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The cost-effectiveness of bike lanes in New York City

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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.
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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
Citys (NYC) scal 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 ndings 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 benets. 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 trafcow and protect pedestrians.
Bike lanes take three forms. Class Ibike lanes
provide a route that is physically separated from
moving trafc. Class IIbike lanes have a marked
lane on the road. Finally, Class IIIbike lanes
merely provide shared road markings for drivers
and add safety legislation. Each of these comes
with a different set of risks and benets. 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 laneinvestment using New York
Citys (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 benet 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
rst 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 inuenced by temporary change in the
weather or road conditions that might inuence 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;0:15. doi:10.1136/injuryprev-2016-042057 1
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Participants
The Markov model (displayed in gure 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 gure 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
$8 109 511. NYC DOT also tracks trends in NYC cycling using
the In-Season Cycling Indicator, which is derived from counts
of bicycle trafc at several locations.
8
We ran a simple linear regression model with the percentage
of increase in ridership as the independent variable and the add-
itional bike lane miles constructed every year as the dependent
variable from 2007 to 2014. The percentage of increase in
ridership was calculated using the in-season cycling indicator of
each year. The estimated model is Y=0.004X0.0888, with R
2
equals to 0.59, which indicates that the model is able to explain
the variance of the dependent variable quite well.
This OLS analysis revealed that the percentage of expansion
in bike ridership would increase by 0.4% for every additional
one mile of bike lane construction. Using the tted equation, we
calculated that the probability of riding bikes would increase by
9.32% if 45.5 lane miles were constructed in 2015.
Figure 1 Markov model of bike lane implementation in New York City (NYC).
2 Gu J, et al.Inj Prev 2016;0:15. doi:10.1136/injuryprev-2016-042057
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Costs
We adjusted monetary costs to constant 2015 US$ 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 CDCs
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 Directors
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 signicant 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.
1215
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 victims
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 victims life course.
The EQ5D captures the following ve 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, 20092013
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).
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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 trafc
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 trafc injuries
of cyclists in the USA was rst 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 gure 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 trafc incidents and a signicant
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-specic risk of death.
22
Survivors will
gain one HRQL-adjusted life year from age 36 to age 70 years,
reecting 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 inuence analysis (tornadodiagram) 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 benets 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 victims 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;0:15. 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 outcomesmight simply
reect 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 citys 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 t 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 nal 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 reect the ofcial 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;0:15. doi:10.1136/injuryprev-2016-042057 5
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... For socioeconomic growth, as well as a robust, entrepreneur-friendly economy, broad plans are needed for businesses to be sustainable [49][50], including databases for investigations. Nugent et al. [39] noted several potential obstacles to such analytics platforms, including limited data interoperability, data acquisition expense, and a lack of standardized technical terminology for social and behavioral factors. ...
... We expect health policymakers to be more proactive, trying to demonstrate that, in addition to improving the population's health, health systems have favorable direct and indirect economic effects, in a thoughtful and structured investment. A strong economic argument for investment in health promotion and disease and injury prevention involves labor market participation and combating social inequalities, corroborating the findings of different authors [49][54] [73]. ...
... Regarding physical activity in other domains, interconnections to the environment are less clear. While active transport that replaces car use obviously saves greenhouse gas emissions and is also overall cost-effective, 22 the construction of bike lanes will at the same time cause greenhouse gas emissions. Occupational physical activity can take many forms, producing environmental co-benefits (eg, fitting a roof with solar panels), or causing environmental harm (extracting fossil fuels), depending on the nature of the work and the cooperation this is performed for. ...
... Protected bike lanes are generally separated from motorized vehicle traffic by physical barriers [13]. Research indicates that investments in bike lanes in New York City (NYC) are costeffective and result in increased use [12,14]. Daily bicycle use grew 104% between 2011 and 2021, with an estimated 30% of NYC residents riding a bike and nearly 900,000 doing so daily [12]. ...
