Health and Transportation Data

SECTION 1: DATA INVENTORY

Transportation and public health data can be broadly categorized into three groups: data describing the built environment and transportation system, measures of transportation-related exposures and transportation behaviors that impact health, and measures of health outcomes associated with transportation-related exposures and transportation behaviors. Data sources are discussed for these broad categories below.

Built Environment and Transportation System Characteristics

Transportation Infrastructure: Roadways

Physical and operational characteristics of the transportation system influence transportation health impacts. At a national level, the Highway Performance Monitoring Systems (HPMS) compiles data on roadway network extent, use, condition, and performance. The HPMS is focused on motorized travel and does not capture detailed roadway characteristics, such as the presence of sidewalk and bike lanes, that may be relevant to public health. More detailed roadway characteristic data may be available at the local level via local governments or municipal planning organizations. Additionally, the 2nd Strategic Highway Research Program (SHRP2) Roadway Information Database (RID) provides detailed road segment data in six major areas (Tampa, FL; Bloomington, IN; Buffalo, NY; Raleigh/Durham, NC; State College, PA; and Seattle. WA). Increasingly, mapping services like Google Maps and OpenStreetMap are integrating more detailed bicycle and pedestrian infrastructure information. Further, OpenSidewalks is working to standardize and improve sidewalk data within OpenStreetMap. Data collection to support these data can include cell phone apps to encourage citizen data contributions, such as Sidewalk Scout (opens new window). More information on the HPMS can be found at https://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/ (opens new window) and on the SHRP2 RID at https://ctre.iastate.edu/roadway-information-database-rid/ (opens new window).

Transportation System: Transit Service

While typically available from local transit providers, the quality of transit service provision varies widely across the United States. As real-time transit service applications became more popular, the General Transit Feed Specification (GTFS) was developed to provide a consistent format for transit agencies to provide stop, route, and scheduled service information to third parties, such as Google Maps. USDOT has developed the National Transit Map to make GTFS data voluntarily provided by local transit agencies generally available to the public. The National Transit Map is a nationwide catalog of fixed-guideway and fixed-route transit service data, including transit stop, route, and schedules information. Because transit agencies are not required to report to USDOT via the GTFS, data coverage is inconsistent nationally and data quality, especially for route information, varies substantially between agencies. While GTFS is limited to fixed-route transit service, GTFS-flex is an extension currently in development to extend GTFS capabilities to include demand-responsive transit services. Finally, basic transit system data, such as system-level ridership, is available via the National Transit Database. More information of the National Transit Map can be found at https://www.bts.gov/content/national-transit-map (opens new window) and the National Transit Database as https://www.transit.dot.gov/ntd (opens new window). Additional information on the GTFS-flex prototype can be found at https://github.com/MobilityData/gtfs-flex (opens new window).

Built Environment

Characteristics of the built environment measures have well-documented influences on travel behavior and can support—or present barriers to—active transportation. The American Community Survey (ACS) provides basic built environment information, such as population density, with high geographic resolution. Employment density can be obtained from a number of sources. The Longitudinal Employer Household Dynamics Survey (LEHD) offers the Origin-Destination Employment Statistics (LODES) dataset, which contains counts of employees by industry sector derived from administrative records for all block groups in the United States. The YourEconomy Time Series (YTS, formerly called the National Establishment Time Series) database offers more detailed data; however, the full database is available only for purchase. While providing high-resolution data on employment locations, both the LODES and NETS databased are derived from administrative records for places of employment and therefore suffer from the “headquarters problem”—administrative records may list the address of an establishment’s headquarters, rather than the address of a worker’s specific place of employment. More information on LODES can be found at https://lehd.ces.census.gov/data/ (opens new window) and more information on YTS can be found at http://youreconomy.org/profile/about.lasso (opens new window). Finally, measures of land use diversity, often taking the form of an entropy index, can be derived from local land use data, such as parcel land use classification databases. However, such data are not universally available and may require substantial cleaning and verification. Alternatively, land use diversity measures may be derived from the industry sector employment categories as a proxy for various commercial and industrial land uses coupled with Census population as a proxy for residential land use. The United States Geological Survey (USGS) provides estimates of global land cover at 30-meter resolution: https://www.usgs.gov/core-science-systems/national-geospatial-program/land-cover (opens new window). The National Land Cover Database (NLCD) includes estimates of land cover for a series of years starting in 2001, and can be used to track changes over time (https://www.mrlc.gov/). The Shuttle Radar Topography Mission (SRTM) obtained elevation data at a nearly-global scale of coverage, down to resolution of 30 meters for the U.S. https://www2.jpl.nasa.gov/srtm/ (opens new window).

