1. Will a Bike Ride a Day Keep the Doctor Away?
Evidence from the Calgary Cycle Track Pilot Project
Janessa Linton
University of Calgary
Abstract
Government spending on health care related to chronic disease caused by inactivity and obesity
is growing in young adults (Macdonald, B. 2007). Studies have shown that an active cyclist
commute leads to an increase in physical activity that is health enhancing. Leading to the
question, does bicycle infrastructure improve health? This study decomposes this question into
two segments; Will bicycle infrastructure cause an increase in bicycling? And; What are the
health outcomes associated with increased bicycling? My study uses the City of Calgary Cycle
Track Pilot Project as a laboratory for three reasons: 1) Prior to the pilot project Calgary had no
protected bike lanes; 2) Calgary winter weather conditions are different from the existing
literature; 3) Calgary has an extensive bicycle usage dataset that allows for casual estimation
strategy to be used. Preliminary bicycle level calculations revealed an increase in bicycle trips of
670,113. To generate estimates that capture the casual effect of bicycle infrastructure on
bicycle use in Calgary. First, I compare the weather trends before and after the Calgary pilot
project, then I use a difference-in-difference (DD) estimation strategy. I find that weather was
not an influencing factor on the increase in bicycle use, and the DD produced close to casual
results. The DD estimated the increase in bicycle usage to be 592,119 trips which resulted in
health savings of $11.8M, this was calculated from health care expenditures saved and the
value of a statistical life saved. Expressed in net present value with a 5% discount, the bicycle
infrastructure resulted in $237M in health benefits over an infinite time-horizon.
Econ 695/697, Research Methods

2. 2
1 Introduction
Health researchers have found that chronic disease related to obesity and physical inactivity is
on the rise (Oja et al., 1998). An increase in physical activity of 30 minutes a day is considered
health enhancing, leading to the prevention of related chronic disease (Rutter et al., 2003).
Specifically, studies have shown that the primary health benefits that arise from cyclist
infrastructure is reduced “mortality due to conditions such as cardiovascular disease and cancer
as a result of increased physical activity” (Rutter et al., 2003). Consequently, bicycle
infrastructure is a viable way for commuters to safely increase their physical activity which will
lead to health improvements. Therefore, it is important to investigate if bicycle infrastructure
improves health. To do this, I explore two questions: (1) Will bicycle infrastructure cause an
increase in bicycle use? (2) What are the health benefits associated with increased bicycle use?
Young adults have had a large decrease in their level of physical activity, and cycling
trips have significantly declined as a result of greater car ownership (Macdonald, B. 2007). In an
effort to curb this trend, bicycle lane infrastructure in an urban city center is used by
governments to encourage physical activity into daily routines. Rutter et al. (2003) referenced a
study done in Copenhagen that supports the linkage of increased bicycle use in a population
and health benefits associated with this. The study had a sample population of 30, 640; of
these, 6,954 adults (aged 20-60 years) were regular cycle commuters. The study followed up
with the population over an average of 14.4 years. The mean time of travel was three hours a
week for the cycle commenters, and “their relative risk of death was 0.72 (95% CI 0.57, 0.91),
after adjustment for age, gender, educational status, leisure- time physical activity, BMI, blood
lipid levels, smoking, and blood pressure.” (Rutter et a.l, 2003, p. 90). The results suggested that

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a cyclist is 28% less likely to die from any cause than a non-cyclist, in any given year. As the
above study has shown, as well as similar studies, public funds used by policy makers to
investment in bicycle infrastructure can be offset by a significant decrease in health care costs
and expenditures. They also can result in a reduction in external costs, such as in air pollution
and noise, and personal savings in fuel consumption and parking fees associated with driving a
motorized vehicle. In my study, I will determine the increased bicycle use caused from
Calgary’s new bicycle infrastructure, and I will approximate the health care savings and a
statistical life saved from the infrastructure investment.
In this paper, I use the City of Calgary Cycle Track Pilot Project as a laboratory to
continue the research in the area of urban bicycle infrastructure. The pilot project is a network
of protected bike lanes in the downtown core of Calgary. In April 2014, The City of Calgary
approved the Centre City Pilot Cycle Track Network Pilot Project, which was designed to add
bicycle infrastructure to downtown Calgary streets, where there had previously been no
designated biking lanes. These bicycle lanes were opened in June, 2015, after a public
information campaign advertising the project to the public. The design of the bicycle lanes is
intended to encourage more bicycle trips into and out of the downtown core, and to reduce
conflicts between people who are walking, biking and driving.
The City of Calgary Cycle Track Pilot Project is novel for three reasons: (1) Prior to the
pilot project Calgary had no protected bicycle lane infrastructure; (2) Calgary’s winter weather
conditions permit my results to be more applicable to other Canadian and northern United
States (U.S.) cities; (3) The City of Calgary offers a unique dataset that allows my research to
formulate causal results that present a more accurate value for the increased bicycling achieved

