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Synthesis of Methods for Estimating Pedestrian and Bicyclist Exposure to Risk at Areawide Levels and on Specific Transportation Facilities

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Chapter 3. Exposure Analysis at Areawide Levels

Pedestrian and bicyclist exposure to risk can be estimated and analyzed at numerous geographic scales. This chapter summarizes examples of exposure analysis conducted at areawide levels. In this chapter, areawide is a generic term that includes all geographic scales that are not facility-specific. The term areawide in this chapter includes several area scales, such as networks, neighborhoods, systems, regional, city, and state.

Exposure at areawide levels is usually measured or estimated at a macro-level. This is different than the facility-based exposure for an entire area, which is generally based on the micro-level point or segment data (e.g., pedestrian counts collected at intersections), but then aggregated to the wider area. This chapter focuses on areawide exposure analyses used in pedestrian and bicyclist safety analysis.

Summary of Practice

Table 1 provides examples of exposure analysis at areawide levels with the following information:

As can be seen from Table 1, studies varied in terms of their scales of exposure estimation, geographic coverage, data and methodologies as well as the units of exposure. While all studies were conducted at an aggregate level, some focused on a national level and some others performed their analysis for specific regions or communities. If a smaller scale area-level is used for the analysis, it is possible to apply the end result to a wider scale enlarging the analysis coverage. For example, Salon (2016) used Census tracts (a smaller scale than city or region) for her analysis, but then converted the estimates to a neighborhood level. This approach provided relatively more accurate estimates.

All the studies listed in the table have been eventually used in pedestrian and bicyclist safety analysis (e.g., estimating crash and/or injury rates). This was achieved by developing a variety of exposure measures in a given area, which can be mainly categorized as follows:

Table 1. Examples of Exposure Analysis at Areawide Levels
Reference Coverage Development Scale Data Sources Methods Unit of Exposure
Chu 2003; Chu 2009 United States Country NHTS Sketch planning Population, number of hours traveled
Jacobson 2003 California cities, Danish towns, European counties, United Kingdom, Netherlands City, county, country Survey data - five  different data sets for different areas (three population level and two time series); U.S. Census Journey to Work data (for California cities) Sketch planning (including least squares analysis) Kilometers walked/bike, portion journey to work trips for walk and bike
Blaizot et al. 2013 Rhône County, France County Regional and national household travel surveys Sketch planning Number of trips, distance traveled, time spent traveling 
Rasmussen et al. 2013; Lyons et al. 2014 Columbia, MO; Marin County, CA; Minneapolis Area, MN; Sheboygan County, WI Metropolitan statistical area, community NHTS, ACS, annual count data for pedestrians and bicyclists Sketch planning Number of trips per mode, mode share, VMT
National Complete Streets Coalition 2014 United States Metropolitan areas NHTS, ACS Sketch planning Population, number or percent of people commuting by walk
Schneider et al. 2015 State of Wisconsin State ACS, State Intercensal estimates, and annual estimate of resident population, VMT Sketch planning Population, VMT, number of walk & bike commuters
Alluri et al. 2015 State of Florida State NHTS Sketch planning Population, number of walk trips
Retting and Rothenberg 2015 United States State, country U.S. Census Population Sketch planning Population
Salon 2016 State of California Census tracts NHTS, California household travel survey Sketch planning (including cluster analysis) Miles walked and biked
Alliance for Biking and Walking 2016 United States State, country ACS Sketch planning Population, number of walk & bike commuters
NACTO 2016 Multiple large US cities City ACS Sketch planning Number of cyclists
Guler and Grembek 2016 10 counties in California County Regional household travel surveys Sketch planning Total time traveled in million hours, total number of trips in millions

Greene-Roesel et al. (2007) and Molino et al. 2012 provided an extensive discussion on these exposure measures. As discussed by Greene-Roesel et al. (2007), choosing an appropriate measure depends on various factors including study purpose, methodology used, and location focused. For example, in his study, Chu (2003) suggested a time-based comparative approach to examine the fatality risk of walking. The results of this study indicated improvement when a time-based exposure measure is used instead of using only population-based measures. However, there were also limitations, such as the time-spent walking can be over-estimated in the surveys since “…people may perceive time spent on walking longer than it actually is…” (Chu 2003). A third factor to be considered is the availability of resources. While some measures might be more advantageous (or appropriate) to use than others, one might not have enough budget or time to collect or process more complex exposure estimates.