Transportation Behaviors and Exposures

Geographic Commuter Flows

Several data resources provide origin-destination information for individuals’ work commutes. The Census Transportation Planning Package (CTPP) provides home to work origin-destination data for commuters at small geographies as well vehicle availability by household. Origin-destination pairs for small geographies are often limited at small geographies to protect respondent confidentiality, especially for less common work modes (e.g., walking and biking). Preparation of the data by the Census Bureau takes several years and has been complicated by changes in survey methods employed by the American Community Survey. In addition to the CTPP, the LODES Origin-Destination (OD) data provided above also contain Census block-level commuter flow data for the United States, stratified by several worker characteristics (three bins each for worker age, worker income, and worker industry sector). Both the CTPP and LODES OD data provide only commuter flows, which do not capture non-work travel such as recreational, social, or shopping trips. More information on the CTPP can be found at https://ctpp.transportation.org/ (opens new window) and on the LODES OD data at https://lehd.ces.census.gov/data/ (opens new window).

Household Travel Surveys

Travel surveys are designed to meet a variety of needs, and they typically contain detailed information on travel behaviors, including walking, biking, and taking public transit. While walking and biking trips may be recorded directly when a respondent completes the travel survey, respondents may walk or bike intermittently and these trips may not be recorded. However, travel diaries may not capture all walking and biking trips to and from public transit. Travel surveys such the National Household Travel Survey (NHTS) have supplemented travel diaries with questions asking respondents about the frequency of walking and biking trips over longer periods of time, such as walking trips in a typical week. Similarly, travel surveys may also contain questions to capture longer-term driving behaviors, such as per-capita yearly vehicle-miles travelled. The quality and depth of household travel surveys, especially at the local level, vary greatly. The NHTS offers a detailed, high-quality snapshot of travel behavior across the United States; however, it is administered infrequently. Additionally, states and MPOs can request to be over-sampled and/or include additional add-on questions in their NHTS, which may include health-related questions. The latest NHTS should be publicly available in early 2018. More information on the NHTS can be found at https://nhts.ornl.gov/ (opens new window).

Time Use Surveys

Like household travel surveys, time use surveys capture transportation physical activity as a component of the larger survey instrument. The American Time Use Survey (ATUS) contains information on walking and biking by respondents; however, the limited sample size and breadth of the survey limit its usefulness in measuring transportation physical activity. Additionally, the U.S. Environmental Protection Agency maintains the Consolidated Human Activity Database (CHAD), which catalogues time use data from a number of studies. More information on the ATUS can be found at https://www.bls.gov/tus/ (opens new window) and on CHAD at https://www.epa.gov/healthresearch/consolidated-human-activity-database-master-version-chad-master-technical-memorandum (opens new window).

Physical Activity Surveys

Broad physical activity surveys often contain questions specific to physical activity from transportation. The International Physical Activity Questionnaire (IPAQ) prompts respondents to recall walking and biking trips over the previous 7 days. A copy of the IPAQ can be found at http://www.sdp.univ.fvg.it/sites/default/files/IPAQ_English_self-admin_long.pdf (opens new window). Similar questions may also appear in routine health surveys, including the BRFFS (e.g., when administering the survey, North Carolina has included transportation physical activity and/or greenway use as supplemental questions in most years of the survey since 2009), NHANES, and NHIS. While physical activity recall questions are relatively easy to include in a survey, respondents tend to over-report physical activity in recall surveys compared to objectively measured physical activity. Finally, limited physical activity survey data are routinely collected in the ACS via the commute mode to work question. The differences between reported physical activity in various surveys were recently explored in a circular developed by staff at the CDC, available at https://www.cdc.gov/mmwr/preview/mmwrhtml/ss6407a1.htm (opens new window). The CDC also developed a more general resource on physical activity surveillance, available at https://www.cdc.gov/nccdphp/dnpa/physical/pdf/pa_qs_surveillance.pdf (opens new window).

