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The cost-effectiveness of bike lanes
in New York City
Jing Gu, Babak Mohit, Peter Alexander Muennig
Mailman School of Public
Health, Columbia University,
New York, New York, USA
Correspondence to
Dr Babak Mohit, Mailman
School of Public Health,
Columbia University,
722 W 168th St. Rm 480,
New York, NY 10032, USA;
bm2762@cumc.columbia.edu
Received 5 April 2016
Revised 19 July 2016
Accepted 10 August 2016
To cite: Gu J, Mohit B,
Muennig PA. Inj Prev
Published Online First:
[please include Day Month
Year] doi:10.1136/
injuryprev-2016-042057
ABSTRACT
Background Our objective is to evaluate the cost-
effectiveness of investments in bike lanes using New York
City’s (NYC) fiscal year 2015 investment as a case study.
We also provide a generalizable model, so that localities
can estimate their return on bike lane investments.
Methods and findings We evaluate the cost-
effectiveness of bike lane construction using a two-stage
model. Our regression analysis, to estimate the marginal
addition of lane miles on the expansion in bike ridership,
reveals that the 45.5 miles of bike lanes NYC constructed
in 2015 at a cost of $8 109 511.47 may increase the
probability of riding bikes by 9.32%. In the second stage,
we constructed a Markov model to estimate the
cost-effectiveness of bike lane construction. This model
compares the status quo with the 2015 investment.
We consider the reduced risk of injury and increased
probability of ridership, costs associated with bike lane
implementation and maintenance, and effectiveness due
to physical activity and reduced pollution. We use Monte
Carlo simulation and one-way sensitivity analysis to test
the reliability of the base-case result. This model reveals
that over the lifetime of all people in NYC, bike lane
construction produces additional costs of $2.79 and gain
of 0.0022 quality-adjusted life years (QALYs) per person.
This results in an incremental cost-effectiveness ratio of
$1297/QALY gained (95% CI −$544/QALY gained to
$5038/QALY gained).
Conclusions We conclude that investments in bicycle
lanes come with an exceptionally good value because they
simultaneously address multiple public health problems.
Investments in bike lanes are more cost-effective than the
majority of preventive approaches used today.
OBJECTIVE
The USA has 67 million bicyclists, making over
300 million trips per year in big cities alone,
1
with
almost 700 deaths and 48 000 serious injuries per
year.
2
This high level of casualties makes the USA
the most dangerous place among wealthy nations
to bicycle. Per kilometre and per trip cycled, US
bicyclists are twice as likely to be killed as German
cyclists and over three times as likely as Dutch
cyclists.
3
One effective and intuitive way of pre-
venting injury is to introduce bike lanes on all
major cycling routes, an infrastructure intervention
that reduces all forms of injury by 25%.
4
Unlike
helmet laws, bike lanes do not require behavioural
change on the part of the cyclist, and they come
with other benefits. For example, they ‘normalise’
exercise behaviours, reduce pollution and may help
address the obesity epidemic in the USA. In places
where bike lanes have been installed, cycling (and
possibly other forms of exercise) tends to increase
greatly. This creates a virtuous cycle, as the more
people who bike, the safer it becomes to cycle.
5
Well-designed bike lanes improve safety for
people on bikes and reduce excessive speeding in
cars, organise trafficflow and protect pedestrians.
Bike lanes take three forms. ‘Class I’bike lanes
provide a route that is physically separated from
moving traffic. ‘Class II’bike lanes have a marked
lane on the road. Finally, ‘Class III’bike lanes
merely provide shared road markings for drivers
and add safety legislation. Each of these comes
with a different set of risks and benefits. For
example, Class II bike lanes often force cyclists to
ride next to parked cars and place the cyclist at risk
of colliding with suddenly opened car doors.
Nevertheless, much of the academic literature on
bike lanes treats all forms of bike lanes equally.
Our objective is to evaluate the more generic
notion of a ‘bike lane’investment using New York
City’s (NYC) 2015 investment as a case study. We
use NYC because it provides a known investment
in bicycle infrastructure. We use predicted 2015
values (rather than actual values) so that
year-to-year variations in weather, road conditions
or other factors are smoothed across many years.
While we use NYC as an example, our intent is to
provide a much more generalisable model, such
that localities can estimate the return on their
investment in bike paths. We undertake this study
because, while investments in bike lanes appear to
broadly benefit health, it is not clear that they bring
more value than other types of health investments
that a city or locality might make, such as expan-
sion of healthcare services.
