The even-numbered
clusters represent durations with the last digits being 0 or 5 (red symbols connected with continuous
lines). The uneven-numbered clusters represent durations with the last digits being 1–4 or 6–9 (blue
symbols connected with dashed lines). For further explanations, see legend to Figure 4and Methods.
Int. J. Environ. Res. Public Health 2017, 14, 1166 8 of 14
3.3. The Relation between the Self-Reported Duration Last Digit Category and the Estimated Cycling
Velocities with Gender
Given that the proportion of responses in last digit categories differed between the sexes (Figure
4), the analysis was furthered through sex-specific analyses. An overall trend towards higher
estimated cycling speeds with longer commuting durations was noted in both sexes, as well as in the
last digit duration clusters with 1–4 or 6–9, as compared to 0 or 5 (Figures 5 and 6).
Figure 5. The relation between consecutive clusters of self-reported cycling durations and the
estimated cycling velocities for males (mean value and 80% confidence interval) (n = 498). The even-
numbered clusters represent durations with the last digits being 0 or 5 (red symbols connected with
continuous lines). The uneven-numbered clusters represent durations with the last digits being 1–4
or 6–9 (blue symbols connected with dashed lines). For further explanations, see legend to Figure 4
and Methods.
Figure 6. The relation between consecutive clusters of self-reported cycling durations and the
estimated cycling velocities in females (mean value and 80% confidence interval) (n = 1018). The even-
numbered clusters represent durations with the last digits being 0 or 5 (red symbols connected with
continuous lines). The uneven-numbered clusters represent durations with the last digits being 1–4
or 6–9 (blue symbols connected with dashed lines). For further explanations, see legend to Figure 4
and Methods.
Figure 6.
The relation between consecutive clusters of self-reported cycling durations and the estimated
cycling velocities in females (mean value and 80% confidence interval) (n= 1018). The even-numbered
clusters represent durations with the last digits being 0 or 5 (red symbols connected with continuous
lines). The uneven-numbered clusters represent durations with the last digits being 1–4 or 6–9 (blue
symbols connected with dashed lines). For further explanations, see legend to Figure 4and Methods.
Int. J. Environ. Res. Public Health 2017,14, 1166 9 of 14
The relations illustrated in Figures 5and 6motivated analyses to detect whether some conceivable
determinants of cycling velocity altered in a systematic way with increased durations. The relations
between age, body weight, BMI, and category of cycling environment were therefore plotted for each
sex as error bars for the different duration clusters (data not shown). The levels of age, body weight,
and BMI were stable with increasing duration categories in both sexes and therefore could neither
explain the increased velocities with longer durations nor the variations between consecutive last
digit duration categories. However, a slight but systematic difference in cycling environments was
noted with increased durations, particularly in the males. Therefore, a natural next step to explore this
complexity was to make use of multiple regression analyses.
3.4. The Relation between Cycling Velocity and Distance, as Well as Other Predictors
The relations illustrated in Figures 1,2,5and 6motivated systematic analyses in the form of
multiple regression analyses of possible determinants of cycling speeds. The correlations between
continuous variables among outcome and predictor variables are presented in Table 3.
Table 3. Correlation coefficients between continuous outcome and predictor variables (n= 1558).
Variable Velocity Distance Duration Age Weight BMI
Velocity -
Distance 0.67 *** -
Duration 0.32 *** 0.89 *** -
Age −0.21 *** −0.07 ** 0.03 n.s. -
Weight 0.24 *** 0.19 *** 0.09 *** 0.07 ** -
BMI 0.02 n.s. 0.06 * 0.06 * 0.16 *** 0.76 *** -
Note: n.s. = not significant, * = p<0.05, ** = p< 0.01, *** = p< 0.001.
Multiple regression analyses revealed that cycling speeds were positively related to commuting
distance, being male, of younger age, and higher body weight but lower BMI, and the last digits
in self-reported durations being 1–4 or 6–9, as compared to 0 and 5, as well as cycling in suburban
(versus inner urban) areas (Table 4). Based on the partial correlations, the most important variable for
predicting cycling speed was route distance. The regression equation was: cycling velocity (km
·
h
−1
)
= 16.2 + 0.64
×
distance (km) + 1.69
×
sex (1 = male)
−
0.066
×
age (years) + 0.036
×
weight (kg)
−
0.15
×
body mass index (kg
·
m
−2
)
−
1.66
×
last digit in duration report (1 = 0 or 5)
−
0.65
×
cycling
environment (1 = inner urban). For details, see Table 4.