Physical Activity Tracking Devices

Transportation physical activity levels are measured in several routine surveys and can be tracked using accelerometer- and GPS-based devices. A growing number of commercially available devices can be used to track daily physical activity, including transportation physical activity. Accelerometer-based devices record movement throughout the day, but are sensitive to the location worn on the body (e.g., on the wrist versus on the hip) and do not identify specific activities. Methods such as machine learning have been explored to derive activities from patterns of accelerometer readings, but consistent methods to do so are not readily available. GPS-based devices record location in real-time and typically prompt a user to enter the beginning and end of an activity, such as a bike ride, to identify the specific activity. Other devices use both accelerometer and GPS data to measure activity levels and identify activity type in real time, allowing the user to correct incorrectly identified activities and potentially refine activity identification over time.

Air Quality

Transportation systems have a direct impact on ambient air quality, which in turn influences a number of health outcomes. In the United States, the Environmental Protection Agency maintains a network of air quality monitors and provides real-time and historic concertation data for National Ambient Air Quality Standard pollutants at these sites. EPA’s Air Quality System (AQS) contains ambient air pollution data collected by EPA, state, local, and tribal agencies from thousands of monitors throughout the United States, including a network of near-road monitors (https://www.epa.gov/aqs (opens new window)). NASA also publishes data from satellite monitors, such as the Ozone Monitoring Instrument at https://aura.gsfc.nasa.gov/omi.html (opens new window). However, the limited spatial resolution of the air quality monitoring network in the United Sates often requires supplemental techniques to estimate individual exposure to pollutant in ambient air, such as spatial interpolation, spatial regression, or air quality modeling.

Transportation Noise

The Bureau of Transportation Statistics at USDOT recently developed a national transportation noise map. The noise map facilitates the tracking of trends in transportation-related noise, by mode, and collectively for multiple transportation modes. The data allow viewing the national picture of potential exposure to aviation and highway noise. The data also allow viewing of the potential exposure at the state or county level. More details on the National Noise Map are available at https://www.bts.gov/geospatial/national-transportation-noise-map (opens new window).

Health Outcomes

Population Health Surveys

A number of routinely administered surveys collect data on population health. While transportation health impacts are not a focus of any single survey, most surveys contain questions on health outcomes that are sensitive to transportation-related exposures. In the United States, useful surveys include the Behavioral Risk Factor Surveillance System (BRFSS) survey (opens new window), which is managed by the Centers for Disease Control and Prevention (CDC) and administered by state health departments on an annual basis. The BRFSS contains a number of core questions addressing the health status and behaviors of the population. States can include supplemental add-on questions. The CDC also administers the National Health Interview Survey (NHIS) (opens new window) and the National Health and Nutrition Examination Survey (NHANES) (opens new window), both of which use in-person interviews to estimate the health status and health behaviors for demographic groups of adults in the United States. Health survey data help transportation and public health practitioners understand existing health disparities and provide an understanding of the baseline population health status of a population that may be effected by a change in a transportation-related exposure.

A handful of more specialized population health surveys contain data that may be useful for some transportation-health interactions. The Panel Study of Income Dynamics (PSID) (opens new window) is the longest-running household survey in the world (since 1968). PSID tracks information on expenditures on vehicles, fuel, transit, and health care, as well as ancillary information about health and disease status. PSID frequently includes special add-on questions. PSID’s health metrics have been found to align well with the NHIS over time. NIH’s “Add Health” study (opens new window) (National Longitudinal Study of Adolescent to Adult Health) is a longitudinal, national representative sample of adolescents in the United States designed to track the emergence of chronic disease into middle age, and includes associated social, environmental, behavioral, and biological data. The California Health Interview Survey (NHIS) (opens new window) is the largest state-run health survey in the nation, focused on the state of California.

