US transportation agencies are expressing increased interest in integrating health considerations
into decision-making (USDOT, 2014; USDOT, 2012). Additionally, the use of HIA has been growing
rapidly in the transportation sector (Dannenberg et al., 2014). While a handful of existing policy
mechanisms exists to consider health impacts in transportation decision-making, applications of these
mechanisms are limited. In air quality nonattainment areas (areas not meeting the requirements of the
Clean Air Act), funds from the Congestion Mitigation and Air Quality Improvement Program may be
used to fund transportation projects that improve air quality and improve public health (USDOT, 2016).
Additionally, new highway and transit projects that may increase emissions from diesel vehicles in
nonattainment and maintenance areas are now required to conduct quantitative PM hot-spot analyses at
the project scale (EPA, 2015). Similar stipulations exist for CO emissions from a wider range of projects
in nonattainment and maintenance areas (EPA, 2015). Finally, many transportation agencies seek to
reduce fatal crash rates on a per-VMT basis, failing to consider that increases in VMT offset the benefits
of programs that reduce per-VMT health risks (Litman, 2014). While increasing physical activity is
sometimes considered a potential benefit of transportation systems, the health risks of built environments
that discourage active transportation behaviors are rarely conceptualized as a health impact of
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multiple transportation health risks and offers a substantially more rigorous approach than is offered by
current tools and approaches.
As interest in the health impacts of transportation has grown, so has the development of decision-
support tools seeking to bridge the gap between transportation and public health agencies. However, these
emerging tools often provide data at low spatial resolution and provide little insight into the relationships
between transportation systems, exposures to transportation health risks, and health impacts. Without
sufficient spatial resolution or linkages between exposure and health outcomes, such tools fail to
adequately inform transportation decision-makers how their decisions may impact public health. Lacking
tools to estimate the health impacts of specific transportation decisions, a number of transportation
agencies have recently included health metrics into established decision-making processes as an approach
to make progress towards public health goals. At the state level, a number of state departments of
transportation have including health-related metrics in structured decision-making processes, such as the
inclusion of bicycle and pedestrian mode share as a recommended evaluation metric in Caltrans’ Smart
Mobility framework (USDOT, 2014). Local-level efforts have also focused on including health metrics in
structured decision-making, such as awarding points to transportation projects that address identified
health disparities in prioritizing project funds in Nashville, TN (USDOT, 2012). A critical gap in these
existing frameworks is the lack of modeling tools to translate transportation-related exposures to
population health impacts. Improved modeling of transportation health impacts helps clarify the pathway
from exposure to health impacts and could be a valuable tool in integrating health considerations into
routine transportation decision-making.
The modeling framework employed in this dissertation is well-suited to integrate with recent
innovations in transportation demand modeling. Traditional four-step travel demand models first generate
trips at the household level, distribute these trips across space, assign modes to these trips, and finally
assign these trips onto the transportation network. Four-step models can be used to support health impact
assessments of transportation air quality impacts (Mansfield et al., 2014). However, the usefulness of
urban area into “transportation analysis zones” (TAZs). Four-step models trips estimate between, but not within, TAZs; thus, active trips with short distances (i.e., occurring entirely within a TAZ) are not
modeled. Four-step transportation demand models are increasingly being replaced with activity-based
transportation demand models, which offer much finer geographic resolution and provide detailed
estimations of travel behaviors at the individual level (TRB, 2015). A necessary step when building an
activity-based travel demand model is the generation of a synthetic population for which the model will
estimate travel behaviors. An emerging literature explores the development of synthetic populations in
urban areas for this purpose (Zhu & Ferreira, 2014). Critically, the microsimulation framework used in
this research could easily use the same synthetic population as activity-based transportation demand
models. Detailed predictions of individual-level travel behaviors (including trip modes, distances, and
locations in an urban area) could easily be used to characterize individual-level exposure in the model
used in this dissertation. Thus, this work provides a framework that could support the integration of
detailed population-level health impacts into routine travel demand modeling activities as activity-based
travel demand models gain prominence in the field.
Even without complete integration with transportation demand models, the model developed in
this dissertation could support the integration of health considerations into a range of decisions about the
built environment. The model is modular and scalable, enabling its application in many routine
transportation decision-making practices. Metropolitan planning organizations could use the model
developed in this dissertation to support a variety of planning efforts. Integrated land use and
transportation planning efforts could be compared based a range of health metrics. The model could also
be used to include health outcomes when transportation agencies make funding decisions under budget
constraints (e.g., project prioritization). The model could be downscaled further to compare health
outcomes between alternatives at the project or corridor scale, offering transportation agencies a means to
quantify public health outcomes as a part of the National Environmental Policy Act (NEPA) process. In
addition, the model could be used to consider transportation health risks alongside other health risks that
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modeling framework is modular and can be modified to include additional intermediate disease pathways
and additional exposures. In sum, the modular and scalable design of the model developed here presents
an opportunity to integrate health considerations into transportation decision-making in a much more
rigorous manner than is currently practiced and to provide robust decision-support as the fields of
transportation and public health continue to converge.
5.3. Limitations
While providing a more rigorous approach to estimating the population health impacts of
transportation systems, the model developed in this dissertation has substantial data requirements.
Notably, dynamic models require detailed age-specific functions characterizing baseline death rates,
disease prevalence, and disease incidence. Because mortality is a rare event for younger age groups, state-
level data were needed to develop baseline death rate functions. Additionally, incidence data are not
readily available for many diseases in the US; thus, disease incidence was estimated using the World
Health Organization’s DisMod II tool. However, baseline model calibration revealed reasonable model performance despite estimations in underlying epidemiological data. Finally, epidemiological evidence
linking chronic exposure to PM2.5 to morbidity is limited. Thus, the model developed in this dissertation
does not consider morbidity related to air pollution exposure. Practically, the model used in this work is
very computationally intensive due in large part to the size of the transition matrices within the Markov
model. While executable on a typical desktop workstation, the computational demands present practical
limitations to performing uncertainty analysis using techniques such as Monte Carlo simulation.