3.5. The Relation between Cycling Velocity and Duration, as Well as Other Predictors
Cycling speeds were positively related to commuting durations, being male, younger age, higher
body weight, lower BMI, last digits in self-reported durations being 1–4 or 6–9, as well as cycling
in suburban areas (Table 5). Based on the partial correlations, three more important variables for
predicting cycling speed were duration, sex, and age. The regression equation was: cycling velocity
(km
·
h
−1
) = 18.3 + 0.096
×
duration (min) + 2.67
×
sex (1 = male)
−
0.084
×
age (years) + 0.051
×
weight (kg)
−
0.20
×
body mass index (kg
·
m
−2
)
−
1.49
×
last digit in duration report (1 = 0 or 5)
−
1.59 ×cycling environment (1 = inner urban). For details, see Table 5.
Int. J. Environ. Res. Public Health 2017,14, 1166 10 of 14
Table 4.
Multiple regression analyses of the relation between cycle commuting speed and distance, as well as other predictors. All calculations are based on data
coupled to self-reported durations of ≤50 min (n= 1558).
Model 1 R2= 0.56
Outcome Variable Predictor Variables
Cycling Velocity
(km·h−1)
Distance
(km)
Sex
(0 = Female;
1 = Male)
Age
(years)
Weight
(kg)
BMI
(kg·m−2)
Last Digit in
Duration
Self-Reports
(0 = 1–4 or 6–9;
1=0or5)
Cycling
Environment
(0 = Suburban;
1 = Suburban–Inner
Urban)
Cycling
Environment
(0 = Suburban;
1 = Inner Urban)
y-intercept 16.2
unstandardized
regression
coefficient B
0.64 1.69 −0.066 0.036 −0.15 −1.66 −0.35 −0.65
95% CI 14.8–17.6
95%
confidence
interval
0.60–0.68 1.26–2.13 −
0.079–
−
0.053
0.008–0.064 −
0.25–
−
0.06
−1.98–−1.34 −0.69–−0.02 −1.03–−0.26
p-value 0.000 p-value 0.000 0.000 0.000 0.011 0.001 0.000 0.038 0.001
partial
correlation 0.62 0.19 −0.24 0.06 0.08 −0.25 −0.05 −0.08
Table 5.
Multiple regression analyses of the relation between cycle commuting speed and duration as well as other predictors. All calculations are based on
self-reported durations of ≤50 min (n= 1558).
Model 2 R2= 0.34
Outcome Variable Predictor Variables
Cycling Velocity
(km·h−1)
Duration
(min)
Sex
(0 = Female;
1 = Male)
Age
(years)
Weight
(kg)
BMI
(kg·m−2)
Last Digit in
Duration
Self-Reports
(0 = 1–4 or 6–9;
1=0or5)
Cycling
Environment
(0 = Suburban;
1 = Suburban–Inner
Urban)
Cycling
Environment
(0 = Suburban;
1 = Inner Urban)
y-intercept 18.3
unstandardized
regression
coefficient B
0.096 2.67 −0.084 0.051 −0.20 −1.49 0.33 −1.59
95% CI 16.6–20.0
95%
confidence
interval
0.079–0.113 2.14–3.19 −
0.100–
−
0.068
0.017–0.086 −
0.31–
−
0.08
−1.89–−1.09 −0.08–0.75 −2.06–−1.12
p-value 0.000 p-value 0.000 0.000 0.000 0.003 0.001 0.000 0.114 0.000
partial
correlation 0.27 0.24 −0.25 0.08 −0.09 −0.18 0.04 −0.17
Int. J. Environ. Res. Public Health 2017,14, 1166 11 of 14
4. Discussion
Important initial findings showed that commuter cycling velocities were positively related to both
distance cycled and self-reported cycling durations. These results prompted to widen the analyses to
also include other factors that could potentially explain the variations in velocity. This was undertaken
in a stepwise exploratory fashion which will be commented upon below.
First of all, it was noted that self-reported cycle commuting durations are preferably stated as
multiples of five, and that this is coupled with lower estimated cycling velocities compared to durations
with the last digits being 1–4 or 6–9. This finding is in line with those of Kelly [
13
], who studied
duration reporting in cyclists followed with cameras and found that over-reporting of durations was a
general phenomenon, but that those choosing 0 and 5 as their last digits over-reported their durations
to ~200% higher degree than those reporting last digit durations of 1–4 and 6–9.
