1. Factors Affecting Cycling
Kiarash Fariborzi
University of Florida, Department of Civil and Coastal Engineering
Word Count: 3,332
Number of tables: 3
2. Abstract
Bicycle accounts for only a few of the trips made in the US (about 1%) while majority of trips
are within bike distance, and both the public and policy makers seem to be interested in
promoting cycling for its associated benefits. Therefore, research on cycling behavior is so
timely and of vital importance. This paper studies the factors that can encourage people to cycle
using 2000 Bay Area Travel Survey datasets. The focus is on physical environment factors while
controlling for demographics of respondents. The study found several explanatory factors that
are linked to cycling; street block size and bike lane density in an area encourages cycling
whereas population density is a deterrent to cyclists.
Key Words: Bike use, Cycling, Built environment, Land use
3. Problem Definition and Background
Non-motorized transportation modes are associated with lots of advantages for individuals and
communities. National Bicycling and Walking Study recognizes health, transportation,
environmental, economic and quality of life benefits for walking and cycling. This importance
has been perceived by both the general public and policy makers. A Princeton Survey indicates
that 83% of Americans believe that federal funding for sidewalks, bike lanes and bike paths
should be maintained or increased. Another evidence demonstrating the growing interest of
Americans in biking and walking is the 25% increase in the NHTS reported bike and walk trips
from 2001 through 2009, meanwhile the total number of trips had no more than 2% increase.
Furthermore, policy makers have shown their interest in promoting non-motorized transportation
through setting up several funding programs aiming at maintaining and developing bike or walk
facilities. As the result, total pedestrian and bike funding in the US was risen from $6 million in
1990 to $1,200 million in 2009, showing an increase from 0.1% to 2% of all Federal-aid surface
transportation funds between 1990 to 2009. However, the main focus has been on walking.
According to NHTS 2009, mode share of walking with 10.5% of all personal trips has been
much more than that of biking with only 1%. Among all 60% of the trips which are within a
reasonable biking distance (i.e. 5 miles or less), bike trips account for only 1.59%. There are,
however, some communities where biking mode share is far higher such as UK, France and Italy
with 4 times and Netherlands 28 times that of US (Handy et al., 2010). Even some cities in the
US had a much higher bike share in 2013 than the US average such as Boulder and Berkeley
with 11.1% and 8.3% percent, respectively (according to the American Community Survey
(ACS) 2013).
Having mentioned the evidence demonstrating the public’s and planners’ value to cycling
together with the potentials of bike use promotion, it is of particular importance to address the
factors having an impact on individual’s cycling behaviors. The purpose of this paper is to study
cycling in Bay Area Region considering objective environmental attributes as well as
demographic characteristics of households and individuals. To our particular interest is to
discover if excluding households with no bike from the sample can improve accuracy of bike
use models.
Literature Synthesis
There are many factors influencing people’s decision to bike. These factors could be categorized
as: 1) built environment-related, 2)demographic, and 3) social environmental. This study focuses
on the first two groups because even though the dataset used provides elaborate information on
the first two sets, it lacks data on respondents’ attitudes towards cycling.
Some of the built environment elements found to contribute to cycling are: bike infrastructure,
land use and street connectivity. High bike path density implies shorter distance to potential
destinations, thereby facilitating bike trips, especially utilitarian ones (Dill and Voros, 2007).
4. Past research works have studied the impact of bike infrastructure sufficiency on bike use in
different ways. Mouden et al. (2005) found that the closer people live to trails, the more likely
they are to bike, so are they when perceiving that a combination of bike lanes and trails exist in
their neighborhood. Furthermore, Dill and Voros (2007) measured density of bike lanes in terms
of miles of bike paths within ¼ mile radius around people’s house, concluding that it has no
impact on bike use, but positively does people’s perception of having enough bike lanes in their
neighborhood. Similar result was obtained in another study where people’s perception of their
neighborhood having an off-street bike path network was positively associated with recreational
and utilitarian bike use (Handy et al., 2010). As far as the correlation of bike trip purpose and
bike path, it is believed that bike lanes not leading to workplaces may only attract non-
transportation cycle trips (Dill and Voros, 2007).