DESIGN
We evaluated the cost-effectiveness of the construc-
tion of bike lanes in NYC in 2015 as a case study. We
first used regression analysis to estimate miles of bike
lanes constructed in 2015 and to model the effect of
additional lane miles on the expansion in bike rider-
ship. Using data through 2014, we use ordinary least
squares (OLS) to predict values for 2015 so that ana-
lyses are not influenced by temporary change in the
weather or road conditions that might influence bike
path construction, ridership or injury. We then con-
structed a Markov model using TreeAge Software
(TreeAge Software. TreeAge Pro 2015 (R 1.0).
Williamstown, Massachusetts: TreeAge Software;
available at https://www.treeage.com, 2015) to esti-
mate the costs and effectiveness of bike lane construc-
tion in 2015 for people starting to ride bikes at the
age of 36 (the average age of people riding within
NYC) over the next 34 years. While we use NYC as
an example, we also rely on broader sources of data
so that the model outputs are more generalisable.
Gu J, et al.Inj Prev 2016;0:1–5. doi:10.1136/injuryprev-2016-042057 1
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Participants
The Markov model (displayed in figure 1) targets the entire
cohort of 8.5 million people who live in NYC in 2015,
6
includ-
ing bike riders (both existing and new riders because of added
bike lanes) and non-riders. The model has two major arms,
namely the status quo arm, which simulates the situation
without bike lane construction in 2015, and the bike lane arm,
which simulates the situation after the construction of bike lanes
in 2015. The latter arm is depicted in figure 1, and given the
structural similarity of the two arms, the status quo arm has
been collapsed. Both arms include the whole NYC population.
Under both the status quo arm and the bike lane arm, a portion
of NYC population chooses biking as their main way of trans-
portation, while the others do not. Subsequently, both bike
riders and non-riders have a probability of injury that is differ-
ent from one another.
We estimate that with a population of 8.5 million people and
a ridership probability of 0.013,
7
the number of bike riders in
the status quo arm is 110 500 persons. With a 9.32% increase,
8
we estimate that there will be 120 445 riders in the intervention
arm.
Regression analysis
We obtained the number of bike lane miles constructed from
2007 to 2014 from the NYC Department of Transportation
(NYC DOT).
9
Using these data, we estimated that the NYC
DOT constructed 45.5 miles of bike lanes in 2015 at a cost of
$8 109 511. NYC DOT also tracks trends in NYC cycling using
the In-Season Cycling Indicator, which is derived from counts
of bicycle traffic at several locations.
8
We ran a simple linear regression model with the percentage
of increase in ridership as the independent variable and the add-
itional bike lane miles constructed every year as the dependent
variable from 2007 to 2014. The percentage of increase in
ridership was calculated using the in-season cycling indicator of
each year. The estimated model is Y=0.004X−0.0888, with R
2
equals to 0.59, which indicates that the model is able to explain
the variance of the dependent variable quite well.
This OLS analysis revealed that the percentage of expansion
in bike ridership would increase by 0.4% for every additional
one mile of bike lane construction. Using the fitted equation, we
calculated that the probability of riding bikes would increase by
9.32% if 45.5 lane miles were constructed in 2015.
Figure 1 Markov model of bike lane implementation in New York City (NYC).
2 Gu J, et al.Inj Prev 2016;0:1–5. doi:10.1136/injuryprev-2016-042057
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Costs
We adjusted monetary costs to constant 2015 US$ using the
general consumer price index as listed in table 1. We went on to
obtain lifetime medical costs for non-fatal, hospitalised and fatal
injuries per person from the 2010 US averages using the CDC’s
Web-based Injury Statistics Query and Reporting System (CDC
WISQARS).
10
We also derived costs per person associated with
death (funeral) from the 2009 National Funeral Director’s
survey.
11
We included the cost of death (burial costs) in the
model, even though the entire cohort will eventually expire
over time, and thus the monetary cost of death is the same over
a lifetime, regardless of whether the cost of death is calculated
at an earlier or later point in life. The rationale for including
these costs in our calculations is that bike lanes have the poten-
tial to reduce premature deaths, allowing members of the popu-
lation to die of other causes in the later stages of life. Because of
this, the time course over which the death costs are discounted
is longer in the speed-reduction branch and shorter in the non-
intervention branch. This difference in the time of discounting
leads to monetary differences significant enough to include in
the model.
We obtained implementation cost and maintenance cost per
mile of bike lanes through a review of the literature and
reports.
12–15
We then multiplied the cost per mile by estimated
lane miles constructed in 2015, which we had derived from the
previous regression analysis.