Guler and Grembek (2016) used two household travel surveys to estimate exposure by mode for ten counties in California in terms of: total time traveled in million hours and total number of trips in millions.  The authors concluded that a time-based metric is more capable of capturing the different travel risk characteristics of different modes by comparing to a trip-based metric for each of the ten counties.

Summary of Data Sources and Methods

Data Sources

The main source of information for areawide exposure analysis is survey data, which facilitate the development of a variety of exposure measures described above (e.g., population). Given that most surveys used are able to provide detailed information on the characteristics of individuals and their trips, these exposure measures are also often studied by different characteristics, such as demographics (age, gender, etc.). In addition to regional travel surveys, in the United States, the two commonly used datasets for areawide exposure analysis include NHTS and ACS (described below). This section also describes a survey conducted by the National Highway Traffic Safety Administration (NHTSA) that is primarily focused on pedestrian and bicyclist attitudes, but also includes trip information.

National Household Travel Survey

Being the nation’s inventory of daily travel, the NHTS is periodically conducted and provides detailed information to examine travel behavior of American public. The most recent published NHTS survey was conducted in 2009 and included 25,000 households representing all U.S. States and the District and Columbia (Santos et al. 2011). The data can be used to compute several different exposure measures (e.g. population, miles traveled) nationally or by major Census division. Additional add-on samples are also made available to the States and regional agencies for purchase. For example, “with the aim of supporting modeling efforts and examining travel behavior in Texas, the Texas Department of Transportation (TxDOT) purchased an additional sample of 20,000 households in Texas for the TxDOT NHTS Add-On Program in 2009 to go along with the sample of 2,255 households collected from the national survey” (Dai et al. 2014).These add-on samples provide the opportunity to populate different exposure measures at a finer geographic level and to develop more robust safety analysis, as also discussed in Edwards et al. (2012).

However, it is also important to recognize the limitations of the NHTS data that have been acknowledged by many researchers and practitioners in the field (see, for example Clifton and Krizek 2004; Sharp and Murakami 2005; Pucher et al. 2011; Salon and Handy 2014). While providing a rich national sample, the NHTS sample sizes might have sparse coverage at fine geographic scales particularly critical in the context of the current study. In their 2014 report, Salon and Handy (2014) emphasized the difficulty of estimating bicycle and pedestrian activity due to lack of data coverage and indicated that “at the geographic resolution of the census tract, there are more than 2500 tracts that are not sampled at all by the NHTS, and only 15 of the sampled tracts include more than 30 household observations”. According to the feedback received by the task force on Understanding New Directions for the National Household Travel Survey Transportation Research Board (TRB 2013), “the number of bicycle trips recorded is so low as to preclude almost any analysis”. It was noted that several users requested a more representative sample of this special population to develop statistically reliable analyses. This and several other limitations have been discussed by the expert panel for the upcoming NHTS, which could help improve the current data issues (NHTS 2015, TRB 2016).

American Community Survey

While geographically covering the entire states, the ACS data differ from the NHTS as it provides information only pertaining to primary work commute trips (and for individuals aged 16+). ACS data are the replacement of the U.S. Census Journey to Work data, with improved sample design and data collection frequency. The ACS is an ongoing survey process, and the estimates are released each year based on the aggregated responses in 1-year, 3-years, or 5-years estimates. While the yearly estimates are available for areas of at least 65,000 people, the 3-year estimates are available for areas of at least 20,000. Starting with 2010, the 5-year estimates have begun to be available for all areas at smaller geographic scales including census tract and block group levels (the latter is the smallest geographic level available). As also demonstrated in McKenzie (2014), the ACS survey data provide several measures that can be used in an areawide exposure analysis for pedestrian and bicyclist safety, such as walking and bicycling commuting rates across cities in the nation.