Like presence of bike infrastructure, land use diversity can influence the length of potential
bicycle trips (Dill and Voros, 2007 and Handy et al.,2010), but the relationship of cycling and
land use has not been addressed in literature so much as that of cycling and infrastructure (Dill
and Voros, 2007). This relationship may depend on whether trips have utilitarian or recreational
purposes (Moudon et al., 2005). In a study in Bay Area, Crevero and Duncan (2003) indicated
that increase in land use mix would result in more bike trips. Similarly, Handy et al. (2010)
found that perception of longer distance to certain destinations is associated with fewer utilitarian
bike trips. On the other hand, one study in Seattle indicated that only small convenience store
acreage encourage cycling (Moudon et al., 2005).
Demographic factors have been frequently studied in cycling research, and are believed to have
more significant impact on bicycling behaviors than built environment (Moudon et al., 2005).
Bike use was found to be positively associated with being white, male, middle-aged, transit user,
physically healthy and owning more than 1 vehicle per adult in the household (Moudon et. al.,
2005). However, there are conflicting findings; for instance, one study found that Hispanics are
more likely to bike, or that age is negatively related to bicycling (Bureau of Transportation
Statistics, 2003). Only a few research took account of the relationship between bike ownership
and use (Handy et al., 2010), although in Seattle study Modoun et. al (2005) found individual
bike ownership to be significant in their estimated bike use model.
As discovered from the literature review, there are some conflicts in the results of the studies
regarding the significancy of variables explaining people’s decision to bike as well as the
direction of the correlations. One possible reason could be attributed to the context of the studies.
For example, attitudes of people towards cycling as well as built environment attributes affecting
people’s decision to bike may vary from community to community. Consequently, since this
study is carried out in a different environment and uses different data from the reviewed studies,
it may be associated with different findings even about the most commonly used contributing
factors to bike use (e.g. age and sex) in previous works. Furthermore, built environment-related
factors have not been measured in the same way across the past studies (Handy et al., 2010),
which may affect the outcomes. For instance, Moudon et al. (2005) measured land use effect
5. through setting 12 land use clusters, whereas this study will capture it using land use mix index.
Also, in this study a comprehensive set of objectively measured urban form data will be
employed including, number of family types, bike lane, local road highway densities within
certain radiuses around every one of the household locations in the sample, among others.
Although most of them have been studied in past research, the combination of them in the same
study is rare. Finally, probably the most different work of this study is to build two sets of bike
use prediction models; one, for people owning a bike in their household, and the other for all the
people in the sample. This will help figuring out if excluding zero-bike households from the
sample can lead to a more accurate model.
Data Assembly and Qualitative Analysis
The dataset used to carry out this study is 2000 Bay Area Travel Survey (BATS). This dataset
contains daily activity information of 32,701 respondents belonging to 14,683 randomly selected
households, recorded for each person during 2 consecutive days (not the same 2 days for all
people). Every activity record includes information about time of day, day of weak, mode(s)
used, geographical coordinates of the location of activity, among others. To obtain the estimation
sample, every person was labeled either “0” provided they had not ridden bike in the 2 days of
survey, or otherwise, “1”. Data on characteristics of the respondents (e.g. age, gender and race),
and their households (e.g. number of vehicles, bicycles, and income) were obtained from the
BATS personal and household datasets, respectively, and were linked to the corresponding
person, so were the built-environment data within ¼-, 1- and 5-mile radius around every
household’s location extracted from the BATS urban form dataset. For a total of 1,196 cases the
urban form and/or personal data were not matched, in which cases the person was removed from
the sample. In addition, people less than 5 years old as well as the disabled were excluded since
bike is not a feasible choice for them. Therefore, the final estimation sample size became 28,986.
Sample share of cyclists in the estimation sample is 3.3%, which is much less than that in similar
studies with 20% “regular cyclists” (Dill and Voros, 2007), or 21% “cyclists” (Moudon et al.,
2005). However this difference makes sense since the mentioned studies define a person as
cyclist if he/she rides bike at least once a week, while this study considers one as a cyclist if they
biked at least once over the 2-day survey. Table 1 shows frequency distribution of selected
explanatory variables. Younger adults and men are more likely to have cycled. This result is
consistent with Bureau of Transportation Statistics (2003). Household income level, however,
seems to have no correlation with one’s decision to bike. Vehicle availability in the household
appear to be significantly related to one’s decision to bike with people owning less than 1 car per
adult in their household being far more likely to bike than those having more than 1. This
observation could be reasonable especially if the share of utilitarian cyclists has been relatively
high. It is intuitive that household bike ownership is positively associated with cycling, but
surprisingly, bike use share of 6.3% is recorded for households with more than 1 bike per capita.