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from the new bicycle infrastructure. Thus, my study offers additional insights and realistic
results for cities that currently have no or little bicycle infrastructure in a similar climate.
The existing literature that evaluates bicycle lane infrastructure falls into three
categories. The first area of study links the increase in commute cycling with an increase in
health benefits. This literature aims to answer three main questions: (1) “Is there a desire to
increase cycling in society?”; (2) “Will cycling result in health benefits?”; and (3) “How can
cycling be encouraged?” A second area of literature developed tools that can be used in bicycle
lane infrastructure evaluation. The goal of this literature is to streamline tools that make future
work and policy analysis in bicycle infrastructure easier and more effective. The third and
largest area of literature used existing bicycle infrastructure information from cities in Europe,
the United Kingdom (UK) and the U.S. to establish the current and future benefits of installing
bicycling infrastructure. However, I find that the literature in the third area contains limited
data sources and focuses on speculating about future bicycle use based on hypothetical
infrastructure increases. The researchers in the third area also used different techniques to
establish a method that is based on their own literature review, infrastructure design, and
required research outcomes, thus making comparisons between different bicycle infrastructure
My paper addresses the question, what are the health benefits associated with public
investment in new urban bicycle path infrastructure? As discussed above, the City of Calgary
Pilot Project is novel in the area of study surrounding urban bicycle infrastructure. To
determine the increased bicycle trips, the City of Calgary installed data collection meters to
track daily bicycle trips on the newly built bicycle lanes. I use the City of Calgary data to
determine the increased bicycle usage from the bicycle lane pilot project. To establish more

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rigorous results, I approach this as a casual question, “Has the bicycle infrastructure caused the
increase in bicycle trips in Calgary?” To make a causal link between the bicycle infrastructure
and increased bicycle usage, I employ a difference-in-difference (DD) estimation strategy.
Intuitively the DD estimation is, determining the difference between intersections on and off
the bicycle infrastructure both before and after the infrastructure is installed. Once you know
the differences both before and after, I find the difference between them, resulting in the value
of increased bicycle use after the installation of bicycle infrastructure caused only by the
intersections on the bicycle infrastructure roadways. By determining that Calgary’s new bicycle
infrastructure is responsible for the increase in bicycle use in the downtown core, I then
approximate the health care savings and a statistical life saved from the infrastructure
investment. I determine the value of a statistical life saved and any potential savings in health
care expenditures by using well-established calculations and tools from the literature.
To preview my main results, I find over the first year of the Calgary Cycle Track Pilot
Project being open it saw an annual increase in bicycle use of 592, 199 trips. This resulted in an
annual health care expenditure savings of $441,250.32. At the increased level of bicycling the
Health Economic Assessment Tool calculates that 2 deaths are prevented, equating to an
annual valued of $11,408,000. In aggregate, the net present value of the total health benefits
over an infinite time horizon are $236,985,006 from the increase in bicycle usage caused by the
new bicycle infrastructure in Calgary.
My work fits within this growing literature that evaluates government investment in
bicycle lane infrastructure, my paper makes three main contributions to the literature. First,
typical work in this area estimates how much bicycling has increased or approximates future

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increases of bicycle use from a proposed bicycle infrastructure project. My work is unique in
that I am able to make use of a rich dataset from the City of Calgary, which contains daily
bicycle trip counts and historic counts of bicycle trips into and out of the downtown core. Thus,
I am able to provide a more accurate and casual relationship between bicycle infrastructure and
an increase in bicycling. Secondly, previous studies only explored expanding current bicycle
infrastructure. However, my work with the Calgary data will provide results related to new
bicycle infrastructure. My work will also offer results that are specific for cities that currently
do not have bicycle infrastructure and are looking to install such infrastructure. Finally, my
work is novel because Calgary’s weather conditions are unique1
and have the potential to have
a significant effect on the results, in contrast to previous studies that have used cities that have
a bicycle friendly climate.
2 Literature Review
This section details three main categories of literature in the area of bicycle infrastructure
identification and evaluations, that were outlined in the introduction.
2.1 Health Benefits from Bicycling
Oja et al.’s (1998) paper is relevant because it provided the evidence and research that
validated the need for future work in the literature on the health benefits of having bicycling
infrastructure in place. Oja et al. (1998) ran three different studies to examine the utility
achieved by commuting to work by walking or cycling. The first study was a questionnaire
1
Throughout the winter Calgary can have severe cold snaps, although rarely lasting more than a week. The
weather can change very quickly, from day to day and even hour to hour. Winter can be long and autumn can
short, as Calgary can see frost and snow falls starting in mid-September (Calgary Weather & Climate | Visit
Calgary. 2016).