We derived the literature sources for model input parameters
of cost, quality-adjusted life years (QALY), probabilities and
others through multiple keyword searches, related to the burden
of injury of bicycle rider studies, on Google Scholar, Web of
Science and PubMed. After sources were selected, they were
ranked according to levels of evidence.
Quality-adjusted life years
We assessed the impact of injury and death on victim’s
health-related quality of life (HRQL) using the EuroQol 5D
(EQ5D-5L). HRQL is required to calculate QALYs, and effect-
ively adjusts a year of life lived with a condition for health.
HRQL is scaled from 0 to 1, with 0 representing death and 1
representing a state of perfect health. Our objective was to
obtain a score that represented an injury that was serious
enough to require hospital admission. We assumed that other
injuries would only incur a transient decrease in HRQL and
would therefore not incur a meaningful change in HRQL over
the victim’s life course.
The EQ5D captures the following five domains of health:
mobility, anxiety/depression, self-care, usual activities and pain/
discomfort. We used an earlier estimate of the HRQL score of a
person who incurred an injury serious enough to be hospita-
lised. This score was obtained from two paediatric orthopaedic
surgeons at Columbia University Medical Centre who had
extensive experience following such individuals over the course
of their lives.
16
We used the average value of their predicted
EQ5D score, 0.55, in our model for injury victims who required
hospitalisation.
We followed the logic from Rabl and De Nazelle
17
to estimate
the additional gains in life expectancy (LE) due to increased
physical activity (PA) and reduced pollution. The LE gain due to
PA from 25 to 65 years old is 1.32 years. Thus, the LE gain
from PA per year is 0.033.
For air pollution, the dose-response function for mortality
due to chronic PM2.5 exposure is linear and with slope:
sDR=0.00065 years of life lost per person per year per μg/m
3
of PM2.5. Also the researchers estimated that avoided emis-
sions due to shift to bicycling is 71.8 g PM2.5/year. Thus, the
Table 1 Values used in the Markov model evaluating bike lane construction in 2015 versus the status quo
Variables
Abbreviation
in tree diagram Base SD Low High Data sources
Lifetime medical costs per capita ($)
Fatal injury cMedicald 11 973 5000 CDC
10
Non-fatal injury cMedicali 57 764 53 210 136 067
Death costs per capita ($) cFuneral 7306 5000 National Funeral Directors Association
11
Programme costs total/per capita ($)
Implementation 8 109 511/0.97 0.77 Elvik et al.;
13
Bushell et al.;
12
Litman;
14
Zegeer
15
Maintenance per year 532 971/0.06 0.06
Probability of injury
Non-riders pInjury 0.0004 0 0.0008 NYS DMV;
18
NYS DOH
19
Bike riders pInjuryBike 0.0008 0.0003 CDC;
10
Statista
7
Case fatality ratio pDeadi 0.08 0 0.15 NYS DMV
18
Probability of riding bikes, status quo pBike 0.013 0.003 ACS, 2009–2013
Increase in bike ridership, Vision Zero (%) 9.32 1 NYC DOT (2015b)
HR, injury, Vision Zero HRcycle 0.83 0.1 NYC DOT (2015c); Elvik et al
13
HRQL, injured uInjury 0.55 EQ5D survey
Average age (years) 36 10 New York City Department of City Planning, 2012
LE gain, Vision Zero (years)
From physical activity LEgain_pa 0.033 0 0.04 Rabl and De Nazelle
17
From reduced pollution LEgain_p 0.047 0 0.05 Rabl and De Nazelle
17
HRQL, health-related quality of life; LE, life expectancy; NYC, New York City.
Note: New York City Department of Transportation. 2015b. 2014 NYC In-Season Cycling Indicator - An Estimate of Trends in Regular Cycling for Transportation [Online]. http://www.nyc.
gov/html/dot/downloads/pdf/2014-isci.pdf (accessed 13 Jul 2015).
New York City Department of Transportation. 2015c. Manhattan Pedestrian Safety Action Plan [Online]. http://www.nyc.gov/html/dot/downloads/pdf/ped-safety-action-plan-manhattan.
pdf (accessed 14 Jul 2015).
New York City Department of City Planning. 2014. Population Facts [Online]. http://www1.nyc.gov/site/planning/data-maps/nyc-population/population-facts.page (accessed 8 Jan 2015).
Gu J, et al.Inj Prev 2016;0:1–5. doi:10.1136/injuryprev-2016-042057 3
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LE gain due to reduced pollution is 0.04667 QALYs. Besides
the QALYs gained from reduced injury, additional gain in LE
due to increased PA and changes in air pollution were also
taken into account using estimate obtained from the health
literature. New bike riders gain an average of 0.033 QALYs
per year from increased exercise and New Yorkers as a whole
gain 0.047 QALYs per year from reduced exposure to
pollution.