Vital Records

Detailed mortality and birth data in the United States are collected via the National Vital Statistics System, administered via the National Center for Health Statistics. Detailed mortality records contain cause of death data stratified by demographic characteristics and, as practicable while maintaining privacy, geographic region. These data are used to calculate birth and death rates using standardized populations developed by the National Center for Health Statistics, in collaboration with the National Cancer Institute and the Census Bureau. Additional information on the National Vital Statistics System can be found at https://www.cdc.gov/nchs/nvss/about_nvss.htm (opens new window).

Infection Surveillance

Infectious diseases can spread through transportation networks (e.g., spread of influenza on aircraft flights). The CDC provides the Surveillance Resource Center (opens new window) for an easy-access way to find data and methods for health studies. The National Notifiable Disease Surveillance System (NNDSS) (opens new window) is managed by CDC for a network of local, state, territorial, federal, and international agencies to monitor and prevent notifiable infectious and non-infectious diseases . For United States, CDC provides weekly surveillance data for influenza through its FluView interactive web site (https://www.cdc.gov/flu/weekly/fluviewinteractive.htm (opens new window)). Internationally, the World Health Organization provides information on infectious diseases (http://www.who.int/csr/don/en/ (opens new window)).

Traffic Fatality Records

Several databases capture fatal traffic crashes with varying levels of detail. The FARS database, maintained by NHTSA, records the location of every fatal crash involving a motor vehicle traveling on a public roadway in the United States on a yearly basis. Motorist, passenger, pedestrians and cyclist fatalities are included. Each crash record contains additional supplemental information on vehicle, roadway, driver, and victim characteristics. These data can be used to identify high-risk segments of the transportation system, evaluate safety countermeasures, and explore the relationships between traffic fatality risks and other variables. While FARS contains a complete record of crash fatalities, it does not contain complete records of injury crashes or near misses. Thus, in areas where bicycle and pedestrian fatalities are relatively rare, the FARS database may have limited statistical power in identifying high-risk locations or evaluating safety countermeasures. FARS data are published yearly and typically in the latter portion of the year after the year of the dataset (e.g., FARS 2016 data were released in late September of 2017). Thus, some fatalities may not be included until nearly two after the incident. Several states have begun report all motor vehicle crashes to NHTSA into the FARS process shortly after an incident occurs via the Electronic Data Transfer (EDT) pilot program. In addition to FARS and EDT, the Highway Safety Information System (HSIS) contains detailed records for all motor vehicle crashes in seven states (California, Illinois, Maine, Minnesota, North Carolina, Ohio, and Washington). Each HSIS record is geo-coded and contains crash, roadway, and traffic volume data. States may also include roadway curve/grade, intersection, and interchange data as appropriate. More details on the FARS database are available at https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars (opens new window) and more information on the EDT pilot program can be found at https://www.transportation.gov/government/traffic-records/nhtsa-electronic-data-transfer-pilot (opens new window). More information on HSIS can be found at https://www.hsisinfo.org/background.cfm (opens new window).

Epidemiological Studies

Epidemiological studies provide evidence linking transportation-related exposures to health outcomes. While some transportation health outcomes, such as fatal crashes, are acute, other transportation health impacts are the result of chronic exposure to a stressor. Thus, it can be difficult to attribute specific health problem or mortality risk to a transportation-related exposure. Several types of epidemiology studies are common, including case-control studies, cohort studies, panel studies, and case-crossover studies. Cohort studies follow individuals with varying exposure and estimate the relationship between observed health outcomes in the cohort and observed exposures, controlling for other factors. Panel studies are a longitudinal study where the people are repeatedly followed at intervals over time. Case-control studies use a case as her own control and can be used to compare exposures during a “time window” the onset of an acute health problem (e.g., heart attack or stroke) to a “control window” of time. The resulting evidence links a specific exposure dose to increased (or decreased) risk of a specific health outcome, such as all-cause premature mortality or incidence of lung cancer. This evidence plays a central role in estimating the health impacts of changes in transportation-related exposures.