High density of bike lanes (i.e. more than 20,000 meters in 1 square mile radius around
household locations) is likely to encourage cycling whereas there is no obvious relationship for
6. other density levels. The share of areas with no bike lanes is fairly high, and unexpectedly, more
people rode bike in those areas, compared to the next two higher density levels. Finally, the land
use mix index has a curvilinear relationship with bike use such that regions with fairly diverse
land use (i.e. the index of 0.34 to 0.51) have the greatest share of cyclists (3.7%).
Methodology
In this study a binary logit model will be developed, predicting the odds that a person ride (or do
not ride) bike at least once in any given two days. A Base model will be primarily estimated
using the frequently used explanatory variables identified from the literature review, especially
demographic variables. Dropping the statistically insignificant variables at 95% confidence level
from the base model, other seemingly relevant variables will be added one by one to the model,
and they will be maintained in the model should the t-test shows a statistically significant (or
only marginally insignificant) correlation between the added variables and the dependent
variable (i.e. bike use). In this way, the collinearity of independent variables is likely to be
identified, although the correlation of seemingly collinear variables will be separately assessed
using correlation analysis, so that the variable which has stronger relationship with cycling will
be included in the model. The final model will only encompass statistically significant variables
at 95% confidence level. Subsequently, the accuracy of the model will be examined in
comparison with another model developed based on the cases that own at least 1 bike in their
households.
Empirical results
Table 2 shows the final specifications of the binary logit model. The negative (positive) sign of
the coefficients imply a negative (positive) correlation between the corresponding variable and
bike use. The model encompasses 15 factors significantly affecting bike use at 95% confidence
level, including 5, 3 and 7 variables respectively capturing the impacts of personal
characteristics, household characteristics and built environment attributes on people’s decision to
bike. In this section the model results are presented and intuitively explained.
Demographic factors and bike use:
Demographic factors play major role in encouraging or discouraging people to bike. Age has
negative linear correlation with cycling, controlling for other factors. This result is intuitive since
being in a good health is a prerequisite for cycling, and younger people are more likely to be
physically healthy. In addition this finding matches previous research (Bureau of Transportation
Statistics, 2004). Gender and race also affect bike use such that being male and white are
positively associated with cycling. Furthermore, having full flexibility in working hours and
being full time student positively affects bike use though the magnitude of their impact is not as
great as the other mentioned demographic variables. A plausible explanation for the former result
is that commuters with non- or partially flexible working hours have to be worried about being
7. late in their work place so they choose faster modes than bike. The latter result also could be
attributed to the bike-friendly environment of most college cities in the US.
Vehicle and bicycle ownership in household contribute even more to the likelihood of cycling
than personal characteristics do. The positive correlation of bike ownership and use was
anticipated since having bicycle is a prerequisite to cycling. Auto availability in household,
however, is a deterrent to cycling, so is higher household income measured in a continuous scale.
Perhaps households with higher income can afford multiple cars which discourage them from
using bike. Nevertheless, previous research studies didn’t find any significant relationship
between income and bike use (Dill and Voros, 2007, and Moudon et al., 2005).
Built-environment factors:
The dataset utilized in this study provides a great deal of physical environment information, the
majority of which were tested in the model. Some of them which are not included in the final
model would otherwise become significant although would lead to insignificancy of other
specifications of the model, in most of which cases rho-squared test was carried out to help
identifying the better model.
Among 7 physical environment variables in the final model, number of street blocks within 1-
mile buffer has the highest relative magnitude in the model. High value of this variable suggests
a good street connectivity which enhance accessibility to potential destinations thus promoting
cycling. Further, short street blocks can prevent motorist from speeding hence providing a safe
environment for cyclists.
On the other hand, population density and number of grocery stores within 1-mile radius around
people’s house are deterrents of cycling. The latter finding is consistent with a previous study by
Moudon et al., 2005, which attributes this to the proximity of typical convenience stores to major
arterials and gas stations. Another possible cause is that the density of population and
convenience stores in an area are associated with more auto use which creates an unsafe
environment for cycling.