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survey used to evaluate the study populations’ desire to walk or cycle to work if cycling
infrastructure was in place, or if they would continue to use current modes of transportation.
The safety of pedestrian and cyclists was found to be a major issue that prevents people from
an active commute. This is categorized as a critical impediment for changing one’s behaviour to
include an active commute as a form of health-enhancing physical activity (HEPA) into his or her
routine. The main focus of Oja et al.’s (1998) study was a randomized trial that delivered the
following results:
Regular walking and cycling for a one-way average of about 30 minutes during
commuting to work with self-paced speed improved aerobic fitness, decreased
cardiovascular strain in submaximal standard work, increased the use of fats as
an energy source in physical activity, and caused slight favorable changes in
blood HDL cholesterol in healthy young to middle-aged adults of low to
moderate physical fitness. (Oja et al, 1998)
Overall the results showed that physical activity from active commuting is considered to
be health enhancing. The final survey study found that workplace promotion of an active
commute would be widely accepted by employees, but safety of these activities was a
prerequisite. My paper will specially address this concern as the City of Calgary project has
installed protected bike lanes. Oja et al. (1998) found that walking and cycling to work are the
easiest routine-based ways for the community and general population to increase their physical
activity. Although this paper does not determine the specific benefits that are achieved by an
increase in bicycling, it provided the underpinning of future work and the validity of the results.

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2.2 Health Economic Assessment Tool (HEAT)
Rutter et al. (2003) evaluated how increased physical activity from cycling can change health
outcomes in society and found similar results to Oja et al (1998): not only does it help in
physical and mental health wellness but it also reduces the risks of chronic health diseases. In
this literature, Rutter et al. (2003) identified the need for effective interventions to promote an
increase in active commuting. The type of policies needed to intervene require economic
evolution and a sophisticated way to evaluate the benefits realized from new bicycling
infrastructure. This paper developed a tool called the Health Economic Assessment Tool (HEAT)
for walking and cycling. The tool models the economic value of reduced mortality from cycling.
Although Rutter et al. (2003) developed the tool to help create more complete economic cases
used to justify spending on infrastructure that would increase community physical activity, they
do not use the tool in their paper. My work uses the tool and identification strategy described
by Rutter et al. (2003). This tool is important as it allows for bicycle infrastructure studies to
use a consistent approach to obtaining results. My paper is also able to use a unique dataset
and infrastructure model to complement this literature.
2.3 Bicycle Infrastructure Analysis
The two paper in the literature that are most closely related to my paper are, Saelensminde
(2004) and Gotschi (2011). Both papers presented studies that evaluated the benefits of adding
additional bicycle lanes to existing bicycling infrastructure. Saelensminde (2004) studied three
cities in Norway, and the goal of the research was to influence government spending on bicycle
infrastructure to promote an easier and safer way for people to choose to bicycle as a means of
transportation. In each of the three cities, the benefits substantially outweighed the costs. In

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particular, it was seen that the benefit from reduced costs of less severe and severe diseases
and ailments are substantial. The results showed a benefit-cost ratio of magnitudes greater
than one, for each individual city.
Gotschi (2011) is the first study of its kind in the U.S. This study evaluated three
different future bicycle infrastructure scenarios potentially being added to Portland, Oregon’s
current bicycle infrastructure. This analysis was conducted using data on bicycle counts in
Oregon and utilized a discrete choice model that converted trips into miles. The benefits this
analysis considers are the health care savings from reduced mortality and the saved value of
statistical lives. The results from the three difference investment plans all have a ratio greater
than one. Gotschi (2011) found that as the investment plans’ costs increased significantly
across the three plans, the bicycle miles and annual trips do not. My work will build on this
area of literature and determine causal link between health benefits and bicycle infrastructure.
3 Data
This section provides further details about the study location—the Calgary Cycle Track Pilot
Project. This is followed by a preliminary description of the data and the data limitations.
3.1 Description of Study Area
This study utilizes the unique cyclist-count database the City of Calgary has been compiling both
before and after the Cycle Track Pilot Project implementation. The database consists of data
collected by Eco-Counter machines. These are automated bicyclist counting machines placed
on the cycle track, as well as on Stephen Avenue and the Peace Bridge. The database also
includes annual data from the City of Calgary’s cordon traffic counts that are conducted in May,
bicycle counts, and centre city bicycle volume maps. This extensive database makes Calgary a