Systematic Reviews and Meta-Analyses

Rather than relying on the findings from a single study or type of study, health researchers often review all published systematic reviews or meta-analyses, both of which seek to synthesize findings from various studies to better estimate the true effect of exposure on a health outcome. A recent systematic review and meta-analysis of evidence linking transportation physical activity and all-cause mortality conducted to support the World Health Organization’s Health Economic Assessment Tool (HEAT) can be found at https://doi.org/10.1186/s12966-014-0132-x (opens new window). For ambient air pollution, The EPA’s Integrated Science Assessments (ISA) consider all published evidence linking exposures to criteria air to health outcomes. In 2010, the Health Effects Institute published a report about traffic-related air pollution’s impacts on health (https://www.healtheffects.org/publication/traffic-related-air-pollution-critical-review-literature-emissions-exposure-and-health (opens new window)).

SECTION 2: COMMONLY USED TOOLS

Tools and models to estimate the health impacts of transportation systems are evolving rapidly. The simplest tools include data warehouses that catalogue transportation and health data sources to empower qualitative assessments of transportation health impacts. More specialized tools and models, such as air quality modes and travel demand models, focus on transportation-related exposures that impact public health. Finally, advanced health impact assessment tools draw on quantitative risk assessment techniques to integrate large amounts of data to quantitatively estimate changes in population health outcomes given changes in the built environment and/or transportation system. These tools and models can be applied at various stages of health impact assessment, a six-step process that identifies and estimates the health impacts of a policy decision, such as investments in transportation infrastructure. Several commonly used tools are discussed below, ranging from simple data warehousing tools to complex modeling platforms.

Data Warehousing Tools

Smart Location Database

EPA’s Smart Location Mapping website (opens new window) includes numerous datasets associated with the built environment. The Smart Location Database summarizes more than 90 indicators associated with location efficiency, including density of development, diversity of land use, street network design, and accessibility to destinations. The Access to Jobs and Workers via Transit Tool provides indicators of accessibility to destination by public transit. The National Walkability Index provides walkability scores for census block groups across the entire U.S.

Transportation Health Tool (THT)

The THT, developed in partnership by USDOT and the Centers for Disease Control and Prevention (CDC), catalogues health-related transportation indicators from a variety of data sources in the United States. These data are available at the state, urbanized area, and metropolitan statistical area geographies and are normalized across all geographies to enable peer comparisons for each indicator. These data can be used to facilitate discussions, frame planning processes, and engage both transportation and public health practitioners and stakeholders when making transportation decisions. The THT can be accessed at https://www.transportation.gov/transportation-health-tool (opens new window).

Health and Transportation (H+T) Affordability Index

The H+T Affordability Index, developed by the Center for Neighborhood Technology, is a compendium of transportation and health data sets mapped across the United States, supplemented with modeled estimates of health-relevant transportation measures, such as per capita VMT and greenhouse gas emissions. The data can be viewed online and can be downloaded at the Census block group geography. More information on the H+T Affordability Index can be found at http://htaindex.cnt.org/ (opens new window).

CDC WONDER

The Centers for Disease Control and Prevention (CDC) Wide-ranging OnLine Data for Epidemiologic Research (WONDER) web application provides easy access to many public health datasets in the United States, including detailed birth and mortality data, cancer incidence, and disease prevalence. These data are typically updated yearly and may be disaggregated by various demographic characteristics and by geographies as small as the county; however, data disaggregation is restricted to protect privacy. The WONDER web application allows users to perform custom queries at https://wonder.cdc.gov (opens new window).

Specialized Models

Travel Demand Models

Travel demand models are routinely used by transportation practitioners and support a wide range of decisions made by MPOs. The outputs of travel demand models support estimates of air quality and active transportation behaviors. Traditional four-step travel demand models first generate trips, then distribute these trips between transportation analysis zones (TAZs), then use a model to predict the mode choice for trips, and finally assign trips to the transportation network. These models are typically calibrated using locally-administered household travel surveys. Because four-step travel demand models only estimate trips between TAZs, which may have large spatial extents, they may systematically underestimate short trips that are more likely to be made by active modes, such as walking. However, emerging activity-based travel demand models offer higher-resolution estimates of trip-making, including more detailed estimates of walking and biking trips. An introduction to activity-based models can be found at https://doi.org/10.17226/22357 (opens new window).