As expected, cycling is increased with an increase in bike lane density measured as meters of
bike lanes within a 0.25-mile and 1-mile buffer (though only the former is included in the final
model). Similar results were achieved in previous research where the presence of bike lanes
measured subjectively (Handy et al.,2010), and the distance to nearest trail (Moudon et al.,2005)
stimulated cycling. Bike paths leading to potential destinations can promote utilitarian cycling,
otherwise, they can be destinations for recreational bike users; in both cases they encourage
cycling.
Land use diversity was found to be irrespective of people’s decision to bike. The impact of land
use mix was captured and introduced to the model using an index computed based on the acreage
of “commercial”, “residential” and “other” usages within 1-mile buffer, equally weighted.
8. Subsequently, the impact of commercial and residential land uses were independently tested,
which resulted in residential acres within 1-mile buffer becoming significant and positively
correlated with bike use. Area types of neighborhoods (i.e. CBD, urban, suburban and rural)
were also introduced to the model and turned out to be insignificant which is due to their
correlation with existing variables in the model.
Having estimated the final model (model A), its accuracy was evaluated with respect to a
comparison model (model B) estimated using those cases in the sample who owned at least one
bike in their households. The specifications of the comparison model are listed in Table 3. The
purpose is to find out if excluding zero-bike households from the sample can improve the
prediction accuracy of bike use model. A validation sample of 3000 cases with at least 1 bicycle
in household was drawn from the dataset, both models were applied to it and the percent errors in
predictions were calculated. Model A and B had average errors of 8.3% and 8.8%, respectively.
A paired-sample t-test was carried out showing that the difference in percent errors is statistically
significant, so model A has more accurate predictions. However, neither model does good
predictions for the cases who have biked; model A and B have 91% and 90.5% average errors,
respectively. To examine the accuracy of model A for zero-bike households, a sample of 1000
cases was taken. Expectedly, model A has a very low average error (1.8%) in explaining the
sample, as only a few cases have biked.
Conclusions
This paper examines the factors that affect people’s decision to cycle. The dataset used for this
study is 2000 Bay Area Transportation Survey (BATS) which provides elaborate physical
environment information together with demographics of the respondents. Both sets of factors
were considered in developing a bike use model.
The model has some important results. First, It indicates that demographic factors play a key role
in cycling with relatively higher contribution to cycling compared to environmental factors.
Being young, male and white are positively associated with bicycling. Second, high number of
street blocks per square mile positively affect cycling with increasing street connectivity as well
as hindering drivers from speeding. This finding could be a helpful urban design tip.
Furthermore, residents of neighborhoods with more bike lanes are more likely to bike. Thus,
providing bike lanes can improve cycling in a region (though this study does not control for self-
selection impact). Third, it was indicated that excluding people from the sample for whom
bicycle is hardly a feasible choice (due to lack of bike in their households) does not positively
contribute to bike use model results. Finally, although the model has good predictions for people
who does not bike, it does not appropriately make predictions about those who actually have
biked. This could be due to very low sample share of cyclist in the dataset, and the fact that the
model learns from it. This could be a good tip for future research in this field.
9. Acknowledgments
1. Dill J., & Voros k.(2007). Factors affecting Cycling Demand: Initial Survey Findings from the
Portland, Oregon Region. Transportation Research Record: Journal of the Transportation
Research Board, 2031, p. 9-17.
2. Moudon, A.V., C. Lee, A.D. Cheadle, C.W. Collier, D. Johnson, T.L. Schmid, and R.D.
Weather. (2005) Cycling and the built environment, a US perspective. Transportation Research
Part D-Transport and Environment. 10(3): p. 245-261.
3. Handy S. L., Xing Y., Buechler T. J. (2010). Factors Associated With Bike Ownership and
Use: A Study of Six Small U.S Cities, Transportation, Vol. 37, No.6, p. 967-985
4. Cervero, R., & Duncan, M. (2003). Walking, Bicycling, and Urban Landscapes: Evidence
From the San Francisco Bay Area. American Journal of Public Health, 93(9), p. 1478-1483.