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good laboratory to study the health outcomes generated by the implementation of new bicycle
infrastructure. Similar to the majority of Canada’s provinces and Northeastern U.S. states,
Calgary experiences cold wintery months and a short summer season. Unlike current literature
that evaluates the outcomes of urban bicycle infrastructure, my study on Calgary will provide
results specific to Calgary’s unique weather conditions and the cycle track pilot project that has
introduced Calgary to its first set of protected bicycle lanes in the downtown area.
Approximately a year ago, Calgary implemented its first set of physically separated
bicycle lanes in the downtown area. Previous to this, Calgary had installed many kilometers of
multiuse pathways around the city and has had unprotected designated bike routes in the
downtown, making the Cycle Track Pilot Project the first of its kind in Calgary. The cycle track
includes bicycle lanes on 5th,
St. from 3rd
Ave. S.W. to 17th
S.W., on 12th
Ave., from 11th
St. S.W.
to 4th
St. S.E., and on 8th
/9th
Ave., from 11th
St. S.W. to 3rd
St. S.W. and Macleod Trails to 4th
St.
S.E. Figure 1 below shows the new cycle track route and 10 Eco-Counter machines installed to
collect daily bicycle trip data. The pilot project contains over 5 km of protect bike lanes, with
the 5th
Street section being 1.4 km and the 12th
Ave. section being 2.5 km in length.

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3.2 Data
To gain an understanding of previous bike trends in Calgary before the pilot project, I review the
census data and bicycle volume maps. I use the 2014 Civic Census to collect data to formulate a
preliminary understanding of bicycle use and patterns within Calgary and the different wards.
In addition to this, I have access to bicycle volume maps that started in 2012 until 2015. The
maps provide in depth details of the number of bicyclists at multiple corridors throughout the
city centre. I extract the data points on the maps from 2013 to 2015, I use this data to
understand the bicycle use trends in Calgary both before and after the Calgary Cycle Track Pilot
Project started. I then construct a panel dataset to be used in the difference-in-difference
estimation, with the 2013 to 2015 volume map data points.
The automated bicycle trip counters provide a reliable method for me to collect and
analyze Calgary’s bicycle infrastructure usage into and out of downtown during weekdays since
the pilot project opening in June 2015. The data is publically available daily, and Eco-Counter
says that the counters are 97% accurate (The City of Calgary - Bike Data, 2016). The analysis of
the bike data will incorporate data from June 2015 until July 2016. Unlike other studies, I am
able to include winter months because the City of Calgary maintains the bicycle infrastructure
year round. Both the census and volume map data combined with the recent trip counter data
are used to determine the increase in bicycle trips achieved from the new infrastructure.
In addition to the City of Calgary bicycle data, I collect and analysis Calgary’s historic
weather data provided by Environment and Climate Change Canada. I collect quarterly data for
Calgary both four quarters before and after the pilot project opening. The metrics I collect are
rainfall (mm), snowfall (cm), precipitation (mm), mean temperature (C), snow on ground (cm),

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number of rain days, number of snow days, number of precipitation days and number of wind-
chill days.
3.3 Data Description
Figure 2 below, demonstrates that bicycling as a mode of transportation to work in 2014 is
relatively small. Bicycling accounts for 1.39% of city wide mode of transportation to work in
2014, this is up slightly from 0.87% in 2011 (2014 Civic Census Results. 2014). Consequently,
the natural increase from 2011 to 2014 is insignificant for bicycle transportation to work.
Figure 2 also shows that people who drove alone to work as transportation mode holds the
highest share, at 67.39% in 2014, which is down from 69.58% in 2011(2014 Civic Census Results.
2014). Accordingly, there is substantial opportunity for growth within the category of bicycling
to work in Calgary.

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Table 1 represents the top five wards in Calgary where people use a bicycle as the mode
of transportation to work. (See appendix A for a detailed map of ward position in Calgary). The
top five wards out of 14 total wards represents 78% of the total bicycle commuters. These
areas are located close to the downtown core and are likely to be impacted the most by the
Calgary Cycle Track Pilot Project.
Figure 3 below, exemplifies the significant increase in average daily trips from 2014 to
2015. This information combined with Figure 2 and Table 1 lead me to conclude that there was
an increase in bicycle use from 2014 and 2015 because of the new bicycle infrastructure.

