BIKE SHARING: A RANDOMIZED STUDY EVALUATING THE
UNIVERSITY OF OREGON BIKE LOAN PROGRAM
by
E. PRICE ARMSTRONG
A THESIS
Presented to the Department of Planning,
Public Policy and Management
and the Graduate School of the University of Oregon
in partial fulfillment of the requirements
for the degree of
Master of Public Administration
June 2010
11
"Bike Sharing: A Randomized Study Evaluating the University of Oregon Bike Loan
Program," a thesis prepared by E. Price Armstrong in partial fulfillment of the
requirements for the Master of Public Administration degree in the Department of
Planning, Public Policy and Management. This thesis has been approved and accepted
by:
Dr. Neil Bania, Chair of the Examining Committee
J
~,/ j / 2-0)0
Date
Committee in Charge: Dr. Neil Bania
Dr. Marc Schlossberg
David Roth
Accepted by:
Dean of the Graduate School
111
An Abstract of the Thesis of
E. Price Armstrong for the degree of Master of Public Administration
in the Department of Planning, Public Policy and Management
to be taken
Title: BIKE SHARING: A RANDOMIZED STUDY EVALUATING THE
UNIVERSITY OF OREGON BIKE LOAN PROGRAM
Approved: _
Dr. Neil Bania
June 2010
Around the world, active transportation is looked at as one solution to problems
presented by climate change, epidemic obesity, rising fuel prices, and crippling traffic
congestion. In 2008, the University of Oregon launched the Bike Loan Program as an
effort to address these issues. Because those interested in participating in the Bike Loan
Program were randomly selected based on bicycle size availability, those who were not
chosen acted as a perfect control group. This study evaluates the impacts of the Bike
Loan Program on participants vis-a.-vis non-participants, and seeks to determine if those
participating do ride bikes more often than non-participants. This study will also show
what modes of transportation are being replaced by bike trips when a person participates
in the Bike Loan Program. Finally, this study also shows if there are changes in attitudes
or perceptions about transportation resulting from participation in the program.
CURRICULUM VITAE
NAME OF AUTHOR: E. Price Armstrong
PLACE OF BIRTH: Lexington, Kentucky
DATE OF BIRTH: March 6,1985
GRADUATE MTD UNDERGRADUATE SCHOOLS ATTENDED:
University of Oregon
Hampshire College
DEGREES AWARDED:
Master of Public Administration, 2010, University of Oregon
Bachelor of Arts in History, 2007, Hampshire College
AREAS OF SPECIAL INTEREST:
Transportation Policy
Transportation Finance
Program Evaluation
GRANTS, AWARDS AND HONORS:
Al Frazier Scholarship, Department of Planning, Public Policy and
Management, 2008
IV
vTransportation Research Board Travel Scholarship, 20 I0
Dean's Graduate Fellowship Award, School of Architecture and the Allied
Arts, 2010
VI
ACKNOWLEDGEMENTS
Without the support and guidance of my committee, Dr. Neil Bania, Dr. Marc
Schlossberg, and David Roth, conducting the study, drawing appropriate conclusions
from the data, and expressing those conclusions in a meaningful way would have been
impossible. My committee chair, Dr. Bania, has been the key to keeping this project
moving forward over the past year. The amount of time and input he has devoted to this
project during its development and execution has been crucial in order to create the study
described in the following pages. Equally as indispensible has been Dr. Schlossberg's
contribution to my academic interest in transportation. Without his expertise and advice
throughout my time as a graduate student, I can say with confidence my academic path
would not have been as focused or successful.
I would also like to thank the staff at the University of Oregon Bike Loan
Program, including Rithy Khut, Dave Villalobos, Dan Geiger, and Briana Orr. While
some program administrators get nervous at the prospect of an in-depth evaluation, from
the first time I approached them to do a study looking at the impacts of their program
they have been enthusiastic supporters. This enthusiasm demonstrates to me their
genuine desire to make the Bike Loan Program the most effective, successful program
possible in pursuit of their mission.
Finally, and above all, I would like to thank my wife Tara. Her unflagging
support of my pursuit of a master's degree has been crucial in easing the stressful nights
of data analysis, table creation and draft edits.
va
TABLE OF CONTENTS
Chapter Page
I. INTRODUCTION 1
Policy Context 2
Bike Sharing Programs 6
II. LITERATlTRE REVIEW 9
III. DATA AND METHODS 13
Bike Loan Program Rental System and Subject Selection 13
Variables 17
Data Analysis '" 19
IV. FINDINGS 23
Response Rate 23
Demographic Information 25
Cross-Sectional Analysis 26
Difference-in-Difference Analysis 27
Multivariate Regression 28
V. DISCUSSION 30
Prospective Participant Pool 31
Health 31
Chapter
Vlll
Page
Mobility and Equity 32
Environmental Impact 33
Future Studies 34
Conclusion · 35
APPENDICES 38
A. TABLES 38
B. TIME 1 S·URVEy 52
C. TIME 2 SURVEy 56
REFERENCES 60
IX
LIST OF TABLES
Table Page
I. Self-Reported Mode Replaced by Shared Bike 38
2. Chronology of Events 39
3. Dependent Variables for Travel; Times 1,2 and Change , 40
4. Dependent Variables for Attitudes; Times I, 2 and Change 41
5. Dependent Variables for Travel; Time 2 , , 41
6. Independent Variables; Times 1 and 2 , .42
7. Minima, Maxima and Standard Deviations for Variables 42
8. Travel Mode Frequency Response Values 43
9. BLP Bike versus Non-BLP Bike Use, Number ofTimes per Week 43
10. Time I Response Rate '" ,. '" 43
II. Time 2 Response Rate , 43
12. Time I Responses Received before Rental Sessions .43
13. Responses by Time Slot '" .44
14. Demographic Comparison to University .44
15. Demographic Comparison between Groups 45
16. Travel Expectations in Absence of Bike Loan Program '" .45
17. Trips per week to Travel to Any Destination 46
18. Sensitivity Test of Times per Week Used Excluding Late Time 1 Responses .46
19. Percentage ofResponses Indicating Mode Used at Least Once per Week .46
20. Sensitivity Test of Use Weekly Use Rates Excluding Late Time I Responses .47
Table
x
Page
21. Travel Expectations versus Mode Used .47
22. Sensitivity Test of Expected Mode versus Actual Excluding Late Time I Responses .47
23. Change in Attitudes and Perceptions .48
24. Sensitivity Test ofDiD Attitudes Excluding Late Time 1 Responses .48
25. Time 2 Travel Behavior Multivariate Regression .49
26. P-Values and T-Statistics 49
27. Sensitivity Test ofMode Use Regression Excluding Late Time 1 Responses 50
28. Change in Attitudes and Perceptions Multivariate Regression 50
29. P-Values and T-Statistics '" 51
30. Sensitivity Test ofDiD Attitudes Excluding Late Time 1 Responses 51
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1
CHAPTER I
INTRODUCTION
Today, America lags behind other western countries in the quality and
consistency of its bike and pedestrian amenities. Thanks in large part to their powerful
policy initiatives to promote active transportation and reduce personal vehicle use, other
countries such as Denmark, the Netherlands, and Germany boast bike commute rates
between 14% and 37% for trips under one mile (Pucher & Buehler 2008). Meanwhile,
Americans make only 2% of such trips by bike (Ibid). After decades ofauto-oriented
development in the U.S., however, active transportation is gaining increased attention in
American transportation policy. As America grapples with an obesity epidemic, climate
change, foreign oil dependence, and traffic congestion, policymakers are beginning to
look more closely at biking and walking as simple solutions to these problems (LaHood
2010). With federal, state, and local support, there are a host of programs being
implemented across America to lure people out of cars and onto bikes. Some of these
programs include Safe Routes to School, the Non-motorized Transportation Pilot Project,
Complete Streets policies, and bike festivals or other cultural events.
Of many arguments in favor of increasing investments in bike infrastructure, three
main justifications are most commonly used. These are the benefits to public health, the
relief of traffic congestion, and reduced environmental impacts. Below, each reason will
be more fully explored.
2Policy Context
Health
For the first time in American history, today's generation of children may have
shorter lifespans than their parents (Olshansky et al. 2005). This is attributable largely to
the surge in childhood and adult obesity, which directly costs America an estimated $75
billion in additional health spending (TRB 2005). This represents a serious drain not
only on the nation's economic vitality, but also a measurable reduction in American
quality of life.
In contrast to America's public health crisis is the relatively good health of
European counterparts such as Denmark, France, and Germany (Bassett et al. 2008). One
common denominator between these countries is the level ofphysical activity engaged in
simply traveling to and from destinations by biking and walking (Ibid.). Researchers and
policymakers alike have begun serious study on how America can reduce the obese
population, and these countries can act as case studies for just that.
Recently, attention has been paid to the relationship between the built
environment and levels ofphysical activity, particularly among children. For example,
simultaneous to the rise in obesity has been a decline in the number trips children made
by walking, from a high of 87% in 1969 to 39% in 2001 (Watson & Dannenberg 2008).
The Federal Highway Administration reported that during the same time period children
walking or biking to school declined from roughly 50% to fewer than 15% (SRTS 2010).
It is in the face of numbers such as this that the Safe Routes to School Program was
established to encourage children to walk or bike to 'school.
3Nonetheless, the link between the built environment and public health is still
being debated (TRB 2005). Other factors, such as the availability of low-priced, high-
calorie food, certainly also playa role in obesity rates. However, the evidence shows that
improvements in infrastructure, such as bike lanes, sidewalks, and path connectivity, can
increase the number of people choosing not to drive (Ibid.). Whether this increase in
walking and biking leads to better health outcomes, however, is still being established
(Ibid.).