Probabilities
Probabilities and related inputs are listed in table 1. We obtained
the number of people seriously injured and killed from traffic
crashes in 2013 from New York State Department of Motor
Vehicles.
18
We used these numbers and the population in NYC
to calculate the probability of injury and the case fatality ratio
for both bike riders and non-riders.
19
Bike riders have a higher probability of injury than those who
use other forms of transportation. The number of traffic injuries
of cyclists in the USA was first derived from CDC WISQARS.
10
Dividing the number of injuries by the total estimated number
of cyclists in the USA,
7
we obtained the probability of getting all
levels of injuries for cyclists. A ratio of serious injuries to all
levels of injuries was then calculated using the numbers of
injured cyclists and treated and released cyclists.
10
We then cal-
culated the probability of serious injury for cyclists by multiply-
ing the probability of all injuries and the ratio of serious injury.
The HR injury when new bike lanes were constructed compared
with the status quo (before 45.5 miles of bike lane were added)
was obtained from a comprehensive review of the literature.
13 20
All outcomes in the model were adjusted to 2015 constant US$
and discounted at a rate of 3% per year.
21
Markov model
Our Markov model is based on a societal perspective and, as we
depict in figure 1, has two competing alternatives: adding bike
lanes or the status quo. Both the bike lane arm and the status
quo arm are the same except that the bike lane arm has an
increased probability of riding bikes, a reduced risk of injury for
bike riders, costs associated with bike lane implementation and
maintenance and additional effectiveness because of PA and
reduced pollution.
Irrespective of the arm of the model, a bike rider has a small
risk of serious injury from traffic incidents and a significant
chance of remaining healthy. Under the injury and non-injury
arms of both the status quo and bike lane alternatives, we devel-
oped a two-state Markov process such that every subject is
exposed to an annual, age-specific risk of death.
22
Survivors will
gain one HRQL-adjusted life year from age 36 to age 70 years,
reflecting the average age of cyclists in the city. If the rider
remains healthy, we assigned no costs except the one-time imple-
mentation costs and annual maintenance costs of bike lanes in
the bike lane alternative arm of the model. If the rider is injured,
we assigned an annual medical cost associated with a serious
injury and a decrement in HRQL over the average remaining LE
of the injured cyclist. Uninjured bicyclists gain both HRQL and
LE because of increased PA and reduced pollution. The under-
lying assumptions of the modelling approach are listed in table 2.
We used Monte Carlo simulation and one-way sensitivity ana-
lysis to test the reliability of the base-case result. We either
included what we recognised as plausible boundaries for the
values or included the known random error associated with an
estimate in the Monte Carlo simulation. We also employed half-
cycle corrections to adjust for related modelling uncertainties.
RESULTS
Cost-effectiveness of bike lanes
As shown in table 3, for all people in NYC, bike lane construc-
tion as a part of Vision Zero produced an additional cost of
$2.79 per person and an incremental gain of 0.0022 QALYs
over their lifetimes, compared with the status quo. The incre-
mental cost-effectiveness ratio (ICER) was $1297/QALY gained
(95% CI −$544/QALY gained to $5038/QALY gained).
Sensitivity analysis
An influence analysis (‘tornado’diagram) suggested that the
most important variable in the analysis was the probability of
injury. One-way sensitivity analysis showed that the value of
ICER would drop as the probability of getting injury increased.
However, the maximum value of the ICER was $1318/QALY
gained when the probability of injury was 0. Thus, our conclu-
sion that the programme was highly favourable was robust to a
wide variation in injury estimates based on exercise and pollu-
tion impacts alone.
CONCLUSION
Bicycle lanes address multiple public health problems simultan-
eously. They reduce injury and death, they promote exercise,
and they reduce pollution. We explored the cost-effectiveness of
1 year of investment in bike lanes in NYC as a case study. We do
so after considering costs and health effects of the investment
on New Yorkers and account for long-term benefits as well as
maintenance costs of the lanes installed in the built
Table 2 Assumptions used in the Markov model evaluating bike
lane construction in 2015 versus status quo
Assumption Rationale (impact on estimates)
Future lost productivity and leisure
time costs of injury are included
within the health-related quality-of-life
score
EQ5D scores may implicitly include lost
productivity and leisure time, however
this has been debated (favours status
quo)
Benefits of bike lane construction are
limited to bike riders
Construction of bike lanes may also
reduce injury risks for car drivers and
pedestrians (favours status quo)
The trend of bike lane construction
during the past 7 years will continue
in 2015
Bike lane construction may be more
emphasised because a citywide traffic
safety programme initiated in 2014.