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Table 2 provides a few summary statistics of the data from six of the counters along the
new bicycle infrastructure paths.

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4 Methodology
After reviewing several studies including, Saelensminde (2004), Macdonald (2007), and Gotschi
(2011), I find that health care savings and saved value of statistical lives are most significant
outcomes achieved by bicycling infrastructure. To calculate the value of health care savings and
saved value of statistical lives I will use both estimates developed in previous studies and the
bicycle level estimates I constructed empirically. I first build preliminary bicycle level estimates
using the data described in the above section. Leading to the empirical question to be
answered: Did the bicycle lane infrastructure actually cause the increase in bicycling? The
papers main objective is to understanding the causal relationship between the bicycle lane
infrastructure and bicycle trips, then determine the health outcomes of this increase in bicycle
use. The two caveats in determining the actual increase in bicycle use from the bicycle
infrastructure are: weather influences and bicycle traffic being re-routed to the new bicycle
lanes. Steps are illustrated in this section.
4.1 Estimation Strategy
To establish the preliminary levels of the increase in bike usage, I first use the bicycle volume
maps from 2012 to 2015, and collect data points from six locations on the map that match with
the current counter location along the new bicycle infrastructure. The six points are
predominantly end points of the routes, they provide an understanding of the volume coming into
or leaving the downtown core on the new bicycle infrastructure. I then determine the percentage
increase of bike trips from 2014 to 2015. I find that 2012 to 2014 showed little to no growth,
therefore I take the percentage increase from 2014 to 2015 to be exclusively because of the new
bicycle infrastructure. I then use this percentage increase, combined with the actually bike
volume from July 1st
2015 until June 30th
2016 (one full year post bicycle infrastructure opening)

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to determine the actually increase over this time period. The final step is to aggregate the
increased bicycle use from the six counter locations to get a final value of increased bicycle use.
The next step is to determine if weather had an influence on the possible bicycle usage
increase. To verify this, I compare four quarters both before and after the Cycle Track Pilot
Project opened, as of July 1st
2015. I identify eight metrics: rainfall (mm), snowfall (cm), mean
temperature (C), precipitation (mm), snow on ground (cm), number of precipitations days,
number of rain days and number of snow days.
Finally, I use a difference-in-difference (DD) estimation strategy to determine if there is
an increase in bicycle use along the new bicycle infrastructure, if so, is it caused from the
bicycle infrastructure itself? I estimate a series of regressions to evaluate the difference
between bicycle trips on roads with bicycle infrastructure and roads with non-bicycle
infrastructure formally across time. The regressions are based on model of the general form,
BicycleTripsit = b0 + b1Dummy_NewBikeRoutei + b2Dummy_2015t + bDDInteraction2015t_NewBikeRoutei + eit (1)
where i indexes count location on volume map; t indexes years (2013, 2014 or 2015);
BicycleTripsit is dependent variable included in the panel dataset that I extracted from the
bicycle volume maps; Dummy_NewBikeRoutei is a dummy variable that takes the value of 1 if
bicycle count intersection is on the new bicycle infrastructure route and 0 otherwise;
Dummy_2015t is a dummy variable that takes the value of 1 in post bicycle infrastructure time
periods (2015) and 0 otherwise(2013 and 2014); Interaction2015t_NewBikeRoutei is the
interaction term, generated by multiplying the two dummies; and eit is the error term.

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Intuitively the DD estimation is, determining the difference between intersections on
and off the bicycle infrastructure both before and after the infrastructure is installed. Once you
know the two differences both before and after, I find the difference between them, resulting
in the value of increased bicycle use after the installation of bicycle infrastructure caused only
by the intersections on the bicycle infrastructure roadways. All intersections that are not
included on the bicycle infrastructure roadways act as the counterfactual to the treated points
after the pilot project was initiated. The DD counterfactual comes from the strong common
trends assumption that I make to imply the DD estimation strategy. This presumes that, absent
of bicycle infrastructure difference, both the bicycle infrastructure and non-bicycle
infrastructure intersections should follow the same trend.
For my regressions I employ both log-linear and non-log-linear specification to the Trips
variable, this enables me to find the best fit for the data. I estimate each regression with only a
simple standard error formula. The coefficient on the interaction term is the DD casual effect,
it captures the effect on new bicycle infrastructure on bicycle trips. I use this percentage
increase, combined with the actually bike volume from July 1st
2015 until June 30th
2016 (one
full year post bicycle infrastructure opening) to determine the bicycle trip increase over this
time period. Similar to the aggregation in the preliminary results, I aggregate the increased
bicycle use from the six counter locations to get a final value for the increase bicycle use.
4.2 Health Care Savings
To calculate the health care savings, I will follow Gotschi (2011). First, Gotschi (2011)
formulates, an average estimated health care costs per inactive person. Two of the estimates
take the total estimated health care costs attributed to inactivity in the US, divide by total