Traffic Congestion
A second major reason for promoting bicycling, and more generally non-
automobile modes of transportation, is the massive economic drain caused by congestion.
Every year, the Texas Transportation Institute releases a report on the cost of congestion.
In 2005, the authors estimated a total national cost of $65 billion from time and fuel spent
while idling in traffic (Schrank and Lomax 2005). Cervero (1998) estimated that
congestion costs the economy roughly 2-3% GDP annually in productivity.
The growing consensus is that we are no longer able to "build our way out" of this
problem. One of the reasons for this is the erosion of the purchasing power of gas tax
revenues, which have lost approximately 33% of the purchasing power they had in 1993
(NTIFC 2009). This means that we are struggling to pay for just maintenance of existing
roads and highways, let alone expanding the capacity to accommodate increased traffic
(Ibid.).
4The allure of increasing the number ofpeople walking and biking, then, is
powerful in the face of seemingly intractable obstacles to fixing our car-oriented system.
While the cost of a road expansion to reduce congestion can be in the millions of dollars,
adding paint to existing roadway to create a bike lane might only cost thousands. By
accommodating for bicyclists and automobiles, more capacity is added to existing
roadways, allowing for greater efficiency ofuse and allowing car trips to be replaced
with bicycle trips.
There is, of course, the possibility of induced travel demand - that is, by reducing
traffic congestion drivers may make more trips to take advantage of the reduced time cost
of driving, negating any gains (Cervero 2003). However, the same problem exists for
adding lane capacity to accommodate for more cars. A key difference, as mentioned
above, is that adding lane capacity is several orders of magnitude more expensive than
striping a bike lane.
Environmental Impacts
Environmental concerns are the final reason that bicycling is promoted as an
alternative to automobility. Globally, transportation accounts for roughly one-third of
greenhouse gas (GHG) emissions by sector, second to electricity generation (EPA 2010).
Because of the amount of hydro-electric dams in the Northwest, the proportion ofGHG
emissions from transportation is even higher at roughly 38% (GAGGW 2004). As
policymakers and the public at large become more aware of the grave risks associated
5with climate change, the need for more sustainable forms of transportation becomes
highlighted.
Degraded air quality is also a serious concern, particularly in large urban areas
like Los Angeles. The most comprehensive survey to date of the impact of vehicle
emissions on public health by the Health Effects Institute (2010) found that the literature
generally supported the hypothesis that living in close proximity to major roadways
exacerbates, if not causes, asthma in children. Given that asthma costs Americans over
$20 billion annually (NIH 2010), reducing emissions to alleviate this health burden could
benefit the country as a whole.
Active transportation, again, offers a promising possibility to reduce tailpipe
emissions without reducing levels of mobility. A travel survey from the San Francisco
Bay area showed that over 60% of trips under one mile are made with a car (Cervero &
Duncan 2003); these are the kinds of trips likely to be replaced by walking or biking. If
levels of bike commuting reached Copenhagen rates (roughly 40%), then it can be
presumed that GHG and other air pollution emissions from mobile sources would
decline.
It is unclear what the magnitude of reductions from bicycling can be expected.
According to the National Household Travel Survey, 25% of trips are two miles or less
(2004), and so could conceivably be replaced by bicycling trips. However, it is not
certain what proportion of total GHG emissions they comprise, and thus what a realistic
expectation of bicycling's role in GHG reduction would be.
6Bike Sharing Programs
One method used to increase bicycling is Bike Sharing. As the name indicates,
bike sharing can be any system wherein a fleet of bicycles is rented or loaned to multiple
users. These systems typically exist in a limited geographic area, such as within a central
business district or on a college campus (SmartBike, in Washington, DC for example
covers approximately four square miles), and can be publically or privately owned. The
logic is plain to see - these areas are high-value destinations with office buildings or
dense commercial areas where congestion may be severe and parking scarce. By limiting
the geographic scope, it is easier to keep track of the communal bikes, and since
bicyclists do not typically want to ride very long distances, a limited scope is unlikely to
be a burden to the user.
One such program, the Bike Loan Program (BLP), started in the summer of 2008
after the Associated Students of the University of Oregon (ASUO) dedicated $18,000 in
start-up funds to the Outdoor Program. The program was designed to take impounded
bikes that had been abandoned on campus and add a certain amount of accessories and
branding (baskets, fenders, stickers, etc). The program operates on an "ownership"
model, where the student keeps the bike for one to three academic terms and is
responsible for maintenance.
The Bike Loan Program states as its mission:
7. . . to increase access to affordable, reliable, and sustainable
transportation. Through the integration of long-tenn bicycle loans,
education, and recreation, the BLP will enhance the physical and
cultural development of students. As a resource for alternative
transportation, we will minimize our campus and community
environmental impact." (UO Outdoor Program 2010).
While this mission statement is broad, it covers many key issues described above.
Most obvious is the goal of minimizing environmental impact through promoting
sustainable transportation. They also seek to increase access to affordable transportation
options, and thus increase student mobility and transportation equity. Lastly, enhancing
physical and cultural development can be interpreted as improving student health and
strengthening community cohesion.
What is more interesting than the stated goals of the BLP is the manner in which
the program was incorporated into the political platfonns for ASUO presidential hopefuls
in the Spring of 2009. Two main candidates, Nick Schultz and Emma Kalloway, both
promoted creating a pennanent source of funding, or institutionalizing, the Bike Loan
Program (Emerald Editorial Board, 4/18/2009). They cited two primary reasons for
institutionalizing the BLP: 1) reducing the environmental impact of the University of
Oregon and 2) easing the parking burden on campus (Ibid.). Implicit in both cited
reasons was that the Bike Loan Program will entice people to bike instead of drive.
8This study seeks to evaluate the impacts of the BLP on the program participants,
looking specifically at any changes in transportation mode use and in attitudes regarding
bicycling. By determining these impacts, conclusions can be drawn on the progress made
toward its goals, both stated by the program administrators and by political proponents.
The following report is broken down into four sections. The first is the literature
review on bike sharing programs. The next section describes the data and methods. The
third section details the findings of the study, followed by a discussion and
recommendations based on the findings.
9CHAPTER II
LITERATURE REVIEW
The number of bike sharing programs on university campuses and city centers
today is growing. Unfortunately, the literature on impacts ofbike sharing systems is thin,
but is gaining more attention as cities like Paris, Minneapolis and New York devote their
resources to such programs. Below, existing literature dealing with bike sharing
programs will be reviewed.
Bike sharing is described by Shaheen et al. (2010) as being a solution to what is
called the "last mile problem." This is where a commuter is trying to decide between
driving or taking transit/carpooling, and decides on driving because there is no
connection between the drop-off point and the destination (Ibid). By putting in shared
bicycles around high-value destinations like urban cores, college campuses or corporate
campuses, these systems can provide the last vital link in a non-automobile commute.
Bike sharing started in the late 1960's in the Netherlands and the UK as free,
communal bikes that could be picked up, ridden to any destination, and then dropped for
the next user (Shaheen et aI., 2010). Due to theft and vandalism, these programs changed
fIrst into coin operated and then electronic stall systems so that the fleet could be better
tracked and maintained (Ibid). These programs spread to North America in the early
1990's, starting with Portland, Oregon' s Yellow Bike Program. This program eventually
10
ran into the same problems of theft and vandalism as the early programs in Europe
(Ibid.).
The most prominent case study today for urban bike sharing systems is Paris,
France. The Ve1ib program in Paris started with 13,000 subscribers, growing to 100,000
only three months later, and gaining international accolades for its environmental
responsibility in the process (NYCDCP 2009). Velib is an example of a "Smart System",
complete with docking stations and digital rental technology, which one study put the
international total at 78 cities with a combined fleet of almost 70,000 bicycles in 2008
(Midgley 2009). A separate report put the number at 90 (MetroBike 2009). These
systems are not cheap; Velib in Paris had start-up costs of $114 million, with annual
operating costs of $45.8 million for a fleet of24,000 bicycles (NYCDCP 2009).
Across North America, there are roughly 65 college campuses with bike sharing
programs, with another 10 planned by the end of20l0 (Shaheen et aI., 2010). In the U.S.
only Smart Bike in Washington DC has automated kiosks like in Europe (NYCDCP
2009). However, Minneapolis is slated to begin a bike sharing program "Nice Ride" in
June of20l0, and Philadelphia and New York City are both in the planning stages for
bike sharing programs of their own (Ibid.). Other programs have a variety of funding
levels and organizational structure; indeed, some have no apparent funding source and
operate based on donated labor, space, tools, and bikes
Despite the time, effort and funding devoted to these programs, there have to date
been few systematic studies evaluating the effectiveness of them in cities, and no pre- and
post-test evaluations done on college campus bike sharing programs. As Pucher et ai.
11
(2010) points out, "The impacts of these programs are hard to assess, as they are often
accompanied by the expansion of the bicycle network in anticipation of the increased
bicycling." There are suggestive findings, such as a more than doubling ofthe bicycle
mode share in Barcelona and Paris after the implementation of their programs (Ibid.)
These statistics are confounded however by concurrent improvements in bicycle
infrastructure.
Determining the mode shift impacts of these urban bike sharing programs can be
challenging. A 2007 study of the bike sharing programs in Paris, Lyon and Barcelona
showed that when asked how trips would have been made if not by a shared bicycle, the
majority of respondents indicated public transit (see Table 1) (JZTI & Bonnette
Consulting 2010). However, it is noted that the survey results do not make it clear if the
trip on a shared bike replaces only part of a transit trip (i.e. a circulator bus trip to a
subway line) or the entire transit trip. Depending on interpretation, these results may
overstate or understate the shift from transit.