Air Quality Models

Air quality models offer higher-resolution estimates of the spatial distribution of pollutes in ambient air than the ambient monitoring network. Two types of air quality models are particularly useful for transportation and public health practitioners: dispersion models and grid models. Dispersion models estimate the dispersion of pollutants emitted from a point or along a line (e.g., a roadway); however, these models only estimate the concertation of primary pollutants emitted from these sources (e.g. particulate matter). Concentrations of secondary pollutants such as ozone, which are formed through chemical reactions in the atmosphere, are not estimated by dispersion models. Grid models divide the atmosphere into nested grid cells and estimate the pollutant concentrations within the smallest nested grids accounting for all sources (i.e., point, line, and area-wide emissions) and photochemistry. Thus, grid models can estimate concentrations of both primary and secondary pollutants. However, the complexity of chemical fate and transport models limits their ability to estimate air quality concentrations at high spatial resolution: dispersion models can be used to estimate concentrations at small scales, such as the centroids of Census blocks, while grid models typically estimate uniform concentrations within 4km grids or larger. While both line-source and photochemical air quality models are computationally intensive and require substantial expertise to execute, results of air quality model runs may be available for use in estimating the health impacts of transportation systems as they are often used to demonstrate conformity with air quality standards. A database of empirical air quality models with publicly available data can be found at http://spatialmodel.com/concentrations/ (opens new window).

A number of dispersion and grid models are available for use if custom ambient air quality estimates are needed. Commonly used dispersion models include EPA’s AERMOD (opens new window), the ADMS model (opens new window), and R-LINE (opens new window). The EPA provides guidance on the application of dispersion model for “hot-spot” analysis (https://www.epa.gov/state-and-local-transportation/project-level-conformity-and-hot-spot-analyses (opens new window)). Commonly used grid models include the Community Multiscale Air Quality Model (CMAQ) are used for photochemical modeling (https://www.cmascenter.org/ (opens new window)) and CAMx (opens new window). When using an air quality model, many data inputs, including meteorology data ad emissions data, are required. For motor vehicle traffic, EPA’s MOVES emission model (opens new window) is suitable for modeling emissions at a variety of scales, from county-level to individual road segments.

(Road) Traffic Noise Models

To analyze the impact of noise, one has to obtain data on sound levels. One possible approach to retrieve the data is to measure noise physically. As a more flexible and less expensive alternative, noise can be modeled by using traffic noise models, of which many have been developed over the years. The first models go back into the 1950s and modeled the 50th percentile of traffic noise based on distances and traffic volume. Later models also included the mean speed of vehicles and the share of heavy vehicles. Reviews and comparisons of official traffic noise models are given by Quartieri et al. (2009) (opens new window), Steele (2001) (opens new window), de Lisle (2016) (opens new window) and Garg and Maji (2014) (opens new window).

Traffic Noise Model	Offical model or most commonly applied in	Reference
MPB-Routes 2008 (Nouvelle Methode de Prevision de Bruit)	France	Dutilleux et al., 2010 (opens new window)
CNR (Consiglio Nazionale delle Ricerche)	Italy	Cannelli et al., 1983 (opens new window)
CoRTN procedure (Calculation of Road Traffic Noise)	United Kingdom, Australia, Hong Kong, New Zealand	United Kingdom Department of the Environment,1988 (opens new window)
RLS-19 (Richtlinie Laermschutz an Strassen)	Germany	FGSV, 2019 (opens new window)
FHWA TNM (Federal Highway Administration Traffic Noise Model)	United States, Canada, Mexico	US Federal Highway Administration, 2004) (opens new window)
ASJ RTN Model (Acoustical Society of Japan Road Traffic Noise)	Japan	Sakamoto, 2015 (opens new window)
Son Road	Switzerland	Heutschi, 2004 (opens new window)
Nord 2000	Norway, Denmark, Sweden, Finland	Jonasson and Storeheier, 2001 (opens new window)
CNOSSOS-EU	European Union	Kephalopoulos et al., 2012 (opens new window)

An overview of official traffic noise models and their main location of application is given in table 2.2. Even though official noise prediction models have different formulations for noise prediction, Quartieri et al. (2009) (opens new window) show that the resulting noise levels are similar. In principle, most models rely on empirically estimated equations that usually postulate a logarithmic functional relationship between traffic volume and noise. The estimations may be subject to ’site bias’, leading to errors in the predictions at different sites (Guarnaccia et al., 2011) (opens new window). A special case is the CNOSSOS-EU model, as it aims to harmonize the different national prediction methods in the European Union. The goal is to make results comparable and to allow for common standards for strategic noise mapping.