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population, divide by 0.75 to adjust for adult population, and divide by 0.48 to adjust for
proportion of inactive people. The adjustment of 48% is for the prevalence of inactivity and
allows for the results to be more conservative. The average cost estimate is, $544 per inactive
person per year, inflated to 2008 dollars (Gotschi, 2011). For the second step Gotschi (2011)
assumes that the difference between an inactive and active person in 30 minutes of physical
activity per day. Therefore, it is assumed that 30 minutes of bicycling will give an annual credit
of $544.
In the final steps, I make the assumption that each trip is 15 minutes in length, based on
the bicycle track length and the 2014 Civic Census Results.
Annual health care savings = [daily 30-minute segments] * $544[annual credit for daily segment] (2)
4.3 Saved value of statistical lives
I will use the Health Economic Assessment Tool2
(HEAT) to determine the value of reduced
mortality due to bicycling. As discussed in the literature review section 2.2, HEAT was
developed to support bicycle infrastructure and policy discussions, making it the appropriate
tool for my study. The inputs it requires are, annual bicycle trips and the average distance
traveled. As stated previously, I assume that each trip is 15 minutes in length. The HEAT
estimates the amount of saved lives and the value associated with each saved life according to
the inputs. Therefore, this tool generates results for the value of a statistical life saved exclusive
to my study.
4.4 Validity of results
2
http://www.heatwalkingcycling.org/index.php?pg=cycling&act=introduction

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One important caveat to mention is that other factors are involved that are contributing
to the increase in bicycle usage other than the bicycle infrastructure. To ease these concerns, I
use the DD estimation strategy to construct bicycle level estimates that answer more accurately
if the bicycle lane infrastructure actually caused an increase in bicycling, unlike other studies
that guess the percentage increase that will be achieved from bicycle infrastructure projects.
The validity of the DD approach is governed by the strong common trends assumption
that is made. Signifying that, the bicycle trips trend in non-bicycle infrastructure intersections is
similar to the bicycle trips trend in bicycle infrastructure intersections before the bicycle
infrastructure installation. If this holds then non-bicycle infrastructure intersections provide a
valid counterfactual for intersections on bicycle infrastructure roads post bicycle infrastructure
installation. This assumption is debatable if trajectories prior to the new bicycle infrastructure
were different. The trajectory can be verified graphically or with a regression based test using
pre-treatment periods. My data only has two pre-treatment periods, therefore I am unable to
verify the common trends of the treated and control bicycle paths in the pre-treatment period.
I can make a realistic assumption that there is no reason to believe that the control and treated
pathways were on different trajectories prior to the new bicycle infrastructure implementation.
Nevertheless, the common trends assumption cannot be verified by the data, indicating the
results may be biased.
The estimates calculated in this study for health care savings and saved value of
statistical lives are very conservative. To achieve the conservative results, I did a critical review
of three studies that calculated estimates for similar health outcomes. The health care savings
value is very conservative and is constructed by averaging three different high quality studies.

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The HEAT is customizable to individual case studies, and has been developed as an accurate and
streamline interface. Overall, the careful consideration of the methodology design leads to the
generation conservative and practical results.
5 Results and Discussion
To answer, what are the health benefits associated with public investment in new urban bicycle
infrastructure. First, I determine the increased amount of bicycling in Calgary, post bicycle lane
infrastructure installation. This is a causal question that needs to be identified. Did the bicycle
lane infrastructure cause the increase in bicycle use on the pilot project laneways? Second,
once the levels of bicycling are known I determine the health care savings and the value of
statistical lives saved. Results are presented in the following section.
5.1 Bicycle Levels
Preliminary Results
Table 3 below, shows there is an overall increase in bicycling levels from 2014 to 2015, these
values are based September and October average volume data from the City of Calgary bicycle
volume maps. At six of the counter locations along the new bicycle paths there has was
approximately 1320 trips a day in 2012, 1840 in 2013 and in 2014 a small decrease down to
1780. Based on these values I predicted that without the bicycle lane infrastructure the 2015
value would have had no growth from 2014. Therefore, in table 3 I was able to show that the
six counter location along the new bicycle paths all have over a 200% increase in bicycle trip
traffic, except counter 1.