In the case of bike sharing programs on campus, the evidence base is even
shallower. Probably because of the low cost, grassroots nature of campus bicycle sharing
programs initiated by students, there is very limited data on the potential impact they
might have. Two studies have been conducted by students to determine campus interest
in potential bike sharing programs, though none could be found which evaluate the actual
impacts of existing programs (Brougham et al. 2009; ECOHusky 2004).
Despite the paucity of literature dealing expressly with evaluating the efficacy of
bike sharing programs, there are studies indicating their potential for influencing mode
12
share. Cervero et al. (2009) found that the single strongest predictor for bicycle use is the
availability of a bike. Furthermore, Dill and Voros (2006) found that 45% of non-cyclists
wish they bicycled more, indicating that there is latent demand present prevented by
some factor that may include not having access to a bicycle. However, the reasons most
cited preventing increased bicycling were traffic volumes and lack of infrastructure-
bicycle access was not addressed in the article.
As a last note, there is some evidence that programs providing incentive to
increase bicycling impact primarily what Ogilvie (2005) termed the "motivated
subgroups." Two studies found a shift of 1% to 5% of all trips from car to bicycle
following campaigns to increase bicycling (Ibid.). However, this increase in bicycling
was seen primarily from those who already bicycle regularly increasing the number of
trips taken by bike (Ibid.). In the context of bike sharing programs, this may mean that a
person who normally takes a circulator bus to get from the downtown transit station to
work, instead uses a shared bike to travel in the urban core. While this may reduce
crowding on circulator buses, it may not impact the single occupancy vehicle users, who
are the targets of such programs.
The present study will help fill this significant gap in the literature analyzing bike
sharing programs' impacts on travel behavior, and specifically on college campuses. By
using the method described below, any changes in travel behavior relative to a control
group will shed key insights on the role bike sharing could play.
13
CHAPTER III
DATA AND METHODS
This study used a pre-test (Time 1), post-test (Time 2) model to evaluate the
impacts of the Bike Loan Program. Described in further detail below, this study used
Bike Loan Program (BLP) bike recipients as the experimental group, and prospective
program participants who were not able to receive a bike as the control group. Online
surveys were sent out via email to gather self-reported travel behavior and attitudinal
data. The data was then analyzed using three different methods to look for statistically
significant differences between the test and control groups.
Bike Loan Program Rental System and Subject Selection
The rental system for the Bike Loan Program worked through a somewhat
informal application and allocation process, shown in Table 2. First, prospective
participants in the Bike Loan Program were advertised to through various methods during
the 2008-2009 academic year. One of these methods was through "IntroDUCKtion," the
new student orientation that occurred in May 2009 for incoming students in October
2009. The second method was coordinating with the International Studies Office to
distribute information to incoming international students before they arrived in Eugene.
The third method was through word ofmouth, press coverage in the school newspaper,
and the branded bicycles that sit on campus throughout the year.
14
A prospective program participant contacted the Bike Loan Program staff one of
two ways: she either sent an email to the BLP coordinator asking for more information,
or physically came to the BLP Office to inquire further. Once interest was expressed,
students were directed to sign up for one of two check-out sessions, which occurred on
Saturday, September 19th and Sunday, September 20th• On both days, there were three
one-hour time slots for prospective participants to sign up for (six time slots total). In
order to register for one of the six time slots, prospective participants signed up on posted
sheets in the BLP Office. Each time slot had ten spaces, meaning that potentially 60
students could fill the six time slots.
In addition to the 10 spaces per slot, each one-hour time slot had space for
additional students to sign up as "alternates" in the case that one of the ten was not able to
rent a bike. In total, an additional 32 students signed up to be alternates. See Table 13
for a complete listing of the distribution of control group and experimental group subjects
across the six time slots. Finally, there were also 25 students who emailed the BLP office
to request information about how to receive a BLP bike, but never signed up on the sheet
posted.
Once a student signed up in one of the 60 spaces, or as an alternate, she was
expected to come to the bike rental facility to be matched to a bike. Those who were
unable to receive a BLP bike due to sizing problems or availability were placed on a
waitlist. During the course of the year, more bicycles would be rented out to waitlisted
individuals in smaller numbers (approximately five every other week).
15
Because the bicycle facility for the Bike Loan Program was undergoing a
renovation immediately following the two September rental sessions, the ordinary system
of releasing new bikes to waitlisted students was put on a reduced schedule until the
renovation was complete. Only two waitlisted survey respondents were given bikes
during a release on October l5 t\ shifting them out of the control group and into the
experimental group.
Ultimately, only 40 bicycles were properly functioning and fully equipped for the
two check-out sessions. BLP staff chose 20 bikes for the September 19th session, making
sure to include a wide array of sizes, since that is the primary consideration when
matching a student to a bicycle. This meant that at least 10 prospective participants who
showed up to the first check-out session were necessarily not going to receive a bicycle,
since there were 30 prospective participants and only 20 bicycles; the same situation was
true for the September 20th session
Those prospective participants who signed up for one-hour time slots later in the
day were less likely to receive a bicycle than those who signed up for time slots earlier in
the day. This opens the possibility that those who were more motivated to receive a BLP
bike would have taken the initiative to sign up for an earlier slot in order to maximize
their chances of receiving a bicycle, potentially undermining the randomness of the bike
allocation.
A second non-random element of bicycle assignment was that smaller bikes ran
out earlier in the day than larger bikes. In general, taller prospective participants were
able to rent bikes at all three of the slots, while shorter prospective participants were put
16
on the waitlist if they had signed up for a later slot in the day. It is not known what
impact height might play in travel behavior, and height information was not collected in
the survey.
Finally, the academic year did not begin until Tuesday, September 29 t\ and so the
two rental sessions were nine or ten days prior to this date. Most students arrived the
weekend of the September 26th through 28th , meaning that only students who arrived
early or lived in the city of Eugene would have been able to sign up for a slot on one of
the two rental days. This was a primary reason that the International Studies Program
was targeted for students - those coming from abroad arrived in Eugene on the 15th of
September for orientation. This introduces a third element ofpossible non-randomness to
the study - only those in Eugene prior to the beginning of the academic year could
participate in the first round ofBLP bike rentals. Because of the above three elements of
potential non-random bicycle assignments, the demographic information and travel
preferences of the control group and experimental group were compared to see if there
were any statistically significant differences.
Surveys were electronically distributed to prospective participants based on the
information given when signing up on the posted list or when emailing BLP staff to
express interest in the program. Using this information, emails containing arbitrary ID
numbers and URLs to the online surveys were sent out on September 15th by BLP staff,
and reminders were sent daily until September 20th . The arbitrary ID numbers helped to
ensure the anonymity of the subjects' responses when analyzing the data and reporting
results.
17
For those who signed up for the 60 one-hour slots, every effort was made to
collect their information before the rental sessions on the 19th and 20th of September. For
the 32 who signed up to be on the wait1ist and the 25 who only emai1ed, but did not sign
up on the list, the period to respond to the survey closed on October 13th . Table 12 shows
the number of subjects who responded before the bike rental session.
The Time 2 survey was distributed in the same manner to those who answered the
Time 1 survey. On Wednesday, November 25th the URL to the Time 2 survey was
emai1ed to the 68 study participants of the Time 1 survey. Reminders were sent every
few days until Tuesday, January 6th, at which time the window to respond closed.
Variables
The survey was designed to answer the following key questions:
1. What travel behavior can be established?
2. What expectations exist regarding transportation modes in the absence of
program participation?
3. What are their attitudes and perceptions regarding various modes of
transportation?
The dependent variables used to answer these questions can be seen in Tables 3 to
5. Table 3 shows the Time 1 and Time 2 variables having to do with travel behavior.
The Time 1 questions asked what the expected mode of travel in the absence of a BLP
bike would be, and the Time 2 questions asked what the travel behaviors in fact were.
The Time 2 variables were then subtracted from the Time 1 variables to perform the
18
Difference-in-Difference (DiD) analysis. In both cases, subjects were able to choose
multiple modes.
Table 4 shows the Time I and Time 2 variables dealing with attitudinal questions.
These questions were identical at both survey times, making the analysis of the change
simpler. As with Table 3, the Time 2 responses were subtracted from the Time 1
responses for the DiD analysis.
Table 5 shows the variables asking about frequency of travel mode use during the
Fall term, and utilizes only Time 2 data. This is because many of the students had just
arrived on campus at Time I, and so no baseline travel behavior could be established.
Independent variable data were collected in the Time 1 survey and can be seen in
Table 6. Demographic information, questions regarding distance of living space from
campus, and automobile accessibility was included in these questions. A question
establishing whether the student received a bicycle was asked in the Time 2 survey (that
is to say, whether the student was in the control group or experimental group). This is
because the Time 1 survey was taken before the loan sessions, thus before the study
participant knew of which group she would be a part. Contact was made with BLP staff
to ensure that those who received a bicycle in the middle of the academic term were
shifted into the experimental group.
Some demographic questions were repeated on the Time 2 survey intentionally.
This was to reduce the likelihood of a study participant accidentally giving the incorrect
ID number when taking the Time 2 survey. This demographic information was used to
verify the accuracy of the given ID number.
- -_._---------
19
Some potentially confounding variables were not accounted for, however. As
discussed in the Chapter 2, concurrent efforts to improve bicycling can confound results
from studies looking at one particular program in a whole portfolio of treatments. There
were two main confounding factors in the Fall academic term of2009. The ftrst was the
city-wide effort to increase bicycling, including a more robust effort to keep bike lanes
clear of leaves and the on-going improvements in bicycling infrastructure. A new bike
bridge connecting a residential neighborhood across a previously inaccessible highway to
the Gateway Mall is one example of these infrastructure improvements.