Article
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Micromobility vehicles (MMVs) have become increasingly popular, particularly in urban areas where infrastructure has improved in recent years to facilitate their use. The purpose of this study was to observe protected bike lanes in 10 zones of Manhattan, NYC to: (1) describe the MMVs in bike lanes by type, phone and helmet use; and (2) document MMV users’ responses to obstructions. Approximately 1 in 4 of all riders (260/998) were wearing a helmet. Fewer than 2% were observed using a phone while moving. Fewer than 9% of Citi Bike users were wearing a helmet. In contrast, over one-third of non-Citi Bike users were wearing a helmet (228 of 670, 34.03%). This difference was determined to be significant by a chi-squared test (a = 0.05) with a p-value less than 0.0001. Of the 988 MMVs observed in this study, 398 (40.28%) were motorized and 590 (59.72%) were non-motorized. A similar proportion of users of motorized riders versus non-motorized vehicles were wearing a helmet (28.14%, 112/398 versus 24.41%, 144/590). A total of 232 riders (23.50%) encountered an obstruction in their bike lane. Of these obstructions in a bike lane, 82.33% (191/232) were a car/vehicle and 17.67% (41/232) was garbage. A large majority of riders (87.93%) reacted by riding into the traffic lane. These findings suggest that further research and local education, enforcement, and legislative efforts are needed to examine and implement best practices in the safe operation of MMVs, decreasing bike lane obstructions, promoting helmet use, and raising awareness of MMV legislation.
... While most HIA studies have been conducted in European cities, the general findings have been echoed in several U.S. studies. Three of these have been cost-benefit analyses that monetized the benefits of cycling but not its risks (Gotschi, 2011;Grabow et al., 2012;Gu et al., 2017); these studies estimated substantial economic benefits from planned cycling interventions in various U.S. cities. ...
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Health impact assessments (HIAs) have been used to evaluate the benefits and risks of cycling and other transportation interventions. Most HIAs use aggregate, city-level data rather than considering how impacts might vary across neighborhoods. To address this limitation, we developed a novel HIA framework for evaluating intra-city spatial variation and equity in the health tradeoffs of cycling. We illustrated the utility of this framework by applying it to Los Angeles, CA, estimating changes in mortality risk that might be expected from shifting a 2.5-mile daily car trip to cycling for five years. This shift was associated on average with a 12.4% net reduction in mortality risk, and a 50% increase in cycling could prevent approximately 600 deaths over five years. However, benefits were significantly lower in places with higher percentages of Black and Hispanic residents and lower socioeconomic status. To avoid widening health disparities, cycling promotion should be coupled with mitigation strategies in marginalized communities where risks are currently highest.
... The study used emission and dispersion models combined with existing ERRs from the literature and evaluated the policy impact across SES levels to check for potential social inequalities. For an instructive example of cost-effectiveness analysis (question 5c) that incorporates multiple pathways and health outcomes, we refer the readers to Gu et al. [55], who evaluated the cost-effectiveness of bike lane construction in New York City, US, in two phases. First, they estimated the impact of increasing bike lane miles on bike ridership using regression analysis. ...
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Purpose of Review Evaluating the environmental health impacts of urban policies is critical for developing and implementing policies that lead to more healthy and equitable cities. This article aims to (1) identify research questions commonly used when evaluating the health impacts of urban policies at different stages of the policy process, (2) describe commonly used methods, and (3) discuss challenges, opportunities, and future directions. Recent Findings In the diagnosis and design stages of the policy process, research questions aim to characterize environmental problems affecting human health and to estimate the potential impacts of new policies. Simulation methods using existing exposure–response information to estimate health impacts predominate at these stages of the policy process. In subsequent stages, e.g., during implementation, research questions aim to understand the actual policy impacts. Simulation methods or observational methods, which rely on experimental data gathered in the study area to assess the effectiveness of the policy, can be applied at these stages. Increasingly, novel techniques fuse both simulation and observational methods to enhance the robustness of impact evaluations assessing implemented policies. Summary The policy process consists of interdependent stages, from inception to end, but most reviewed studies focus on single stages, neglecting the continuity of the policy life cycle. Studies assessing the health impacts of policies using a multi-stage approach are lacking. Most studies investigate intended impacts of policies; focusing also on unintended impacts may provide a more comprehensive evaluation of policies.
... Many cross-sectional (Bopp, et al., 2015;Buehler & Pucher, 2011;Dill & Carr, 2003; K. J. Krizek & Johnson, 2006;Pucher & Dijkstra, 2000) or longitudinal interventional studies (J. Gu, Mohit, & Muennig, 2017;Henao et al., 2015;H. Li, Graham, & Liu, 2017) also find correlations between the amount of cycle infrastructure and cycling frequency at different aggregation levels. ...