Health Economic Assessment Tool (HEAT)

The HEAT tool, developed by the World Health Organization, provides a straightforward web-based platform to estimate the health and economic benefits of investments that support increased walking and biking for transportation. When a user enters estimates of the number of people expected to increase their walking or biking time by a certain amount, the tool estimates the economic value of avoided premature mortality due to increased physical activity. Additional information on the HEAT model can be found at http://www.heatwalkingcycling.org/ (opens new window).

Environmental Benefits Mapping and Analysis Program – Community Edition (BenMAP-CE)

BenMAP-CE is an open-source computer program that calculates the number and economic value of air pollution-related deaths and illnesses. The software incorporates concentration-response relationships, population files, and health and economic data needed to quantify these impacts. Default datasets include information for both the U.S. and China. BenMAP-CE, along with training materials and examples can be found at https://www.epa.gov/benmap (opens new window).

The Intervention Model for Air Pollution (InMAP)

Similar to BeMAP, InMAP is a reduced complexity air quality model that estimates health impacts, economic damages from exposure to fine particulate matter based on input emissions data. InMAP uses a high-resolution air quality model to support neighborhood-scale impact assessments and environmental justice analyses. More information on InMAP can be found at http://spatialmodel.com/inmap/ (opens new window).

Propensity to Cycle Tool

The Propensity to Cycle Tool is an online, interactive planning support tool that estimates cycling potential at small scales and along routes across England. The tool is designed to assist transportation planners identify where cycling is currently common and where there is the greatest potential for growth in cycling. In addition to estimating additional cycle trips, the tool also estimates health and CO2 emissions reductions. While developed for the United Kingdom, the tool is open source can be modified for other contexts. More information about the tool can be found at http://www.pct.bike/ (opens new window). The source code for the tool is also available at https://github.com/npct (opens new window).

Integrated Transportation Health Impact Model (ITHIM)

ITHIM is an integrated transportation and health impact modeling tool that can be applied to estimate the health effects of transportation scenarios and policies at the urban and national level. The health effects of transport policies are modelled through the changes in physical activity, road traffic injury risk, and exposure to fine particulate matter (PM2.5) air pollution. Some versions of ITHIM also predict changes in CO2 emissions. ITHIM is being used in research and by health and transportation practitioners to estimate the health impacts of scenarios, compare the impact of travel patterns in different places and model the impact of interventions. ITHIM works either as a stand-alone model, or it can be linked with other models (e.g. transport, health, economic). More information on ITHIM can be found at http://www.mrc-epid.cam.ac.uk/research/research-areas/public-health-modelling/ithim/ (opens new window). Source code for ITHIM is available at https://github.com/ITHIM (opens new window).

SECTION 3: EMERGING TRENDS

Built Environment and Travel Behavior

Data Gaps

Pedestrian and Bicycle Infrastructure

The availability of sidewalk and bike infrastructure data is limited, and data that do exist may have been collected using different tools, making comparisons across datasets difficult.

Emerging Resources

Passively collected data

Walking and biking data are relatively limited and often rely on self-report methods, such as travel diaries and/or surveys. Physical activity trackers, such as accelerometers, have been used to collect more detailed physical activity data. However, these collection methods are expensive to deploy and may be effected by selection bias. Passive collection of walking and biking data is increasingly feasible with cell phone-based apps, potentially allowing researchers to recruit large study populations at relatively low cost. Cell phone-based physical activity tracking apps can use both GPS and accelerometer technologies to improve the accuracy of activity tracking, without requiring the user to specify the beginning and end of an activity. Further, these apps can periodically prompt the user to validate activities after they occur and utilize techniques such as machine learning to improve the accuracy of activity tracking for difficult to identify activities, such as biking.