The second confounding factor was the Bike Loan Program itself. The BLP not
only provided bikes, but also a free, fully staffed workspace for any student to maintain
his or her bicycle. On top of that, the Bike Loan Program sponsored communal bike
rides and other cultural events to support cycling on campus, which could have also had
an impact on people's use of bicycles.
Because the control groups and experimental groups were equally exposed to
these confounding factors, any changes found in travel behavior between the two groups
can be assumed not to be a result of these factors. Rather, it must be assumed that the
changes are the result of some variable one group was exposed to and the other was not,
such as participation in the Bike Loan Program.
Data Analysis
The ftrst step in analyzing the data was to recode the responses from frequency
statements (see Table 5 for the frequency response choices) into trips per week. The
20
recoding assignments are shown in Table 8. It should be noted that the recoded values
could undercount trips, since "daily" use of a particular mode could indicate multiple
trips per day, and not only one (or seven per week). However, since the number of trips
undoubtedly varied between study participants, a conservative approach was used.
The same variables were also recoded to calculate weekly usage rates. In this
case, any responses of "weekly" or more frequently were coded as "I" and responses of
"A Few Times per Month" and less frequently were coded as "0."
The initial analysis looked at the demographic information of the experimental
and control groups, then the aggregated demographic information of both groups. Lastly,
this information was compared against the university population as a whole. T-tests were
performed to see if there were any statistically significant differences between the
experimental and control groups.
The dependent variable data from Tables 3 - 5 were analyzed in three different
ways. The first used only Time 2 data from Table 5. T-tests were used to compare the
mean number of trips per week using each mode across the control and experimental
groups. As can be seen in Table 5, there were two Time 2 variables for biking-
frequency of BLP bike use, and frequency of non-BLP bike use. These two were
aggregated into one "Fall Bike Use" variable, and in general those in the experimental
group did not use non-BLP bikes and those in the control group did not use BLP bikes.
See Table 9 for the disaggregated use rates.
The second analysis compared the change between Time 1 and Time 2 questions
regarding attitudinal shifts, shown in Table 4, also known as a difference-in-difference
21
(DiD) analysis. The DiD analysis is a method of controlling for unseen static
independent variables that might create a response bias. A second DiD analysis was
performed on the travel behavior change variables in Table 3.
The third level of analysis was a regression to control for independent variables
shown in Table 6 that might influence travel behavior. The regression looked at both
Time 2 mode shifts (Table 5) and Time 1 and 2 attitudinal change data (Table 3). The
regression analysis helps to control for any imbalances between the control and
experimental groups, removing potentially confounding impacts of other independent
variables.
Sensitivity Tests
Because some of the experimental group might have been influenced on their
decision to fill out a survey once the bicycles were issued, a sensitivity test was
performed on the above-described data analyses. As seen in Table 12, those subjects who
responded to the Time 1 survey after the bike rental sessions were excluded from the
analysis. This helps to mitigate potential response bias, particularly among the
experimental group.
Anonymity and Incentives
The University of Oregon's Internal Review Board gave approval in August 2009
for this project, and as described in the application all survey responses were anonymous.
To provide an incentive for the students to complete the entrance survey, study
----- ---------------
22
participants could enter a drawing for a $20 gift certificate to the University of Oregon's
Book Store if they provided contact information. The drawing took place, and a student
was awarded the prize. Since completion of the exit survey was also paramount in
obtaining useful data, $5 in cash was offered to those who completed both the entrance
and exit surveys. Twenty out of the 59 Time 1 and 2 survey respondents collected their
$5 compensation.
23
CHAPTER IV
FINDINGS
Described below, the data analysis consistently yielded statistically significant
findings with regard to mode shift. Attitudinal change was less consistent in statistical
significance. The results must be taken as suggestive, however, and not conclusive, due
to the likelihood of response bias. Despite the effort to randomize the population, the
response rate shows a much higher percentage of experimental group respondents than
control group, indicating a non-random element in the bike allocation process.
Response Rate
The response rate for the Time 1 survey was 57.3% (See Table to). Of the 117
prospective participants, 11 provided illegible or incorrect email addresses. None of those
11 prospective participants ever received a bicycle, and so came out ofthe control group.
This resulted in 106 successfully contacted for participation in the study. Ofthese, 61
responded online to the survey. An additional six filled out paper surveys, as opposed to
online surveys, on the day they were signed up to receive a bicycle. The paper surveys
were administered while the prospective participants waited for their time slot to begin.
It is unlikely that paper surveys would affect the answers given, since the students were
aware of the fact that there was more demand than supply of BLP bicycles. Thus, the
argument can be made that the six who filled out the paper survey were equally as unsure
24
of whether they were going to receive a bike as those who filled out an online survey.
Nonetheless, they were removed from the sample during the sensitivity testing.
As Table 11 shows, the response rate among the control group went down
between Time 1 and Time 2. The reason for some of those in the control group choosing
not to participate in the second round of surveys is possibly because they did not feel as
"invested" in the Bike Loan Program as the experimental group. It should also be noted
that two of the subjects in the control group were assigned bicycles midway through the
term, and thus were shifted into the experimental group. This is why the experimental
group actually increases in size between Time 1 and Time 2. Ultimately, the Time 2
response rate was 50.4% out of the origina111? prospective program participants.
Tables 10- 12 indicate that there was probably response bias, despite the
intended randomness of bicycle allocation. In all three tables, the experimental group has
well over three times the response rate of the control group. Assuming that the bike
allocation was random, those in the experimental group should have been equally as
likely to fill out a survey as those in the control group. While it is unclear from where
this bias comes, it may be a result of the fact that those who sign up for an earlier slot in
the bike rental sessions are more likely to get a BLP bike. Furthermore, it may be that
those who are "pro-bike" enough to sign up early for a BLP bike are also more likely to
fill out a survey for a study. Table 13 corroborates this intuition, showing that the
experimental group came from those who signed up earlier in the day as opposed to those
who signed up for later slots.
25
Demographic Information
As can be seen in Table 14, there are few demographic similarities to the
university as a whole. Women, international students, and graduate students in particular
were overrepresented in the study participants. For other measures, such as car
ownership, campus commute distance, and income, no information for the university as a
whole could be found. The differences suggest that the results of this study cannot be
extended to the University-wide population.
The demographics between the control and test groups showed more similarities
to each other than to the campus as a whole. As seen in Table 15, there was an absence
of statistically significant demographic differences which confirms the two groups'
similarities. In both groups, there were high proportions of international students,
graduate students, and women, although the control group graduate students were less
disproportionately represented. Other areas, such as income, distance from campus, and
car ownership/access were similar between groups. However, it should be noted that age
and status a graduate or undergraduate student were almost different at 5% significance.
A second test of this homogeneity was a comparison of mean scores in the
variables included in the category "Mode of Travel without a BLP Bike" described in
Table 3. Had statistically significant differences been found, such a result would show
that the groups were not homogenous in their travel preferences, which might have
indicated a non-random element in the participant selection process. However, as can be
seen in Table 16, there was no statistically significant difference between test and control
26
groups. This suggests that the travel mode preferences or expectations were not different
between groups, meaning that neither group was predisposed to using one mode of
transportation over another, and indicating homogeneity between the two groups.
The above tests would indicate a randomly chosen allocation into the control and
experimental groups. However, this finding is contradicted by the response bias
discussed above. While the statistical tests did not yield any significant findings, the
response bias does suggest a non-random bike allocation process.
Cross-Sectional Analysis
Using t-tests on the Time 2 travel behavior data described in Table 5, a cross-
sectional look at the two groups showed significant results. As seen in Table 17, there is
a statistically significant difference between the number of trips taken per week on bike
versus public transit between the control group and experimental group. The control
group reported 3.74 fewer bike trips per week as compared to the experimental group. At
the same time, the experimental group reported just over 2.26 fewer public transit trips
per week versus the control group. No statistically significant results were found in car
use or walking between the control and experimental groups.
Statistically significant findings were also produced when the weekly usage rate
was compared. As seen in Table 19, there was a significantly higher percentage of study
participants in the experimental group who biked at least once per week as compared to
the control group. Similarly, those in the control group used public transit and walked
27
more than those in the experimental group. Again, no statistically significant differences
were found in the use of cars to travel to any destination.
Difference-in-Difference Analysis
The DiD analysis looked at both travel expectations versus actual mode choice,
and attitudes; the former showed stronger statistically significant findings. The question
was worded so that potential participants should imagine what their travel behavior
would have been in the absence of a BLP bike; thus, if the control group was totally
accurate, there would have been no change between their prediction and actual mode
choice.
Table 21 shows that the statistically strongest difference was between expected
and actual bike use. The control group overestimated the amount that they would bicycle,
with an overall 60% decrease in biking activity over expectations. The experimental
group showed an increase of bicycling activity versus a no BLP bike scenario, with
bicycling 10% above expectations.
Table 21 also shows a statistically significant decrease in public transit usage over
expectations among the experimental group, with a 41 % reduction below expected use
rates. In the control group, public transit expectations were met, with the difference
between expected and actual use being zero.
Table 23 shows the change in attitudes between the Time 1 and Time 2 surveys.
The only statistically significant findings were in the perception ofphysical health. On a
5-point scale, the control group rated themselves as .4 points worse in the Time 2 survey,
28
whereas the experimental group rated themselves .1 points better. There were no other
statistically significant findings.
Multivariate Regression
The multivariate regression did not maintain all of the findings from the t-tests
after controlling for several independent variables. Table 25 shows that after controlling
for age, gender, student status, car ownership and access, income and distance from
campus, the experimental group is still more likely to take an increased number of bike
trips per week as compared to the control group. However, the statistical significance
with regard to walking and public transit trips does not hold in the regression.