Thesis
A broad combination of different methods is used in an integrated approach to evaluate interrelations between infrastructure and bicycle transport. First, the bike-friendliness of the urban environment (bikeability) is defined via a literature analysis in combination with an interactive expert survey. This definition of bikeability is then operationalized using open geodata, ensuring transferability. In addition, the effects of bikeability on mode choice are evaluated using a multinomial logit model. On the detailed level of route choice, the influencing parameters are further differentiated in a graphical online stated preferences survey. Mixed logit discrete choice models are then developed to quantify the trade-offs of interest. Furthermore, extensive data retrieved from a bike routing engine are clustered and analysed to reveal underlying route preferences, without the potential effects of an overt survey situation. Results show a consensus in understanding of bikeability, as provided by experts. This is defined by a stable interaction of the components composing bikeability. The mode choice model proves the strong positive effect of high bikeability on choosing the bike as a mode of transport. On the detailed level of route choice, the particular influence of cycling infrastructure along main streets is confirmed, and differentiated according to the specific design. Aside from specific individual and structural implications, a greater separation from motorized transport generally corresponds with a higher utility for cyclists. Regarding side streets, the results reveal the general importance of minor roads and the enormous benefit of cycle streets prioritizing cyclists. The presented findings may be used for further research and deliver recommendations for planning, which are discussed in the present study. Zur Analyse von Zusammenhängen zwischen Radverkehr und Infrastruktur kommt eine breite Kombination unterschiedlicher Methoden in einem integrierten Gesamtansatz zum Einsatz. An die Herleitung der radfahrtauglichen Umgebung (Bikeability) über eine Literaturanalyse und einen interaktiven Expertenprozess schließen sich die Operationalisierung dieser Definition mittels offener Geodaten sowie die Bewertung der Einflüsse auf die Verkehrsmittelwahl in einem multinomialen Verkehrsmittelwahlmodell an. Auf der Ebene der Routenwahl werden dann die Einflussgrößen in einem diskreten Entscheidungsexperiment differenziert. Dabei kommen logistische Regressionsmodelle zum Einsatz. Des Weiteren werden Daten aus der Fahrradnavigation in einem Clusterverfahren genutzt. Im Ergebnis zeigt sich ein konsensuales Verständnis von Bikeability unter Abbildung des Zusammenspiels der fünf wichtigsten infrastrukturellen Parameter. Durch Nutzung offener Geodaten ist der entwickelte Ansatz uneingeschränkt räumlich übertragbar und thematisch adaptierbar. Das Verkehrsmittelwahlmodell belegt den stark positiven Einfluss der Bikeability auf die Wahl des Fahrrades als Verkehrsmittel. Auf der differenzierten Ebene der Routenwahl bestätigt sich der besondere Einfluss der Radinfrastruktur an Hauptverkehrsstraßen. Die Ergebnisse zeigen dabei eine Abstufung im Nutzen für den Radverkehr, die dem Ausmaß der baulichen Trennung vom motorisierten Individualverkehr entspricht, sowie spezifische individuelle und strukturelle Implikationen. Neben Infrastrukturen an Hauptstraßen wird durch die angewandten Methoden auch die generelle Bedeutung von Nebenstraßen verdeutlicht und weiter differenziert. Die Ergebnisse zeigen dabei den enormen Nutzen von Fahrradstraßen aus Sicht der Nutzenden. Die Erkenntnisse bieten spezifische Anknüpfungspunkte, sowohl für weitere Forschung als auch für Planung und Praxis, die in der Arbeit diskutiert werden.
... Both stated preference [40,63] and revealed preference studies [44,69,70] conclude that cycle infrastructures are preferable to cycling in mixed traffic and can compensate for the adverse effects of busy streets. Many cross-sectional [13,14,16,71,72] or longitudinal interventional studies [73][74][75] also find correlations between the amount of cycle infrastructure and cycling frequency at different aggregation levels. In contrast, other cross-sectional [64] and revealed preference studies [62] refute such interrelations. ...
Article
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The present study aims to deduce bikeability based on a collective understanding and provides a methodology to operationalize its calculation based on open data. The approach contains four steps building on each other and combines qualitative and quantitative methods. The first three steps include the definition and operationalization of the index. First, findings from the literature are condensed to determine relevant categories influencing bikeability. Second, an expert survey is conducted to estimate the importance of these categories to gain a common understanding of bikeability and merge the impacting factors. Third, the defined categories are calculated based on OpenStreetMap data and combined to a comprehensive spatial bikeability index in an automated workflow. The fourth step evaluates the proposed index using a multinomial logit mode choice model to derive the effects of bikeability on travel behavior. The expert process shows a stable inter-action between the components defining bikeability, linking specific spatial characteristics of bikeability and associated components. Applied components are, in order of importance, biking fa-cilities along main streets, street connectivity, the prevalence of neighborhood streets, green path-ways and other cycle facilities, such as rental and repair facilities. The mode choice model shows a strong positive effect of a high bikeability along the route on choosing the bike as the preferred mode. This confirms that the bike friendliness on a route surrounding has a significant impact on the mode choice. Using universal open data and applying stable weighting in an automated work-flow renders the approach of assessing urban bike-friendliness fully transferable and the results comparable. It, therefore, lays the foundation for various large-scale cross-sectional analyses.