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Table 3 presents the data collected from six of the eco-counter on the new bicycle
paths, and shows an increase of 670,113 trips above what would been expected along those
routes without the new infrastructure. Table 3 provides a clear understanding of the increase
bicycle levels over a one-year period after the bicycle infrastructure was installed in Calgary.
Although I find there is an increase in bicycle usage along the new bicycle infrastructure, table 3
does not provide a causal link between the increased bicycle usage and the new bicycle
infrastructure.
Weather Influence
Figure 3 shows that overall weather from quarter three and four in 2014 and quarter one and
two in 2015 do not vary significantly from the four preceding quarters 2015 and 2016. The
weather data from before and after the pilot project bicycle path opening shows minimal

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variation. Therefore, I can conclude that weather effects had minimal influence on the
increased bicycle trips along the new paths in downtown Calgary, as seen in table 3.

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Difference-in-Difference Estimation
Table 4 represents the results generated by running two separate DD regressions, (1) uses non-
log-linear specification of trips and (2) uses a log-linear specification of trips. Regression (1) DD
interaction coefficient is significant at 0.1% and the new bicycle infrastructure has a positive
influence on trips. The DD interaction coefficient provides an average increase of 455 trips on
the count location found along the new bicycle infrastructure roads. Regression (2) reveals that
the DD interaction coefficient is significant at 0.1% and the new bicycle infrastructure has a
positive influence on trips. The DD interaction coefficient in regression (2) implies that there is
a 219% increase in bicycle trips on the average count location found along the new bicycle
infrastructure. I find that the total increased trips are, 592 119 (see Appendix B for full
calculation), this is comparable to 670, 113, which I calculated as the increased trips in the
preliminary analysis.

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Both DD interaction coefficients provide evidence that the increase in bicycle use along
the new bicycle infrastructure is likely caused by the new bicycle infrastructure. Although the
DD estimation strategy allows me to make a close to causal link, there is still question of the
validity. My two main concerns are: (1) The robustness of the results as the strong common
trend assumption made with the DD estimation is unverifiable with my data. The common
trend assumption intuitively means that in the absence of the bicycle infrastructure the non-
bicycle infrastructure intersections follow the same trend as the bicycle infrastructure
intersections. Therefore, normally the DD common trend is verified before the installation of
the bicycle infrastructure and if it holds, the DD uses the non-bicycle infrastructure
intersections as a reference point for what path the bicycle infrastructure intersections would
have followed if the bicycle infrastructure wasn’t installed. As a consequence of not being able
to verify the common trend assumption my results may be biased in two ways. (1) The results
may be upward biased, if for example, the City of Calgary choose 12th
Avenue, 5th
Street, 8th
Avenue and 9th
Avenue as bicycle infrastructure roads because they were experiencing a larger
increase in bicycle usage than other roads. Conversely, if the City of Calgary choose 12th
Avenue, 5th
Street, 8th
Avenue and 9th
Avenue as bicycle infrastructure roads because they were
experiencing a smaller increase in bicycle usage than other roads. (2) There potentially could
have been other policies during the time frame of my study that influenced the increase in
bicycle use. For example, if work places set up incentive to encourage employees to bicycle to
work, this would cause the results to be overestimated.

25. 25
5.2 Health Valuation Results
Two health outcomes associated with public investment in new bicycle infrastructure are health
care savings and value of a statistical life saved. Valuing these two health outcomes is the final
step of this paper, the results are presented in table 5. It quantitatively provides meaning to
the increase in bicycle usage from the new bicycle infrastructure in Calgary. See appendix C and
D for the detailed calculation of saved health care expenditures and input data used in the
HEAT, respectively. I will discussion the results under the DD estimation, as they provide the
most accurate results of the increase in bicycle use.
Over the first year of the Calgary Cycle Track Pilot Project being open it saw an annual
increase in bicycle use of 592, 199 trips. This results in annual health care expenditure savings
of $441,250.32, meaning the increased bicycle trips saved tax-payers from putting $441,250.32
additional funds into health care. At the new current increased level of bicycling the HEAT
calculates that 2 deaths are prevented, equating to an annual valued of $11,408,000.