In addition to limitations of the health impacts model itself, estimation of individual-level
exposures to transportation health risks presents challenges. The most recent estimates of PM2.5
concentrations across the region were for 2011, while other exposures were estimated for 2013.
Additionally, it is assumed that PM2.5 concentrations are constant over time; however, PM2.5
concentrations will change in the future as the vehicle fleet changes and travel behaviors shift. While the
model is capable of incorporating exposures that vary over time, limitations in available estimates of
model used to estimate PM2.5 concentrations included most road segments in the study region, traffic
levels on many smaller roadways are not routinely collected as part of the Federal Highway
Administration’s National Highway Performance Program. Contributions to ambient PM2.5 from these
roadway segments were not included in estimated PM2.5 concentrations used in this study. Finally,
chemical pathways leading to secondary formation of PM2.5 were not modeled in the work used to
characterize PM2.5. Secondary formation of other pollutants in ambient, such as ozone, are also not
considered. However, secondary formation of air pollutants likely occurs after some atmospheric mixing
and is thus likely to vary less over space and, in turn, vary less in relation to neighborhood-scale built
environment variables. Despite these limitations, use of an advanced line-source dispersion model to
provide high-resolution estimates of PM2.5 across the study region offered a much more detailed
characterization of exposure to air pollution than used in previous studies. Further, the modeling
framework developed in this work could easily incorporate new exposure information as advancements in
high-resolution air quality modeling continue.
Additionally, the model used in this dissertation does not consider increased inhalation rates
during active transportation. Active commuters may be exposed to more air pollution while walking or
biking alongside roadways and may inhale greater amounts of pollutants in ambient air due to increased
respiration rates while being active. Individuals may also be more active during times of the year when
photochemistry is more active. However, using annual average pollutant concentrations masks potential
seasonal effects on total inhalation doses of airborne pollutants. Further, the modeling framework used
here does not predict where active transportation behaviors will occur—while walking trips may occur in
the same block group as an individuals’ home, biking trips and walk trips from public transit may occur in other block groups. Annual average PM2.5 concentrations are also used in this work to characterize
exposure while acute exposure for active commuters may vary significantly within and between days.
Additionally, it is unclear how increased acute exposure to air pollution may modify risks estimated in
long term cohort studies (Pope et al., 2002). However, Woodcock et al. assume an increase in air
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health impacts (2014). Thus, the magnitude of underestimation in health risks for active commuters due to
this limitation is likely minor.
Estimations of transportation physical activity are largely based on individual commute modes,
while walking and biking trips may be taken by individuals who typically drive to work. While built
environment variables—population density and percentage of rental units—are considered and have
significant effects on transportation physical activity, the magnitude of these effects is small relative to
the magnitude of the commute mode to work variable (Figure 3.3). Thus, the transportation physical
activity predictions used in this work may under-predict walking and biking trips for individuals who live
in walkable environments but commute to work using a private vehicle. Additionally, fixed-route and
demand-responsive (paratransit) services are both considered public transit and riders who access transit
via park-and-ride lots are combined with users who walk or bike to access transit. Thus, additional factors
that may influence walking and biking associated with public transit use, but are not reported at the
Census block group geography in the American Community Survey, may lead to over-predictions of
active travel related to transit usage in areas with paratransit service only (e.g., rural areas) and for transit
users who use park-and-ride lots to access public transit. Finally, downscaling national level data to
estimate transportation physical activity in the study region may result in some upwards or downwards
bias due to unobserved variables. Despite these limitations, the regression models used to predict
transportation physical activity model performed well when validated in the study region.
The modeling framework used in this dissertation also does not consider area-level built
environment factors or the safety-in-numbers phenomenon in estimating fatal crash risk. Epidemiological
evidence used in this work conceptualizes fatal crash risk on the individual level, which limits the ability
of the model to consider area-based risk modification. Because this framework also does not consider the
specific locations of walking and biking trips, the ability to consider the role of specific built environment
variables in modifying fatal crash risk is constrained. Additionally, the model developed here considers
only mortality from motor vehicle, pedestrian, and bicycle trips in part due to methodological difficulties
environment. Despite these limitation, this work still finds the lowest total fatal crash risk in the most
walkable neighborhoods—neighborhoods that would be most likely to have built environments that
support active transportation and have a higher number of commuters. Incorporating risk reductions from
area-level factors and the safety-in-numbers phenomenon would likely strengthen the revealed association
between neighborhood walkability and fatal crash risk.
Finally, the model developed in this dissertation is applied in only one case study region. The
relationship between the built environment and air quality is influenced by exogenous policy variables,
such as regulatory regimes for automobile emissions. Additionally, fatal crash risks may be modified by
vehicle-level safety standards, cultural norms regarding driving, and other factors. Transportation physical
activity levels may also vary relative to built environment characteristics in different ways in different
cultural contexts. Thus, the magnitude of risk tradeoffs between air pollution, transportation physical
activity, and fatal crashes may differ in cities in less developed countries and in emerging mega-cities
with weak environmental regulation (e.g., Asian mega-cities). However, while the conclusions regarding
risk tradeoffs discussed here are not widely generalizable, the modeling framework developed in this
dissertation could be applied in other contexts to explore risk tradeoffs within different regulatory and
cultural contexts.