Interestingly, independent of last digit duration category, cycling velocities increased in both
males and females with increasing durations (Figures 5and 6), and when checked for, it was not
apparent that it was due to other conceivable factors, such as a systematic difference in age with
different durations. To fully check for this, multiple regression analyses were undertaken, and they
revealed that cycling speeds were positively related to commuting distances or durations, being male,
of younger age, having higher body weight but lower BMI, and self-reported durations with the last
digits 1–4 or 6–9, as well as cycling in suburban areas. The findings will be discussed below.
The cycling velocity increased in the full models by about 0.64 km
·
h
−1
per km of distance, and by
about 0.10 km
·
h
−1
per minute increase in duration. One consequence of this is that energy demands
per unit of time of cycling, and thereby the metabolic equivalent of task (MET) values, increase with
both durations and distances. This is relevant for consideration in, for instance, physiological and
epidemiological studies, as well as in health economic assessments. For example, the WHO Health
Economic Assessment Tool (HEAT) for Cycling [
2
] makes, at present, use of a fixed MET-value of 6.8
and a cycling velocity of 14 km
·
h
−1
for both males and females independent of distance or duration.
The findings in this study lend support to a development in that respect.
These relations were calculated on the basis of the duration range 1–50 min, and it is reasonable
not to extrapolate the increase in velocities beyond 50 min or the corresponding distance. This is
because analyses of the remainder of durations (not shown) indicated that there was no further
significant increase in velocity with increased duration, or a clearly smaller increase was noted with
increased distance.
The explanatory power with sex and age as predictors (Tables 4and 5) is in line with their relation
to maximal oxygen uptake [
18
] and [
19
] (p. 306) and its relation to speed in competitive cycling [
16
,
17
].
Furthermore, the power output during cycling is related to levels of oxygen uptake [15].
Combining body weight and BMI as predictors provides an opportunity to evaluate the integrated
approximate effect of body size and body fat. In a large group of slender children, youth, and young
adults, the overall sex-neutral relation between body weight and maximal oxygen uptake indicates the
same relative increases in these variables [
29
] (p. 106). Based on that, body weight could be expected
not to be a relevant predictor of cycling speed. However, body mass may affect cycling velocity in
various ways [
20
]. One rationale for including it here as a predictor is that cycling signifies transporting
both a body and a vehicle. The most common weight of the male and female bicycles sold in the
Stockholm region has been about 18.5 kg for a number of years (personal communication with bicycle
dealers). Adding that weight will lead to ~30% increase in the body + bicycle weight of a 60-kg person,
but only ~20% for a person weighing 85 kg. That speaks in favour of persons with greater body
mass being able to cycle faster since the relative effect of the added cycling weight can affect both the
rolling resistance and the gravitational effect on ascents [
20
]. Another aspect favouring taller persons,
who generally weigh more, relates to the cyclist’s body surface area, which affects wind resistance.
In relative terms, it is lower for a taller person [
20
]. On the other hand, a higher body weight and
BMI will act in the opposite direction through increased energy demands at a given pedalling rate
Int. J. Environ. Res. Public Health 2017,14, 1166 12 of 14
and workload [
21
,
22
]. Interestingly, the overall effect of body weight was positive for cycling velocity,
whereas, as could be expected, BMI had a negative effect.
The behavioural dichotomy of duration reporting as a predictor in the multiple regression analyses
shows that its effect (1.49–1.66 km
·
h
−1
higher velocities with last digit durations of 1–4 and 6–9,
as compared to last digit durations of 0 and 5) is generalised to different distances or durations,
ages and sexes, etc. Against the background given above, the relation between speed and distance
or duration reported by individuals with last digit duration reports of 1–4 and 6–9 is interpreted
to be a more correct representation of the actual values, and it is recommended to be used in such
applications as epidemiological studies, traffic modelling and planning, health economic assessments,
and cost-benefit analyses.
Another novel finding is that the environment cycled in affects the velocities, with about 0.65 km
·
h
−1
higher speeds being noted for a given distance cycled in the suburban areas as compared to the inner
urban areas in a metropolitan setting. In light of the differences in environmental conditions between
these areas [23], these results may not, however, be surprising.