The ACS provides an easy access to a regularly collected and nationally comparable measure of walking and bicycling data, but it is not without limitations as acknowledged by various earlier studies in the literature. As noted earlier, ACS data do not include all trips but instead it covers only work-related trips. This might become particularly problematic for exposure studies conducted in areas where non-motorized work trips do not provide a representative sample of overall biking and walking behavior. Another important limitation is related to the sampling such that ACS data might have high margin of error in areas where the number of surveys is low. Spielman et al. (2014) indicated that “the margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate”, while the initial expectations were 33 percent (Navarro 2012). This loss of precision led to significant difficulties in using the data (Spielman et al. 2014), which is likely be more of a concern for bicycle and pedestrian studies. Although aggregation of the data helps improve margin of error due to larger sample sizes, this approach may not be practical especially for local studies with smaller geographic areas.

While the uncertainty in ACS data has been frequently acknowledged, there has not been much effort to develop methodologies to overcome this limitation. Spielman and Folch (2014) developed “a spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization”. Spielman and Singleton (2015) suggested a “shift from a variable-based mode of inquiry to one that emphasizes a composite multivariate picture of census tracts”, and applied this concept through a geodemographic typology across all census tracts in the United States. Bradley et al. (2016) developed a Bayesian approach to combine various surveys (including ACS) to “account for different margins of error and leverage dependencies to produce estimates of every variable considered at every spatial location and every time point”. Wei et al. (2016) developed a classification method “to explicitly integrate errors of estimation in the assessment of within-class variation and the associated groupings”. Future research will greatly benefit to develop best practices and guidelines in accounting for large margin of error problems for smaller areas.

NHTSA Attitudes and Behavior Survey

Over the last fifteen years, NHTSA has administered two surveys that have been focused on better understanding bicyclist and pedestrian attitudes and behaviors. While providing valuable insights, these surveys have not been utilized for exposure analysis.  The 2002 National Survey of Bicyclist and Pedestrian Attitudes and Behaviors included a representative sample of 9,616 U.S. residents aged 16 years or older, and it was conducted between June 11 and August 20, 2002 (Royal and Miller-Steiger, 2008). For the 2012 National Survey, a total of 7,509 interviews were conducted with residents aged 16 years or older between July 12 and November 18, 2012 (Schroeder and Wilbur, 2013a).

In general, both surveys included various questions related to the perceptions, attitudes and behaviors of bicyclists and pedestrians, and knowledge of infrastructure as well as the laws related to bicyclists and pedestrians.  In addition, the surveys compiled several questions on bicycle and pedestrian trips focusing on the most recent date respondents bicycled and walked within the last 30 days (and over the summer months due to the timing of the survey). The 2012 survey also assessed the changes in bicycling and walking behavior and attitudes since 2002 (Schroeder and Wilbur, 2013b). Both surveys used a similar questionnaire for consistency and comparability (Schroeder and Wilbur, 2013c), and aimed at developing a better understanding on the attitudes and behaviors of bicyclists and pedestrian activities.  However, “these data cannot be used to project year-round bicycling and walking behaviors” since it is generally a “reflection of biking and walking activity in the summer months” (Royal and Miller-Steiger, 2008).


Exposure analysis at areawide levels is usually conducted through different versions of sketch planning methods to estimate nonmotorized activity/travel in an area. In most cases, sketch planning methods include simple computations and rules of thumb for quick estimations of population and travel behavior. These methods mostly depend on the available data (such as nationally collected survey data) and require little effort in terms of data collection and no specialized expertise. While the results are mostly in the form of aggregate level estimates with relatively low accuracy, they lead to simple and practical solutions especially when the resources (time, budget, staff, data, etc.) are limited.