Two other independent variables were significant predictors of mode choice. The
first is proximity to campus. Perhaps unsurprisingly, those who live within half a mile of
campus ("walking distance") are more likely to walk. A second statistically significant
factor was whether one was an international student, with international students taking
2.03 more bike trips per week more than undergraduates.
Attitude differences, as shown in Table 28, showed some statistically significant
fmdings when controlling for other variables. Those in the experimental group were
more likely to have a positive feeling about personal health. The regression also showed
that international students are more likely to have an improvement in their confidence in
mechanical knowledge and skills with bikes, and to have a positive feeling about their
health.
29
Sensitivity Analysis
The sensitivity analysis showed that the most statistically significant finding, that
the Bike Loan Program increases the use of bicycling, to still be significant after
removing the questionable data. Table 18 maintains the findings in the original analysis
shown in Table 17. The sensitivity test in Table 20 shows that the modes used at least
one time per week has the statistically significant findings reduced. The Difference-in-
Difference analyses were largely unchanged in sensitivity testing (Table 22 and 24),
however the finding regarding improved sense of personal health was no longer
significant.
The sensitivity analysis for the regressions (Table 27 and 30) showed similar
results to the full analysis, with biking being the only mode significantly impacted by
participation in the Bike Loan Program. However, the health impacts are still
significantly impacted in the sensitivity analysis.
30
CHAPTER V
DISCUSSION
This data analysis has important implications for the design of the Bike Loan
Program, depending on the desired outcomes. As was seen in the findings section, the
statistically strongest results were that the experimental group did, in fact, bicycle more
often than the control group. However, this appears to have been at the expense of public
transit and walking trips, with no impact on car use.
However, because of the response bias, these findings must be taken as suggestive
and not conclusive. Since the prospective participants who filled out the entrance survey
were disproportionately those who also received a bicycle, it can be presumed that
whatever variable predisposed them to respond to the survey may have also facilitated
participation in the Bike Loan Program. One likely scenario is that those who were
already "pro-bike" were able to sign up for an early slot on one of the two rental days,
thus ensuring their participation in the BLP. This same enthusiasm for the BLP was what
motivated them to fill out the survey for this study. Thus, their responses would be
biased with "pro-bike" feelings, meaning that it is uncertain what impact the BLP
actually had on their actions. While other statistical demographic comparisons between
the groups did not yield any significant differences, this key aspect of the study
compromises the conclusiveness of the findings.
31
Prospective Participant Pool
As described above, the demographic infonnation showed some interesting
contrasts between the population targeted by the Bike Loan Program and the University
of Oregon population at large. While only 6.5% ofUO students are international
students, there are almost ten times as many represented in the overall sample for this
study. However, this proportion of international students is unsurprising given a
partnership between the BLP and the International Studies Office.
The targeting of international students may be counterproductive to the goal of
reducing the environmental impact of the campus. Since international students studying
for one year or less at the university are unlikely to invest in a car, these students are
unlikely to have a car to forgo, which is key to reducing GHG emissions or alleviate
parking pressures. This is corroborated by the fact that only six study participants
reported owning a car, constituting just 10.5% of the sample.
Health
The evidence indicates that experimental group members do, in fact, consider
themselves healthier as compared to the control group. The t-tests and regressions all
show that this dependent variable is significantly impacted by participation in the BLP.
Logically, this conclusion makes sense; the most consistently significant mode shift is
from public transit to bicycles for those in the experimental group. This means that
32
students are shifting from a passive to an active mode of transportation, and this physical
activity could contribute to a stronger feeling of health.
Further study is needed to determine if the experimental group actually is
healthier than the control group. For one thing, a feeling of health does not necessarily
equate actual health. Furthermore, there is no evidence that a leisurely bike ride is better
for one's health than a brisk walk, for example.
Mobility and Equity
There is some evidence of increased mobility resulting from the Bike Loan
Program through self-reported trip rates. The number of walking and transit trips is
exceeded by the number of biking trips replacing them (see Table 25). This could
indicate a number of things: it could mean that bicycles provide increased mobility, and
so the experimental group no longer chained trips together that might accompany a
slower walking trip, or a more complex transit trip. It could also mean that the bike trip
itself was an end, with more recreational outings.
Furthermore, there is a logical increase in accessibility of goods and services
when walking trips are replaced by biking trips. Walking is typically very slow (2 - 3
mph), whereas even slow biking is five times faster. This means that the same
destinations can be accessed in one-fifth the time, or destinations five times further away
can be accessed in the same amount of time, versus walking. Thus, it can be inferred that
the Bike Loan Program has successfully increased mobility for the experimental group.
33
The data also demonstrate that low-income individuals were by and large the
typical recipients of BLP bikes. Based on the section of the mission statement hoping to
increase the accessibility of affordable transportation options, the Bike Loan Program
appears to have been a success.
Environmental Impact
The environmental goal of the program was stated as "reducing the environmental
impact of campus." The environmental impacts of this program can be estimated based
on the number of trips generated or reduced as a result ofbicycle allocation. Since the
evidence shows the control and experimental groups to be statistically similar, we can
assume that all 117 students in the original prospective participant pool would have
exhibited the same mode shifts had they participated in the program. If this is the case,
we can estimate conservatively an increase of 3.69 bike trips and a decrease of 1.59
public transit trips per week per student (see Table 25). Across 117 students, that is a
reduction of 204 transit trips per week or 2,046 trips per academic term. However, that
also means there is an increase of bicycling trips by 431 per week, or 4,749 trips per
academic term.
Even though we can estimate the transit trip reduction resulting from the program,
it is difficult to assess the environmental impacts. Because of the limited number of
students in the program (42 out of 22,386), it is unrealistic to assume that bus frequency
would be reduced based on this mode shift. The other finding, that experimental group
members replaced walking trips with biking trips, means that a zero-emissions mode is
34
being replaced with a zero-emissions mode. Thus, it can be assumed that emissions
remained essentially unaltered in the short term.
Longer term findings might reveal something more substantial. As mentioned in
Chapter Three, the Bike Loan Program services included a free bike maintenance
workspace, cultural events, and short-term bike rentals. Upon consideration, it is quite
surprising that a significant mode shift was found over a relatively short amount of time
(six weeks between the end of the Time 1 data collection period and the beginning of
Time 2 data collection). It is unknown whether these students continued biking at an
elevated rate after their participation in the BLP ended, or what impact these other bike-
encouraging activities might have. It is conceivable that this program could act as a
catalyst to spur longer-term bicycle use among a broader array of students. As those who
have rented and returned long-term bike loans increase in number over the years, the
impacts could be broader.
Future Studies
Future studies should try to ascertain the applicability of the BLP impacts to the
wider campus population. If, for example, the BLP bike fleet were increased by a factor
of ten, would the mode shift from public transit to biking also increase by a comparable
amount? This question could be answered by analyzing the existing data and weighting
the cases based on demographic information.
Another research direction based on this study would be determining the longer-
term impacts of the BLP on the experimental and control group. This would require a
35
follow-up survey at a Time 3 sometime after those in the experimental group had left the
program. A third survey would allow lingering impacts of the program, such as
continued use of bikes instead of transit, to become apparent.
Finally, a geo-spatial analysis of the existing dataset could reveal interesting
findings. As was shown in Table 25, those who live within 0.5 miles of the university are
more likely make walking trips, controlling for other factors. It could be interesting to
determine if there is a catchment area around the university wherein bicycling is
maximized - perhaps those who live within 0.5 miles walk more and use transit and bikes
less, those within 0.5 to 2 miles of the university are likely to bike or use transit, and
those more than 2 miles from the university are most likely to drive. Determining this
catchment area could also help the BLP to better target potential participants.
Conclusion
The Bike Loan Program appears to have been successful in many respects. The
number of students using bicycles is probably greater than in the absence of such a
program. There is evidence that the health of the students is at least perceived to have
been improved, and increased use of active transportation would logically verify that
finding. And it would appear that mobility may have improved based on an increased
number of bike trips as compared to the walking and transit trips replaced.
In order to reduce the environmental impact of the campus through tailpipe
emissions abatement, and potentially alleviate parking pressures, it is recommended that
the Bike Loan Program consider changing its targeted student participants. In some
36
ways, international students comprise the ideal target group for the program (at the
university for a limited duration, early arrivers in Eugene, etc.). And, furthermore, there
may be value in accommodating international students to provide incentive for expanded
international student presence at the University. Nonetheless, rather than focusing on
international students, it might be more effective to form a partnership with UO Parking
Services to target those students who have applied for parking permits in order to reach
the program's environmental goals. One model could be providing materials for the Bike
Loan Program along with the application for a parking permit, and a cost comparison to
make a bicycle rental competitive with a permit. Since the opportunity cost of simply
providing targeted advertising materials is relatively low, it is recommended that the BLP
pursue this course of action.
There is also the question ofwhether or not the Bike Loan Program is the most
effective use of $18,000 in the pursuit of the stated environmental, health, and equity
goals. For example, another model might have been to provide a low- or no-interest loan
to students to buy a bike of their own, if it is discovered that the initial investment is a
barrier to increased bike use. While this study is not a benefit-cost analysis of the Bike
Loan Program, a future study could build off of these findings to attempt to quantify the
benefits provided by the BLP.
In all, the findings of this study are consistent with the existing literature showing
that the bulk of mode shift with such programs is from transit to bicycles. On a larger
scale, this could result in reduced bus frequency or eliminated routes, but that is unlikely
to happen in the near term. To address GHG emissions and parking pressures, deliberate
targeting of choice student populations who are likely to own and use cars should be
undertaken. In this way, the program may be able to achieve its environmental goal in
the shorter-term.