A sensitivity analysis (not shown) of the two multiple regression models revealed that the variables
distance or duration, age and sex predicted 85–95% of the variations in cycling speed, in comparison
with the R
2
-values of the full model. Adding body weight and BMI as predictors had a very minor
influence on those levels, but led to between 16–18% decreases in the size of the unstandardised
regression coefficient B for sex. When omitting either BMI or body weight as predictors in the full
regression models, the corresponding increases were 23% in regression coefficient for sex. It illustrates
that those variables play a role in the overall sex differences noted in cycling speed.
The study is cross-sectional and therefore one cannot state anything for certain about the noted
relations in terms of an intra-individual behavioural phenomenon. The increased speeds with distance
and duration could, in principle, be due to a more or less instantaneous programming to higher
cycling efforts when we have longer cycling distances ahead of us and/or a systematic selection of
individuals with a greater capacity for higher cycling speeds involving themselves in longer cycling
durations or distances. Another possibility is that it may be due to a conscious selection of lower speeds
when distances or durations are shorter to avoid sweating. Indeed, intra-individual studies on speed
selection with different distances or durations would be of great value to further our understanding
of these issues. Based on the findings of this study and other ones, a strong recommendation for
future studies and investigations of these matters is to inform the participants about the importance of
accurate duration reports, which excludes rounding off to the last digits of 0 and 5. Use of the time
measuring function in, for example, cell phones should be encouraged, and adding seconds in the
questionnaire responses might possibly signal the importance of accurateness. It is, of course, also
important to measure distances correctly, which is facilitated by open access geographic information
systems available on the Internet, (e.g., gmap-pedometer; www.gmap-pedometer.com). GPS traces
can nowadays be easily obtained by means of smart phones for collecting data on time, distance, and
velocity, as well as the route area cycled in. Compensations for about 5% systematically longer distance
measurements with GPS need to be considered, however [
9
]. If no use is made of such measuring
steps, it is recommended to use stated addresses at the trips’ origin and destination points, and to
measure the straight line distance between them and multiply it by 1.25 [
9
]. That will clearly provide
more adequate distance data than asking participants about which route distances they believe they
have cycled.
Strengths and Limitations
A clear strength of the study is the number of cyclists involved, the variations in the values of all
predictors, and that route distances were measured by a criterion method. Another strength was that
sex, age, body weight, BMI, the last digit category in duration reports, and the cycling environment
were included in the multiple regression analyses. A limitation was the preferential reporting of last
digit cycling durations of 0 and 5, which adds uncertainty to the durations reported. The external
Int. J. Environ. Res. Public Health 2017,14, 1166 13 of 14
validity of the findings should also be considered. Given that the sample is gathered from a small
proportion (~5%) of the general population (cf. [
25
]), it is possible that the cycling velocities do not
mirror the general population. Data were collected in September and October, and during that period
it is light during the predominant commuting hours (cf. [
14
]). It is possible that the speed would be
lower if cycling in the dark. Another aspect relates to the topography. A different degree of hilliness
will most likely affect the cycling velocities. These types of matters deserve future studies.
5. Conclusions
This cross-sectional study shows that commuter cycling velocity is positively related to the
distance and duration cycled. Sex and age are other important determinants, and adding body weight,
BMI, and the last digit category in self-reported durations, as well as the area cycled in, creates even
more correct input values for future epidemiological studies, traffic modelling and planning, and
health economic assessments, as well as cost-benefit analyses.
Acknowledgments:
This study was supported by funding received from the Swedish Research Council for
Health, Working Life, and Welfare (FAS/FORTE: 2012-1296) and the Research Funds of the Swedish Transport
Administration (TRV 2017/63917-6522). The author is grateful to the volunteers for participating in the study,
and for the technical assistance of Erik Stigell, Lina Wahlgren, Cecilia Schantz-Eyre, Golam Sajid, Per Brink, and
Charlie Skog, as well as for the language check by Isaac Austin and Marianne Lundberg. Gunilla Björklund,
Johan N. Sommar, and Rolf Sundberg are thanked for statistical advice. The three anonymous reviewers are
thanked for their valuable comments. I am also grateful to Maria Olovsdotter and Roland Johansson for hosting
me in their most creative and supportive setting at Vargliden in Hemavan, Sweden, a time during which this
study developed in a leapway mode.
Conflicts of Interest: The author declare no conflict of interest.
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