26. 26
Overall, the net present value of the total health benefits over an infinite time horizon are
$236,985,006.
5.3 Discussion
In summary, over the first year, Calgary has seen a health savings of $11.8M, by providing
approximately 5.4kms of new protect bicycle infrastructure, with an investment cost of 5.75M
(The City of Calgary - Cycle Track Network. 2016). I calculated the net present value of the total
health benefits to be $236,985,006. This net present value represents the value of the
infrastructure in future years in present dollars. I used a discount factor of 5% for the
calculation, this discount aligns with Saelensminde, K. (2004), Saelensminde used a 5% discount
but over a 25-year lifetime of the project. Defining the net present value is important to
recognize the future value of the bicycle infrastructure project.
Most importantly, I was able to determine a close to causal relationship between the
new bicycle infrastructure in Calgary and the increase in bicycle usage. I also verified that
weather pre and post bicycle infrastructure was not a contributing factor to the increase in
bicycle use. The DD found a 219% increase in bicycle usage along the bike paths that contained
the new bicycle infrastructure. Finally, I was able to use this determination of the casual
relationship to link the new bicycle infrastructure with health outcomes and derive a monetary
value between the outcomes and the new bicycle infrastructure in Calgary. Using the DD
estimation strategy is the important step to produce results that are particularly influential for
future bicycle infrastructure projects both in Calgary and other cities under similar conditions.

27. 27
6 Conclusion
To conclude, my research reveals that The City of Calgary Cycle Track Pilot Project resulted in
better health through the increase in bicycle usage. It generated a net present value of $237M
from two health benefits, health care expenditure savings and the value of a statistical life
saved. This study is able to provide close to causal results that tells us that the bicycle
infrastructure that Calgary installed caused an increase in bicycle use. I hope this evidence from
Calgary motivates new policies and funding for bicycle infrastructure in Calgary and other cities
that are eager to reduce obesity and inactivity, by encouraging an active commute.

28. 28
Appendices
Appendix A. Ward Map from Calgary Census

29. 29
Appendix B. Difference-in-Difference Aggregation Results
Notes. Detailed table of increased trips per counter location and aggregation of trips.
Comparing both preliminary results and DD estimation results.
Appendix C. Calculations for Annual Health Care Savings
Annual health care savings = [daily 30-minute segments] *$544[annual credit for daily segment] (1)
Preliminary Results
Step 1: Annual Increase Trips: 670116 (15 minute segments)
Step 2: 30 minute segments, annually:670116/2 = 335058 segments/year
Step 3: number of 30 minute segments daily: [(335058segments/year)/365day/year)] = 917.97
Step 4: each daily segment gets a $544 credit annually=$499,375.68
Difference-in-Difference Estimation Results
Step 1: Health enhancing increased trips: 592119
Step 2: 30 minute segments, annually: 592119/2 = 296056 segments/year
Step 3: number of 30 minute segments daily: [(296056segments/year)/365day/year)] = 811.11
Step 4: each daily segment gets a $544 credit annually= $441,243.84
Route Counter
location
Annual
Trip
(July 1st
2015-
June 30th 2016)
Estimated
Increased
Trips
(Preliminary
Results)
Estimated
Increased
Trips
(DD
Estimation
Results)
8th
Avenue #1, West of
8th
Avenue
69,577 0 37807
Stephen
Avenue
#3, East of
1st
Street
180,637 140135 98154
12th
Avenue #5, West of
8th
Street W
198,827 144354 108038
#7, West of
3rd
Street E
69,004 47028 37495
5th
Street #8, North of
5th
Avenue
217,946 127512 118427
#9, CPR
underpass
353,709 211084 192198
Aggregate
trips:
670113 592119

30. 30
Appendix D. HEAT Input Data
Preliminary Results
Input data:
Cycling from a single point in time.
Total number of trips observed: 670113/365 = 1836, 100% are cycle trips
90% are return trips
average distance of 4km (15 minute trips, speed of 16km/hr.)
Evaluate the benefits of this current increased cycling
Average population: (about 20-64 Years)
Crude death rate Canada per 100000 = 739 (world bank)
5,800,000 – value of a statistical life (ignoring currency)
Difference-in-Difference Estimation Results
Input data:
Cycling from a single point in time.
Total number of trips observed: 592119/365 = 1622, 100% are cycle trips
90% are return trips
average distance of 4km (15 minute trips, speed of 16km/hr.)
Evaluate the benefits of this current increased cycling
Average population: (about 20-64 Years)
Crude death rate Canada per 100000 = 739 (world bank)
5,800,000 – value of a statistical life (ignoring currency)

31. 31
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