37
APPENDIX A
TABLES
Table 1. Self-Reported Mode Replaced by Shared Bike
City/Program Public Transit Walk Car Other
LyonlVelo'v 50.6% 36.7% 6.7% 6.0%
BarcelonalBicing 51.0% 26.0% 10.0% 13.0%
ParisNelib 65.0% 20.0% 8.0% 7.0%
38
Table 2. Chronology of Events
Date Event Notes
Before September Prospective participants are 25 prospective participants inquire
12th directed to sign up for the about the program, but never sign
Bike Loan Program up on the posted sheet
September 12th 92 students sign up for the A sign-up sheet is posted in the
bike loan program BLP administrative office
September 15th Surveys sent to 117 program 11 email addresses were invalid.
applicants Those emailed included 25
prospective participants who did
not sign up on the posted sheets.
September 19 41 students complete Time 1 6 program applicants completed
surveys prior to bike paper copies of the survey prior
assignments to bike assignments on Sept 19,
20.
September 19 & 20 40 students receive bikes
(experimental)
77 students placed on waiting
list (control)
September 19 - 20 students complete online
October 13 surveys
November 25 Time 2 survey emailed to 67 2 Control Group Subjects
students who completed Time received a BLP bike on October
1 Survey 15.
January 6 Time 2 data collection ends 59 subjects submit survey
responses.
39
Table 3. Dependent Variables for Travel; Times 1,2, and Change
Potential
Time Variable Variable Name Answers Notes
NoBLPPubTrans Public Transit
Mode of travel
NoBLPWalk Walk
Drive a carTime 1 without a BLP
bike. NoBLPDrive (Alone)
NoBLPCarpool Carpool
NoBLPBike Bike Anyway
FallPubTrans Public Transit
FallWalk Walk
FallCarpool Carpool
Primary Mode
Aggregate tripsTime 2 of Travel in
Fall 2009. FallBike Bike taken on both BLP
andnon-BLP
bikes.
Drive a Car
FallDrive (Alone)
FallPubTrans -
PubTransDif -- NoBLPPT
Change
FallWalk -
WalkDif -- NoBLPWalk
Time 2- between FallDrive -
expectationsTime 1
and actual DriveDif -- NoBLPDrive
actions. FallCarpool-
CarpoolDif -- NoBLPCarpool
FallBike -
BikeDif -- NoBLPBike
40
Table 4. Dependent Variables for Attitudes; Times 1,2, and Change
Time Variable Variable Name Notes
EnjoyBikel, Attitude Questions on aEnjoy Bikes Scale of I - 5, from StronglyEnjoyBike2 Disagree to Strongly Agree
Confident Cyclist ConVCycl,ConVCyc2
No Mechanical NoMechl, NoMech2SkillsTime 1,2
Healthy Person HealthI, Health2
Bike facilities are BikeImpl, BikeImp2Important
Transit is TransImp I, TransImp2Important
Car facilities are
DriveImp I, DriveImp2Important
EnjoyBikeDif EnjoyBike2 - EnjoyBikel
ConVCycDif ConVCyc2 - ConVCycl
NoMechDif NoMech2 - NoMechl
Time 2 - Time I Change Between HealthDif Health2 - HealthlTime 1 and Time 2 BikeImpDif BikeImp2 - BikeImpl
TransImpDif TransImp2 - TransImp I
DriveImpDif DriveImp2 - DriveImp I
Table 5. Dependent Variables for Travel; Time 2
Time Variable Name Notes
Response options were: Daily;
Frequency of BLP
BLPFreq Several Times per Week; Once a
Bike Use. Week; A Few Times per Month;
A Few Times per Year; Never
Frequency ofNon- NBLPF NBLPFreq and BLPFreq were
BLP Bike Use. req aggregated into BikeFreq
Time 2 Frequency of PubTransFreqTransit Use.
Frequency of Car
Use (Driving DriveFreq
Alone).
Frequency of WalkFreqWalking.
41
Table 6. Independent Variables; Times 1 and 2
Time Variable Name Notes
.
Time 2
Time 1 preceded BLP Rental
Program Participant Prgprt Sessions
Year in School Stustat
International Student StuIntl
Age Age
Gender Gender
Possible Answers: Under
Time 1
$10,000; $10,001 - $15,000;
$15,001 - $20,000; $20,001 -
Annual Income Income $30,000; Over $30,001
Live within Walking
Distance ofCampus Walkdist
Own a Car Carown
Have Access to a Car Caracc
Table 7. Minima, Maxima, and Standard Deviations for Variables
Min. Max. SID
Prgprt 0.0 1.0 0.477
Gender 0.0 1.0 0.471
Age 18.0 44.0 4.513
StuIntl 0.0 1.0 0.497
Income 0.0 1.0 0.131
WalkDist 0.0 1.0 0.504
StuStat 0.0 1.0 0.504
CarOwn 0.0 1.0 0.305
CarAcc 0.0 1.0 0.479
PubTransFreq 0.0 7.0 2.440
WalkFreq 0.0 7.0 2.730
DriveFreq 0.0 7.0 0.800
BikeFreq 0.0 7.0 2.920
42
Table 8. Travel Mode Frequency Response Values
Response Value
Daily 7.00
Several Times per Week 3.50
Weekly 1.00
A few Times per Month 0.50
A few Times per Year 0.25
Never 0.00
Table 9. BLP Bike versus Non-BLP Bike, Number of Times per Week
Control Experimental
N 19 40
BLP Bike 0.05 5.00
Non-BLP Bike 1.19 0.30
Table 10. Time 1 Response Rate
Experimental Control Total
Responses 38 29 67
Total 40 77 117
Response Rate 95.0% 37.7% 57.3%
Table 11. Time 2 Response Rate
Experimental Control Total
Responses 40 19 59
Total 42 75 117
Response Rate 95.2% 25.3% 50.4%
Table 12. Time I Responses Received before the Rental Sessions*
Experimental Control Total
Responses 31 10 41
Total 40 77 117
Response Rate 77.5% 13.0% 35.0%
*Does not mclude responses from paper surveys.
- .. _----------
43
Table 13. Responses by Time Slot
September 19th September 20th
Experimental Control Total Experimental Control Total
Slot 1 9 3 13 9 3 18
Slot 2 6 1 13 4 6 19
Slot 3 5 1 12 5 8 17
Total 20 5 38 18 17 54
*Total = Survey Reponses + Non-Responders
**Seven Control Group Subjects did not sign up on posted sheets
Table 14. Demographic Comparison to University
All University of
Demographics Oregon*
N 57 22,386
Age (Mean) 23.6 23.2
Male 32.2% 49.0%
Female 67.8% 51.9%
International Student 58.6% 6.5%
Income < $20K 96.6% --
Live w/in 0.5 mi 52.5% --
Freshman 8.5% 22.3%
Sophomore 13.6% 18.8%
Junior 11.9% 17.5%
Senior 15.3% 21.3%
Graduate Student 47.5% 15.5%
Have Car Access 36.7% --
Own a Car 10.0% --
*From 2009 OUS Factbook
44
Table 15. Demographic Comparison between Groups
Demographics All Control Experimental P-Value
N 59 19 40
Age (Mean) 23.6 22.1 24.5 0.056
Male 32.2% 25.0% 36.0% 0.405
Female 67.8% 75.0% 64.0% --
International Student 58.6% 65.0% 54.0% 0.483
Income < $20K 96.6% 100.0% 93.0% 0.166
Live w/in 0.5 mi 52.5% 45.0% 66.0% 0.415
Undergraduate 52.5% 70.0% 43.6% 0.053
Graduate Student 47.5% 30.0% 56.4% --
Have Car Access 34.0% 27.0% 38.0% 0.476
Own a Car 10.0% 10.0% 10.0% 0.976
*p < .05; ** P < .01; *** P < .001
Table 16. Travel Expectations in Absence of Bike Loan Program
Control Experimental P-Value
N 19 40
NoBLPBike 80% 67% 0.270
NoBLPWalk 80% 62% 0.134
NoBLPDrive 5% 10% 0.457
NoBLPCarpool 5% 5% 0.983
NoBLPPubTrans 80% 74% 0.637
*p < .05; ** P < .01; *** P < .001
45
Table 17. Trips per Week for Various Modes Used to Travel to any Destination
All Responses Control Experimental
Mean SID Mean SID Mean SID P vallie
N 59 19 40
FallBike 3.75 2.92 1.24 *** 1.92 5.05 2.47 0.000
FallPubTrans 2.38 2.44 3.90 *** 2.60 1.60 1.96 0.000
FallDrive 0.37 0.80 0.50 1.07 0.30 0.62 0.371
FallWalk 2.87 2.73 3.70 3.00 2.45 2.51 0.095
*p < .05; ** P < .01; *** P < .001
Table 18. Sensitivity Test of Times per Week Used Excluding Late Time 1 Responses
All Responses Control Experimental
Mean SID Mean SID Mean SID P vallie
N 41 10 31
FallBike 4.27 2.87 1.45 *** 2.42 5.19 2.38 0.000
FallPubTrans 2.37 2.43 4.08 ** 2.76 1.82 2.08 0.009
Fa11Drive 0.31 0.62 0.25 0.41 0.32 0.68 0.751
FallWalk: 2.64 2.70 3.25 3.39 2.44 2.43 0.499
*p < .05; ** P < .01; *** P < .001
Table 19. Percentage of Responses Indicating Mode Used at Least Once per Week.