Article
Abstract Background Modelling studies consistently demonstrated significant health impacts resulting from shifts to active transport, such as walking and cycling, compared to other modes. However, limited understanding of diverse modelling approaches hinders the effectiveness of existing models in advancing future endeavours. Our study aims to compile modelling approaches, settings, and scenarios used in transport health impact modelling and assess the extent to which studies have quantified differential and equity impacts of transport mode shifts. Methods Following 2020 PRISMA guidelines, we systematically reviewed six databases: Ovid Medline, Ovid Embase, Scopus, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Transportation Research International Documentation. Search terms included transport modes, health outcomes, and assessment methodologies. The search covered articles published from 2014 onwards to minimize overlap with previous reviews. Two independent researchers screened records, resolving discrepancies by consensus. Results We identified 24,494 records and included 87 after full-text screening. Studies spanned 28 countries, with five having five or more exclusively focused studies: USA (n = 22), Australia (n = 10), UK (n = 6), Ireland (n = 5), and New Zealand (n = 5). Only 12 studies assessed low- or middle-income countries (China, India, Malaysia, Mauritius, Iran, and Brazil). Comparative risk assessment was the most used approach (n = 51), with Integrated Transport Health Impact Model (ITHIM) (n = 17) and World Health Organization HEAT Tool (n = 11) as popular tools. Scenarios covered hypothetical and observed travel patterns in diverse populations. Less than one-third of studies estimated impacts on population subgroups. Conclusion Multiple transport health impact models have been applied globally, adapting to various settings and environments. However, significant gaps remain in quantifying differential and equity impacts of transport scenarios across population groups, a critical priority due to prevalent health and transport inequities. Future modelling should capture the full range of health pathways associated with transport to enhance its utility in informing decision-making processes.
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Background Urban environments are important determinants of human health. The term walkability summarizes features of the urban built environment that promote walking and other types of physical activity. While the beneficial effects of active and public transport have been well established, the health impact of other features of walkability are less well documented. Methods We conducted a systematic review of health impact assessments (HIAs) of walkability. Studies were identified through PUBMED and Science Direct, from two German websites related to urban health and reference tracking. Finally, 40 studies were included in the present review. We applied qualitative thematic analysis to summarize the major results from these studies. Results Most of the HIAs (n = 31) reported the improvement of health or health behaviour resulting from an investigated project or policy. However, three HIAs reported a lack of improvement or even a decrease of health status. In parallel, 13 HIAs reported a gain in economic value, whereas one reported a lack or loss of economic effects. Moreover, three HIAs reported on social effects and six HIAs gave additional recommendations for policies or the implementation of projects or HIAs. Conclusions Most HIAs investigate the impact of increasing active or public transport. Other features of walkability are less well studied. With few exceptions, HIAs document beneficial impacts of improving walkability on a variety of health outcomes, including reductions of mortality and non-communicable diseases.
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For a variety of environmental, health, and social reasons, there is a pressing need to reduce the automobile dependence of American cities. Bicycles are well suited to help achieve this goal. However, perceptions of rider safety present a large hindrance toward increased bicycle adoption. These perceptions are largely influenced by the design of our current road infrastructure, including the crossing distances of large intersections. In this paper, we examine the role of intersection crossing distances in modifying rider behavior through the construction of a novel dataset integrating street widths and probable trip routes from Chicago’s Divvy bikeshare system. We compare real trips to synthetic trips that are not influenced by the width of intersections and exploit behavior differences that result from the semi-dockless nature of the bikeshare system. Our analysis reveals that bikeshare riders do avoid large intersections in limited circumstances; however, these preferences appear to be heavily outweighed by the relative spatial positions of origins and destinations (i.e., the urban morphology of Chicago). Our results suggest that specific infrastructural investments such as protected intersections could prove feasible alternatives to reduce the perception and safety concerns associated with large road barriers and enhance the attractiveness of non-motorized mobility.