In addition to measuring physical activity, cell phones and other mobile sensor networks can better capture transportation system characteristics that may impact public health. Several commercial vendors, including INRIX and AirSage, offer rich datasets capturing population movements, congestion levels, etc. Crowdsourced congestion and incident data, such as Waze, offer transportation researchers and practitioners near real-time data relevant to traffic safety. Waze provides access to portions of their data feed via the Connected Citizens Program: https://support.google.com/waze/partners/CCP/?hl=en#topic=6324400 (opens new window).

Cell phone-based travel surveys

While household travel surveys have historically relied on paper-and-pencil travel diaries, newer surveys may be supplemented with data collection technologies such as cell phone apps. While these new collection methods present substantial privacy concerns, detailed route-level travel behavior information could provide significant improvements in the understanding of walking and biking behaviors and cumulative exposure to transportation air pollution and noise.

Research Directions

The built environment and travel behavior

While a large number of studies have demonstrated relationships between characteristics of the built environment and travel behaviors, residential self-selection complicates the interpretation of study findings. A handful of studies have employed methodological techniques, such as surveys to reveal underlying preferences of study participants and/or follow-up with participants who change residential location over time, to build stronger causal evidence. The true causal effect of built environment characteristics on travel behaviors remains uncertain. Because these relationships provide foundational evidence for conducting health impact assessments of changes in the built environment on population health, stronger understanding of how the built environment shapes travel behavior, taking into account effects such as residential self-selection, is a critical future research direction.

Health Impact Modeling

Data Gaps

Measurement of competing transportation-related exposures

Exposure pathways in urban areas are complex and may compete within one another—for example, walking in an urban area may increase physical activity levels but also increase inhalation of pollutants in ambient air. Thus, changes in the transportation system may result in trade-offs between competing risks. Simultaneous measurements of physical activity and air pollution exposure for the same individual are very rare. Supplementing objectively measured physical activity with objectively measured air pollution exposure would provide researchers a rich dataset to better understand the nature of these risk tradeoffs in urban areas.

Noise barrier infrastructure effects on other exposure pathways

While state DOTs often have inventories for noise barriers, there currently is no common database that can be used to estimate how noise barriers might affect exposures to noise and air pollution.

Different Vocabularies

Databases for different health outcomes may not be comparable, due to different vocabularies and statistical metrics. For example, are deaths from the NHTSA FARS and CDC WONDER comparable and tracked well? What about injury data? Statistical methods for injury studies may differ from epidemiology and biostatistics studies.

Small geography measures of population health

The availability of many population health characteristics, such as the prevalence of cardiovascular disease, is limited for small geographies. Improved characterization of population health at small geographies would help support transportation health impacts assessments. While privacy concerns may limit the possible geographic resolution of such data, it may be possible to employ statistical techniques to estimate population health measures at small geographies while maintaining individual privacy.

Emerging Resources

Biomarkers

Objectively measured biomarkers, such as levels of certain chemicals in the blood, may provide a deeper understanding of how transportation-related exposures are related to health outcomes. Biomarkers can help identify health impacts associated with exposures prior to mortality or the diagnosis of a disease in an individual, providing a more sensitive instrument to characterize health impacts. Additionally, biomarkers may help researchers better understand how health effects “ramp up” in the population following a change in exposure—for example, how long an increase in physical activity must be sustained for the full health benefit to be seen in an individual.

Exposure Mitigation

EPA has published Best Practices for Reducing Near-Road Air Pollution Exposures at Schools (opens new window). For new schools, EPA has published voluntary School Siting Guidelines (opens new window).

Research Directions

The built environment and measured health outcomes

While studies have demonstrated links between the built environment and travel behaviors and air pollution exposure as well as transportation physical activity, air pollution exposure, and health outcomes, evidence linking the built environment to health outcomes directly is limited. While these relationships can be modeled, observed evidence of improved health outcomes linked directly to built environment measures would substantially strengthen the evidence linking transportation systems and population health.

Retrospective evaluation of transportation projects and built environment interventions

A number of studies have looked at whether regulations have reduced health risks from air pollution. The “iron law of megaprojects” documented by Bent Flyvbjerg, “over budget, over time, over and over again,” suggests a need for retrospective evaluation of transportation projects and built environment interventions as well, and understanding their impacts on health and safety (https://arxiv.org/ftp/arxiv/papers/1409/1409.0003.pdf (opens new window)).