All Responses Control Experimental
Mean SID Mean SID Mean SID Pva1ue
N 59 19 40
FallBike 70% 0.46 35% *** 0.49 87% 0.34 0.000
FallPubTrans 64% 0.48 90% ** 0.31 51% 0.51 0.001
FallDrive 15% 0.36 20% 0.41 13% 0.34 0.476
FallWalk: 63% 0.49 80% * 0.41 54% 0.51 0.038
p < .05; ** P < .01; *** P < .001
46
Table 20. Sensitivity Test of Use Weekly Use Rates Excluding Late Time 1 Responses
All Responses Control Experimental
Mean SID Mean SID Mean SID P value
N 41 10 31
FallBike 76% 0.44 30% ** 0.48 90% 0.30 0.003
FallPubTrans 66% 0.48 90% 0.32 58% 0.50 0.026
FallDrive 15% 0.36 20% 0.42 13% 0.34 0.592
FallWalk 59% 0.50 60% 0.52 58% 0.50 0.917
p < .05; ** P < .01; *** P < .001
Table 21. Travel Expectations versus Mode Used (Time 2 - Time 1)
All Responses Control Experimental
Mean STD Mean SID Mean STD P-Value
N 59 19 40
PubTransDiff -27% 0.52 0% ** 0.46 -41% 0.50 0.003
WalkDiff -36% 0.58 -35% 0.67 -36% 0.54 0.959
DriveDiff -8% 0.28 -5% 0.22 -10% 0.31 0.501
CarpoolDiff -3% 0.26 -5% 0.22 -3% 0.28 0.737
BikeDiff -14% 0.66
-60% *** 0.60 10% 0.55 0.000
p < .05; ** P < .01; *** P < .001
Table 22. Sensitivity Test of Expected Mode versus Actual Excluding Late Time 1 Responses
All Reponses Control Experimental
Variable Mean SID Mean SID Mean SID P-Value
N 41 10 31
PubTransDiff -27% 0.55 20% * 0.42 -42% 0.50 0.001
WalkDiff -39% 0.59 -40% 0.70 -39% 0.56 0.950
DriveDiff -5% 0.22 0% 0.00 -6% 0.25 0.420
CarpoolDiff -2% 0.27 -10% 0.32 0% 0.05 0.319
BikeDiff -12% 0.60
-70% *** 0.48 6% 0.51 0.000
p < .05; ** P < .01; *** P < .001
47
Table 23. Change in Attitudes and Perceptions (Time 2 - Time 1)
All Responses Control Experimental
Mean SID Mean SID Mean SID P-Va1ue
N 59 19 40
EnjoyBikeDif 0.08 0.73 -0.05 0.76 0.15 0.71 0.311
ConfCyclDif 0.12 1.00 -0.10 1.02 0.23 0.99 0.233
NoMechDif -0.08 1.10 0.00 1.12 -0.13 1.11 0.676
HeahhDif -0.07 0.74 -0.40 * 0.82 0.10 0.64 0.023
BikeImpDif -0.17 0.89 -0.30 0.87 -0.10 0.91 0.426
TransImpDif 0.02 0.90 -0.30 0.80 0.18 0.91 0.052
CarImpDif -0.05 1.22 -0.30 1.46 0.08 1.09 0.315
Table 24. Sensitivity Test of DiD Attitudes Excluding Late Time 1 Responses
All Responses Control Experimental
Statement Mean SID Mean SID Mean SID P-Value
N 41 10 31
EnjoyBikeDif 0.07 0.61 0.00 0.82 0.10 0.54 0.670
ConfCyclDif 0.02 0.96 -0.20 1.10 0.10 0.91 0.400
NoMechDif -0.07 1.17 -0.20 1.40 -0.03 1.11 0.700
HeahhDif 0.00 0.67 -0.30 0.82 0.10 0.60 0.100
BikeImpDif -0.20 0.68 -0.30 1.06 -0.16 0.52 0.580
TransImpDif -0.05 0.63 -0.40 * 0.70 0.06 0.57 0.040
CarImpDif -0.02 1.37 -0.40 1.84 0.10 1.19 0.440
p < .05; ** P < .01; *** P < .001
48
Table 25. Time 2 Travel Behavior Multivariate Regression
PubTransFreq WalkFreq DriveFreq BikeFreq
R-Square 0.36 0.84 0.17 0.53
Program Participant -1.59 -1.32 -0.39 3.69 ***
Female -0.20 0.04 0.37 -0.09
Age 0.04 -0.05 0.04 -0.12
futernational Student 0.15 -0.30 0.30 2.03 *
fucome < $20K -- -- -- --
fucome > $20K -0.84 -0.97 -0.01 3.49
Live w/in 0.5 mi -0.55 2.50 ** -0.30 -0.66
Undergraduate -- -- -- --
Graduate Student -1.34 0.54 0.17 0.98
Own a Car 3.89 1.47 -0.34 1.05
Have access to a car 1.84 * -1.58 0.00 0.56
p < .05; ** P < .01; *** P < .001
Table 26. P-Values and T-Statistics
PubTransFreq WalkFre DriveFreq BikeFreq
P T P T P T P T
Constant 0.150 -1.47 0.126 1.57 0.298 -1.06 0.008 2.81
Program Participan 0.056 1.98 0.129 1.55 0.244 1.18 0.000 -4.51
Female 0.785 -0.28 0.960 0.05 0.229 1.23 0.905 -0.12
Age 0.743 0.33 0.704 -0.38 0.455 0.76 0.334 -0.98
International Studet 0.857 0.18 0.730 0.35 0.383 -0.88 0.022 -2.41
fucome < $20K -- -- -- -- -- -- -- --
fucome > $20K 0.731 -0.35 0.707 -0.38 0.996 -0.01 0.166 1.42
Live w/in 0.5 mi 0.467 0.74 0.003 -3.18 0.332 0.99 0.391 0.87
Undergraduate -- -- -- -- -- -- -- --
Graduate Student 0.189 -1.34 0.613 0.51 0.685 -0.41 0.343 0.96
Own a Car 0.053 2.01 0.481 -0.71 0.676 0.42 0.599 -0.53
Have access to a Ci 0.03 -2.31 0.068 -1.88 0.991 -0.01 0.497 0.69
49
Table 27. Sensitivity Test ofMode Use Regression Excluding Late Time 1 Responses
PubTransFreq WalkFreq DriveFreq BikeFreq
R-Square 0.28 0.25 0.06 0.48
Constant -5.95 -7.03 1.51 16.89
Program Participant -1.90 -1.16 0.02 3.86 ***
Female -0.22 0.25 0.14 0.10
Age 0.02 0.10 0.00 -0.20
International Student -0.86 -0.55 0.10 1.28
Income < $20K -- -- -- --
Income> $20K -0.94 -0.60 -0.26 2.07
Live w/in 0.5 mi -0.93 1.97 -0.08 -0.51
Undergraduate -- -- -- --
Graduate Student 0.22 0.13 0.02 0.95
Own a Car -1.86 -3.13 0.61 1.83
Have access to a car -0.04 -0.15 0.03 0.07
p < .05; ** p < .01; *** p < .001
Table 28. Change in Attitudes and Perceptions Multivariate Regression
50
EnjoyBikeDif ConfCycIDiJ NoMechDif HealthDif BikeImpDif TransImpDif CarImpDif
R-Square 0.21 0.19 0.30 0.26 0.05 0.19 0.16
Constant -0.63 0.82 5.06 2.80 -1.27 -0.10 1.34
Program Participant 0.14 0.68 0.16 0.59 * 0.18 0.32 0.30
Female 0.01 0.32 -0.38 -0.15 0.34 0.21 -0.54
Age 0.04 0.06 -0.06 -0.03 0.03 -0.03 0.02
International Student 0.28 -0.21 1.06 ** 0.54 * 0.09 0.21 0.22
Income < $20K -- -- -- -- -- -- --
Income > $20K -0.07 -0.36 1.85 1.27 0.13 -0.24 1.55
Live w/in 0.5 mi 0.45 0.29 -0.10 0.13 0.16 -0.16 -0.62
Undergraduate
-- -- -- -- -- -- --
Graduate Student -0.50 -0.28 0.43 -0.10 -0.22 0.01 0.25
Own a Car -0.69 0.38 -0.83 0.09 -0.22 -0.60 0.73
Have access to a car -0.Q2 0.23 0.34 -0.05 -0.15 -0.52 0.09
p < .05; ** P < .01; *** P < .001
Table 29. P-Values and T-Statistics
51
EJ1ioyBikem ConfCycDif NoMechDif HealthDif BikeImpDif TranslmpDif CarImpDif
P T P T P T P T P T P T P T
Constant 0.710 -0.38 0.715 0.37 0.028 2.29 0.101 1.69 0.603 -0.52 0.957 -0.06 0.653 0.45
Program
Participant 0.591 -0.54 0.055 -1.98 0.650 -0.46 0.027 -2.31 0.630 -0.49 0.287 -1.08 0.514 -0.66
Female 0.980 0.03 0.316 1.02 0.241 0.24 0.521 -0.65 0.331 0.99 0.444 0.77 0.215 -1.26
Age 0.317 1.02 0.300 1.05 0.265 -1.11 0.484 -0.71 0.584 0.55 0.511 -0.66 0.764 0.30
International
Student 0.306 -1.04 0.554 -0.60 0.005 -3.00 0.049 -2.04 0.823 -0.23 0.487 -0.70 0.650 0.46
Income < $20K -- -- -- -- -- -- -- -- -- -- -- -- -- --
Income> $20K 0.931 -0.09 0.732 -0.35 0.083 1.79 0.112 1.63 0.908 0.12 0.790 -0.27 0.272 1.12
Live w/in 0.5 mi 0.073 -1.85 0.381 -0.89 0.763 0.30 0.591 -0.54 0.646 -0.46 0.558 0.59 0.157 1.45
Undergraduate
-- -- -- -- -- --
-- -- -- -- --
-- -- --
Graduate
Student 0.133 -1.54 0.522 -0.65 0.558 0.59 0.748 -0.32 0.641 -0.47 0.984 -0.02 0.664 0.44
Own a Car 0.284 1.09 0.654 -0.45 0.284 -1.09 0.886 0.15 0.811 0.24 0.398 0.86 0.518 -0.65
Have access to
a car 0.955 0.06 0.500 -0.68 0.725 0.36 0.854 -0.19 0.684 -0.41 0.083 -1.79 0.847 0.20
Table 30. Sensitivity Test of DiD Attitudes Excluding Late Time 1 Responses
EnjoyBikeDif ConfCycIDiI NoMechDif HealthDif BikeImpDif TransImpDif CarImpDif
R-Square 0.17 0.13 0.15 0.19 0.19 0.16 0.17
Constant -1.84 1.91 0.42 0.99 -0.23 0.14 -3.14
Program Participant 0.13 0.38 -0.22 0.57 * 0.38 0.49 * 0.50
Female 0.01 0.19 -0.24 -0.02 0.17 -0.08 0.62
Age 0.00 -0.03 -0.07 -0.02 0.00 0.01 -0.03
International Student -0.44 -0.15 -0.66 -0.06 -0.33 0.03 -0.50
Income < $20K
Income> $20K -0.59 -0.50 0.41 0.97 0.14 -0.22 0.10
Live w/in 0.5 mi 0.20 0.30 0.19 0.00 0.12 0.25 0.01
Undergraduate
Graduate Student -0.10 0.16 0.73 -0.17 -0.23 0.01 0.54
Own a Car -0.92 * 0.40 -0.12 -0.18 0.02 -0.32 -1.10
Have access to a car -0.01 -0.02 -0.01 0.02 -0.04 -0.04 -0.07
p < .05; ** p < .01; *** p < .001
APPENDIXB
TIME 1 SURVEY
10# _
Bike Loan Program Entrance Survey
The following is an entrance survey for Bike Loan Program (BLP) participants, and for students who
have expressed interest in participating in the program for the Fall 2009-10 term. Please fill out the
below questions. The data will be reported anonymously and cannot be used to link your identity to
your response. If "other" is chosen, please write in a brief explanation or description.