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Objectives—This report presents complete period life tables for the United States by race, Hispanic origin, and sex, based on age-specific death rates in 2010. Methods—Data used to prepare the 2010 life tables are 2010 final mortality statistics; April 1, 2010 population estimates based on the 2010 decennial census; and 2010 Medicare data for persons aged 66-99. The methodology used to estimate the 2010 life tables was first implemented with data year 2008. The methodology used to estimate the life tables for the Hispanic population remains unchanged from that developed for the publication of life tables by Hispanic origin for data year 2006. Results—In 2010, the overall expectation of life at birth was 78.7 years. Between 2009 and 2010, life expectancy at birth increased for all groups considered. Life expectancy increased for both males (from 76.0 to 76.2) and females (80.9 to 81.0) and for the white population (78.8 to 78.9), the black population (74.7 to 75.1), the Hispanic population (81.1 to 81.4), the non-Hispanic white population (78.7 to 78.8), and the non-Hispanic black population (74.4 to 74.7).
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We examined the public health consequences of unsafe and inconvenient walking and bicycling conditions in American cities to suggest improvements based on successful policies in The Netherlands and Germany. Secondary data from national travel and crash surveys were used to compute fatality trends from 1975 to 2001 and fatality and injury rates for pedestrians and cyclists in The Netherlands, Germany, and the United States in 2000. American pedestrians and cyclists were much more likely to be killed or injured than were Dutch and German pedestrians and cyclists, both on a per-trip and on a per-kilometer basis. A wide range of measures are available to improve the safety of walking and cycling in American cities, both to reduce fatalities and injuries and to encourage walking and cycling.
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Objective: We evaluated the cost-effectiveness of a package of roadway modifications in New York City funded under the Safe Routes to School (SRTS) program. Methods: We used a Markov model to estimate long-term impacts of SRTS on injury reduction and the associated savings in medical costs, lifelong disability, and death. Model inputs included societal costs (in 2013 US dollars) and observed spatiotemporal changes in injury rates associated with New York City's implementation of SRTS relative to control intersections. Structural changes to roadways were assumed to last 50 years before further investment is required. Therefore, costs were discounted over 50 consecutive cohorts of modified roadway users under SRTS. Results: SRTS was associated with an overall net societal benefit of $230 million and 2055 quality-adjusted life years gained in New York City. Conclusions: SRTS reduces injuries and saves money over the long run.
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Objective. —To develop consensus-based recommendations for the conduct of cost-effectiveness analysis (CEA). This article, the second in a 3-part series, describes the basis for recommendations constituting the reference case analysis, the set of practices developed to guide CEAs that inform societal resource allocation decisions, and the content of these recommendations.Participants. —The Panel on Cost-Effectiveness in Health and Medicine, a nonfederal panel with expertise in CEA, clinical medicine, ethics, and health outcomes measurement, was convened by the US Public Health Service (PHS).Evidence. —The panel reviewed the theoretical foundations of CEA, current practices, and alternative methods used in analyses. Recommendations were developed on the basis of theory where possible, but tempered by ethical and pragmatic considerations, as well as the needs of users.Consensus Process. —The panel developed recommendations through 21/2 years of discussions. Comments on preliminary drafts prepared by panel working groups were solicited from federal government methodologists, health agency officials, and academic methodologists.Conclusions. —The panel's methodological recommendations address (1) components belonging in the numerator and denominator of a cost-effectiveness (C/E) ratio; (2) measuring resource use in the numerator of a C/E ratio; (3) valuing health consequences in the denominator of a C/E ratio; (4) estimating effectiveness of interventions; (5) incorporating time preference and discounting; and (6) handling uncertainty. Recommendations are subject to the "rule of reason," balancing the burden engendered by a practice with its importance to a study. If researchers follow a standard set of methods in CEA, the quality and comparability of studies, and their ultimate utility, can be much improved.
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We evaluated the effects of on-street bicycle lanes installed prior to 2007 on different categories of crashes (total crashes, bicyclist crashes, pedestrian crashes, multiple-vehicle crashes, and injurious or fatal crashes) occurring on roadway segments and at intersections in New York City. We used generalized estimating equation methodology to compare changes in police-reported crashes in a treatment group and a comparison group before and after installation of bicycle lanes. Our study approach allowed us to control confounding factors, such as built environment characteristics, that cannot typically be controlled when a comparison group is used. Installation of bicycle lanes did not lead to an increase in crashes, despite the probable increase in the number of bicyclists. The most likely explanations for the lack of increase in crashes are reduced vehicular speeds and fewer conflicts between vehicles and bicyclists after installation of these lanes. Our results indicate that characteristics of the built environment have a direct impact on crashes and that they should thus be controlled in studies evaluating traffic countermeasures such as bicycle lanes. To prevent crashes at intersections, we recommend installation of "bike boxes" and markings that indicate the path of bicycle lanes across intersections.