(favours bike lane construction)
Mortality risk because of traffic injury
occurs only at the time of injury
Injury victims may be at higher risk of
future death both from physical
limitations and economic impact of the
injury on the victim’s life (favours
status quo)
Table 3 Costs and quality-adjusted life years (QALYs) of bike lane
construction in 2015 versus status quo
Point estimate 95% CI
Costs, status quo 31.28 (7.04 to 65.05)
Costs, bike lanes 34.07 (9.61 to 67.78)
Incremental costs 2.79 (−1.11 to 6.57)
QALYs, status quo 31.076 (24.8878 to 32.4646)
QALYs, bike lanes 31.0782 (24.8898 to 32.4673)
Incremental QALYs 0.0022 (0.0008 to 0.0038)
ICER 1296.5 (−544.08 to 5037.89)
ICER, incremental cost-effectiveness ratio.
4 Gu J, et al.Inj Prev 2016;0:1–5. doi:10.1136/injuryprev-2016-042057
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environment. In NYC, the investment comes in at an exception-
ally good value, costing just $1297/QALY gained for the 2015
investment cycle. This is far more cost-effective than preventive
approaches in medicine that few would argue should not be
implemented. For instance, screening and treatment for HIV/
AIDS in high-risk populations cost $50 000/QALY gained—
nearly 40 times as much.
23
It is also considerably more cost-
effective than either providing private health insurance or
expanding Medicaid.
16 24
Our study was subject to a number of important limitations.
Foremost, there are few causal estimates of the effects of bike
lanes on safety or, particularly, usage.
25
While studies consist-
ently show that bike lane construction is followed by increased
cycling and reduced injury, these ‘outcomes’might simply
reflect national trends towards an increased interest in cycling.
We managed this uncertainty by conducting broad sensitivity
analyses on model inputs.
Our model also uses generic references for potentially
important effects of bike lanes. Some of our references
accounted for the lifelong impacts of injuries, reduced expos-
ure to pollution and increased exercise afforded to the cyclist.
However, they measure average effects for an average individ-
ual living in an average city.
17
Yet, this only provides a snap-
shot of the impact of a city’s investment in bike paths. As
cycling grows more popular, it becomes safer to cycle. The
resulting increase in cyclists may generate momentum for
cycling or a change in political resistance to (or acceptance of )
cyclists. Some cities (eg, Portland, OR and Washington, DC)
have been able to tolerate a greater than 400% increase in
cyclists over the past two decades with relatively little political
resistance. Others, such as NYC, have seen the emergence of
anticycling groups as bike lanes and cyclists have proliferated.
To fully understand this relationship, it would be helpful to
have a complex systems dynamics model that could be tailored
to different urban contexts.
As the USA moves to invest more in non-medical policies in
the name of health prevention under the Affordable Care Act,
policymakers and payers are too often left wondering where
their investments might produce good value. Bike lanes are one
investment that certainly seems to fit the bill.
What is already known on the subject?
▸The USA has 67 million bicyclists, making over 300 million
trips per year in big cities alone with 700 deaths and 48 000
serious injuries per year which makes the USA the most
dangerous place among wealthy nations to bicycle.
▸Bike lanes reduce all forms of injury by 25%.
What this study adds?
▸We demonstrate that the incremental cost-effectiveness ratio
of bike lanes is $1297/ quality-adjusted life years (QALY)
gained (95% CI −$544/QALY gained to $5038/QALY
gained).
▸Our study considers multiple factors to highlight the need
for further investment in bike lanes as a crucial part of
urban infrastructure improvement.
Contributors JG and BM cooperated in the analysis and writing of this paper. PAM
supervised the analysis and cooperated in the writing and edited the final draft.
Funding This research was supported by Grant 1 R49 CE002096 from the National
Center for Injury Prevention and Control of the CDC to the Center for Injury
Epidemiology and Prevention at Columbia University Medical Center.
Disclaimer The contents of the manuscript are the sole responsibility of the
authors and do not necessarily reflect the official views of the funding agency.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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Gu J, et al.Inj Prev 2016;0:1–5. doi:10.1136/injuryprev-2016-042057 5
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York City
The cost-effectiveness of bike lanes in New
Jing Gu, Babak Mohit and Peter Alexander Muennig
published online September 9, 2016Inj Prev
2016-042057
http://injuryprevention.bmj.com/content/early/2016/09/09/injuryprev-
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