10. Table 1 Selected demographic and environmental factors and cycling
% within
category
% within bike-
use
% in sample
Sex
Male 4.5% 65.4% 46.7%
Female 2.1% 34.6% 53.3%
Age
<18 5.0% 27.2% 17.9%
18-34 4.4% 25.8% 19.3%
35-44 3.5% 20.7% 19.6%
45-54 2.6% 15.8% 20.0%
55-64 1.7% 6.4% 12.3%
65> 1.2% 4.1% 10.9%
Race
White 3.4% 77.6% 73.5%
Other 2.7% 22.4% 26.5%
Household Income
Refused 4.3% 4.8% 3.7%
<35,000 3.1% 9.3% 9.5%
35,000-49,999 3.1% 12.6% 13.0%
50,000-74,999 3.2% 22.0% 22.2%
75,000-99,999 3.6% 23.2% 20.8%
>100,000 2.9% 28.0% 30.7%
Number of Vehicles
per Adult
<1 5.4% 32.4% 19.4%
1 or more 2.7% 67.6% 80.6%
Number of Bikes to
Household size
0 0.7% 7.9% 34.2%
0-0.99 2.6% 25.1% 31.3%
>0.99 6.3% 67.0% 34.5%
Bike Lane Density
(
meter
1 square mile radius
)
0 3.1% 32.8% 31.3%
0-10,000 2.6% 19.8% 26.4%
10,000-20,000 2.9% 17.8% 21.5%
>20,000 5.0% 29.5% 20.9%
Land Use Mix Index
<0.34 2.8% 22.9% 26.3%
0.34-0.51 3.7% 27.2% 24.0%
0.51-0.61 3.3% 26.1% 25.2%
>0.61 3.1% 23.8% 24.5%
11. Table 2 Specifications of model the final model (model A)
Explanatory variable Coefficient t-statistics
Constant -3.95596*** -19.23
Personal Characteristics
Age -.01602*** -5.58
Male (dummy variable) .86765*** 11.77
White (dummy variable) .32936*** 3.68
Full time student (dummy variable) .24609** 2.17
Full flexibility in working hours (dummy variable) .18825** 2.2
Household Characteristics
Number of vehicles per adult -1.04303*** -10.59
Number of bikes per person 1.04401*** 23.76
Income -.15160D-05** -2.01
Built-environment Attributes
Number of street blocks within 1-mile radius .00252*** 3.95
Bike lane density within 0.25-mile radius .70371D-04** 2.5
Residential acres within 1-mile radius .00028** 2.25
Population within 1-mile radius -.21410D-04** -2.55
Number of multi family dwelling units within 1-mile radius .46224D-04*** 3.29
Number of grocery stores within 1-mile radius -.01233** -2.49
Total employment within 1-mile radius more than 10150 (dummy variable) .20386** 2.25
N=28,986
Log-likelihood at convergence -3625.4058
Log-likelihood with constant only -4215.2538
Rho-squared 0.1399
Adjusted Rho-squared 0.1395
*,**,***: Significant at 90%, 95% and 99% level
12. Table 3 Specifications of the comparison model (model B)
Explanatory variable Coefficient t-statistics
Constant -3.55699*** -16.62
Personal Characteristics
Age -.01042*** -3.43
Male (dummy variable) .87624*** 11.67
White (dummy variable) .30683*** 3.36
Full time student (dummy variable) .26976** 2.3
Full flexibility in working hours (dummy variable) .16763* 1.93
Household Characteristics
Number of vehicles per adult -1.12239*** -10.97
Number of bikes per person .79591*** 16.56
Income -.23257D-05*** -2.99
Built-environment Attributes
Number of street blocks within 1-mile radius .00244*** 3.74
Bike lane density within 0.25-mile radius .65515D-04** 2.26
Residential acres within 1-mile radius .00024* 1.85
Population within 1-mile radius -.19396D-04** -2.26
Number of multi family dwelling units within 1-mile radius .47164D-04*** 3.30
Number of grocery stores within 1-mile radius -.01241** -2.42
Total employment within 1-mile radius more than 10150 (dummy variable) .23098** 2.50
N=28,986
Log-likelihood at convergence -3345.236
Log-likelihood with constant only -3745.844
Rho-squared 0.1069
Adjusted Rho-squared 0.1062
*,**,***: Significant at 90%, 95% and 99% level