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Most individuals prefer bicycling separated from motor traffic. However, cycle tracks (physically separated bicycle-exclusive paths along roads, as found in The Netherlands) are discouraged in the USA by engineering guidance that suggests that facilities such as cycle tracks are more dangerous than the street. The objective of this study conducted in Montreal (with a longstanding network of cycle tracks) was to compare bicyclist injury rates on cycle tracks versus in the street. For six cycle tracks and comparable reference streets, vehicle/bicycle crashes and health record injury counts were obtained and use counts conducted. The relative risk (RR) of injury on cycle tracks, compared with reference streets, was determined. Overall, 2.5 times as many cyclists rode on cycle tracks compared with reference streets and there were 8.5 injuries and 10.5 crashes per million bicycle-kilometres. The RR of injury on cycle tracks was 0.72 (95% CI 0.60 to 0.85) compared with bicycling in reference streets. These data suggest that the injury risk of bicycling on cycle tracks is less than bicycling in streets. The construction of cycle tracks should not be discouraged.
Article
There is a growing awareness that significant benefits for our health and environment could be achieved by reducing our use of cars and shifting instead to active transport, i.e. walking and bicycling. The present article presents an estimate of the health impacts due to a shift from car to bicycling or walking, by evaluating four effects: the change in exposure to ambient air pollution for the individuals who change their transportation mode, their health benefit, the health benefit for the general population due to reduced pollution and the risk of accidents. We consider only mortality in detail, but at the end of the paper we also cite costs for other impacts, especially noise and congestion. For the dispersion of air pollution from cars we use results of the Transport phase of the ExternE project series and derive general results that can be applied in different regions. We calculate the health benefits of bicycling and walking based on the most recent review by the World Health Organization. For a driver who switches to bicycling for a commute of 5 km (one way) 5 days/week 46 weeks/yr the health benefit from the physical activity is worth about 1300 €/yr, and in a large city (>500,000) the value of the associated reduction of air pollution is on the order of 30 €/yr. For the individual who makes the switch, the change in air pollution exposure and dose implies a loss of about 20 €/yr under our standard scenario but that is highly variable with details of the trajectories and could even have the opposite sign. The results for walking are similar. The increased accident risk for bicyclists is extremely dependent on the local context; data for Paris and Amsterdam imply that the loss due to fatal accidents is at least an order of magnitude smaller than the health benefit of the physical activity. An analysis of the uncertainties shows that the general conclusion about the order of magnitude of these effects is robust. The results can be used for cost-benefit analysis of programs or projects to increase active transport, provided one can estimate the number of individuals who make a mode shift.
Article
To develop consensus-based recommendations for the conduct of cost-effectiveness analysis (CEA). This article, the second in a 3-part series, describes the basis for recommendations constituting the reference case analysis, the set of practices developed to guide CEAs that inform societal resource allocation decisions, and the content of these recommendations. The Panel on Cost-Effectiveness in Health and Medicine, a nonfederal panel with expertise in CEA, clinical medicine, ethics, and health outcomes measurement, was convened by the US Public Health Service (PHS). The panel reviewed the theoretical foundations of CEA, current practices, and alternative methods used in analyses. Recommendations were developed on the basis of theory where possible, but tempered by ethical and pragmatic considerations, as well as the needs of users. The panel developed recommendations through 2 1/2 years of discussions. Comments on preliminary drafts prepared by panel working groups were solicited from federal government methodologists, health agency officials, and academic methodologists. The panel's methodological recommendations address (1) components belonging in the numerator and denominator of a cost-effectiveness (C/E) ratio; (2) measuring resource use in the numerator of a C/E ratio; (3) valuing health consequences in the denominator of a C/E ratio; (4) estimating effectiveness of interventions; (5) incorporating time preference and discounting; and (6) handling uncertainty. Recommendations are subject to the ¿rule of reason,¿ balancing the burden engendered by a practice with its importance to a study. If researchers follow a standard set of methods in CEA, the quality and comparability of studies, and their ultimate utility, can be much improved.