If you do not feel comfortable answering a question, feel free to skip to the next one.
At the end of the survey, you will be asked to provide your contact information. Again, this will not
compromise anonymity when data is reported.
1) Are you interested in participating in the Bike Loan Program? (If not, please do not continue)
52
A. Yes B. No
2) Please indicate your academic status at the start of the Fall 2009 term.
A. Freshman
B. Sophomore
C. Junior
D. Senior
E. Other _
3) Are you an international student studying at U of 0 for one year or less?
A. Yes
4) How old are you? __
5) What is your sex?
A. Male
B. No
B. Female
6) What state or country were you a resident of before enrolling at U of O?
7) What is your annual income (not including student loans)?
o Under $10,000
o $10,000 - $15,000
o $15,001 - $20,000
o $20,001 - $30,000
o $30,001 - $40,000
DOver $40,000
8) Do you live within walking distance (about 0.5 mi/1 km) from campus?
A. Yes B. No
9) What is the nearest street intersection to your residence?
10) Is this your first school term attending the U of O? (If yes, skip to 12)
A. Yes B. No
53
1) How dId you primarily travel in previous terms while attending U of O?
o Public transit (Bus, EmX)
o Walk
o Drive a car
o Carpool
o Bike
o Other _
2) How often did you use the following to travel to any destination in previous terms while attending
U of 0:
Bike
A. Daily
B. Several times per week
C. Once a week
D. A few times per month
E. A few times per year
F. Never
G. Other _
A few times per year
Never
Other _
Public Transportation
E.
F.
G.
A. Daily
B. Several times per week
C. Once a week
D. A few times per month
Car
A. Daily
B. Several times per week
C. Once a week
D. A few times per month
E. A few times per year
F Never
G. Other _
Carpool
A. Daily
B. Several times per week
C. Once a week
D. A few limes per month
E. A few times per year
F. Never
G. Other _
A. Daily
B. Several times per week
C. Once a week
D. A few times per month
Walking (Not including from a parking spot/bus/bike rack)
E. A few times per year
F. Never
G.Other _
3) How Often did you park on campus in previous school terms?
A. Daily
B. Several times per week
C. Once a week
D. A few times per month
E. A few times per year
F. Never
G. Other _
2
14) Do you currently own a car?
A. Yes B) No
54
15) If you own a car, how often do you expect to park on campus?
A. Daily
B. Several times per week
C. Once a week
D. A few times per month
E. A few times per year
F. Never
G.Other _
16) If you do not own a car, do you have access to a car if necessary?
A. Yes B) No
17) In the event that you were not to have a Bike Loan Program bike, how would you plan to travel
this year? (Please check all that apply)
o Public transit (Bus, EmX)
o Walk
o Drive a car
o Carpool
o Buy a bikeo Other _
18) What problem(s) do you generally encounter with bicycling? (Please Check all that apply)
o Dangerous traffic conditions
o Lack of bike facilities
(e.g., racks, lanes, etc.)
o Bad weather
o Other
3
o Lack of mechanics knowledge
o Poor biking skills or training
o Poor physical health
o Hilly terrain
Circle the number that corresponds best to your attitude, where 1 indicates that you strongly disagree and
5 indicates that you strongly agree. Leave blank if the question does not apply to you.
Strongly Disagree Neutral Strongly Agree
1) I generally enjoy riding bikes 2 3 4 5
2) I am a confident bicyclist and don't mind 2 3 4 5
riding in traffic.
3) I do not understand how to fix or adjust
problems with a bike 1 2 3 4 5
4) I am a generally healthy person. 1 2 3 4 5
5) Having a bike-friendly community (bike
lanes, slow traffic, bike parking) is important to 2 3 4 5
me.
6) Public transportation with low cost and
frequent service is important to me. 1 2 3 4 5
7) Having a car-friendly community (many 2 3 4 5
lanes, few crosswalks, free parking) is
important to me.
Please enter your name and contact information below to be entered into a drawing for a $20 gift
certificate to the Duck Store, and to be eligible for a $5 payment for completion of the exit survey at the
end of the Fall term.
4
55
APPENDIXC
TIME 2 SURVEY
10# _
Bike Loan Program Exit 8U1vey
The following is an entrance survey for Bike Loan Program (BLP) participants, and for students who
have expressed interest in participating in the program for the Fall 2009-10 term. Please fill out the
below questions. The data will be reported anonymously and cannot be used to link your identity to
your response. If 'other" is chosen, please write in a brief explanation or description.
If you do not feel comfortable answering a question, feel free to skip to the next one.
At the end of the survey, you will be asked to provide your contact information. Again, this will not
compromise anonymity when data is reported.
1) Did you receive a bike from the Bike Loan Program during Fall term of 2009?
56
A. Yes B. No
2) Did you complete an entrance survey for this study?
A. Yes B.No
3) Please indicate your academic status at the start of the Fall 2009 term.
A. Freshman
B. Sophomore
C. Junior
D. Senior
E. Other _
4) Are you an international student studying at U of 0 for one year or less?
A. Yes
5) How old are you? __
6) What is your sex?
A. Male
B. No
B. Female
7) What state or country were you a resident of before enrolling at U of O?
8) Do you still live within walking distance (about 0.5 mi/1 km) from campus?
A. Yes B. No
9) What is the nearest street intersection to your residence?
10) How did you primarily travel in during Fall term of 2009 at U of O? (Check all that apply)
o Public transit (Bus, EmX)
o Walk
o Drive a car
o Carpool
o Bike
o Other _
11) How often did you use the following to travel to any destination during Fall term 2009 at U of 0:
Bike
A. Daily E. A few times per year
B. Several times per week F Never
C. Once a week G. Other
D. A few times per month
Public Transportation
A. Daily E. A few times per year
B. Several times per week F. Never
C. Once a week G. Other
D. A few times per month
Car
57
A. Daily
B. Several times per week
C. Once a week
D. A few times per month
Carpool
A. Daily
B. Several times per week
C. Once a week
D. A few times per month
E. A few times per year
F. Never
G. Other _
E. A few times per year
F. Never
G. Other _
A. Daily
B. Several times per week
C. Once a week
D. A few times per month
Walking (Not including from a parking spot/bus/bike rack)
E. A few times per year
F. Never
G. Other _
12) Do you currently own a car?
A. Yes B) No
13) If you own a car, how often did you park on campus during Fall term at U of O?
A. Daily
B. Several times per week
C. Once a week
D. A few times per month
2
E. A few times per year
F. Never
G. Other _
------- ----------
58
14) If you do not own a car, do you have access to a car if necessary?
A. Yes B) No
15) What problem(s) do you generally encounter with bicycling? (Please Check all that apply)
D Dangerous traffic conditions
D Lack of bike facilities
(e.g., racks, lanes, etc.)
D Bad weather
D Other
3
D Lack of mechanics knowledge
D Poor biking sk\lls or training
D Poor physical health
D Hilly terrain
Circle the number that corresponds best to your altitude, where 1 indicates that you strongly disagree and
5 indicates that you strongly agree. Leave blank if the question does not apply to you.
Strongly Disagree Neutral Strongly Agree
1) I generally enjoy riding bikes 2 3 4 5
2) I am a confident bicyclist and don't mind 1 2 3 4 5
riding in traffic.
3) I do not understand how to fix or adjust
problems with a bike. 1 2 3 4 5
4) I am a generally healthy person 2 3 4 5
5) Having a bike-friendly community (bike
lanes, slow traffic, bike parking) is important to 1 2 3 4 5
me.
6) Public transportation with low cost and
frequent service is important to me. 2 3 4 5
7) Having a car-friendly community (many 2 3 4 5
lanes, few crosswalks, free parking) is
important to me.
Please enter your name and contact information below to be eligible for a $5 payment for completion of
the exit survey at the end of the Fall term.
4
59
60
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