Aggregate demand models also fall into areawide sketch planning methods as they are used to explain areawide activity levels of walking and bicycling based on aggregate characteristics (e.g. population density, employment density, median household income, land use diversity, etc.). These models are very similar to the direct demand models (described in Chapter 4 in details), and typically use regression models to quantify the relationship between overall bicycling or walking demand and the significant factors influencing that demand at a large spatial level. See for instance Barnes and Krizek (2005) for a bicycle demand model at a metropolitan statistical area level, and Ann and Chen (2007) for a nonmotorized demand model at a census block group level.

As part of the Nonmotorized Transportation Pilot Program (NTPP), Rasmussen et al. (2013) developed and applied a community-wide assessment method to examine the travel behavior changes with improved walking and biking infrastructure.  As seen in Figure 4, their model was based on nationally collected data, including NHTS and ACS data, as well as annual counts for the community, and was used to estimate mode share changes and avoided VMT in the pilot communities.  The model results were also used to evaluate the safety implications by estimating the motorist-involved pedestrian and bicycle crash fatalities and reported injuries between 2002 and 2012 for pilot communities (Lyons et al. 2014).

Figure 4. This figure shows a calculation flowchart for the Nonmotorized Transportation Pilot Project. At the left, it shows input/baseline datasets from ACS and NHTS. In the middle portion of the figure, it shows year-to-year mode share adjustments using local count data. At the right, it shows final year 2011 output values.

(Source: Rasmussen et al. 2012)
Figure 4. NTPP Model Framework

Jacobson (2003) used five different datasets to compute the amount of walking and bicycling and explore the collisions involving a motorist and a bicyclist or a pedestrian. His exposure analysis included different areas, including 68 California cities, 47 Danish towns, 14 European counties, United Kingdom, and Netherlands based on the following exposure measures:

Using these exposure measures, he computed injury or fatality rates (based on the data available). For instance, for California cities, “injury incidence rates were calculated using the U.S. census population estimates as adjusted by the State of California’s Department of Finance for year 2000”.

The other studies exemplified in Table 1 also used regional or national surveys to estimate exposure measures at areawide levels using similar sketch planning tools. For example, Blaizot et al. 2013 used a regional household travel survey data as the primary data to estimate exposure measures including number of trips, distance traveled and time spent traveling. Since the data were collected between November 2005 and April 2006, during the analysis stage, the seasonality of the regional data were corrected using the NHTS that covered an entire year. The injury rates were estimated for different road users by “dividing the number of injuries by the exposure measurement, and scaled per 1 million trips, kilometers or hours”. These rates were also disaggregated by demographic characteristics (gender and age groups) and location characteristics (dense areas and non-dense areas).

In another study, Schneider et al. (2015) used simple computations to estimate pedestrian and bicycle crash rates in the state of Wisconsin (e.g. crashes per 100,000 people, crashes per 1,000 walk commuters, etc.) based on state level mileage and VMT data as well as national data, including US Census State Intercensal Estimates, Annual Estimates of the Resident Population and ACS. Similarly, Retting and Rothenberg (2015) used the U.S. Census population data as an exposure measure to compute pedestrian fatality rates (per 100,000 population) in all states of the United States. Alluri et al. (2015) used exposure measures of population (i.e. crashes per million) and number of walk trips (i.e. crashes per million walk trips) to compute crash rates (by age) in the state of Florida using the 2009 NHTS data. Using the 2001 NHTS data, Chu (2003) computed the nationwide fatality rates based on population (i.e. number of deaths per 100,000 population), and time spent walking (i.e. number of deaths per 10 million hours). Using the same NHTS dataset, Chu (2009) used time-based exposure measures for walking (time spent on access to or egress from another mode, time spent waiting for transit vehicles as exposure for walking) and computed the expected injury costs averaged over $2.00 per hour of exposure for walking and motoring.

In addition, in their analysis, National Complete Streets Coalition (2014) used both NHTS and ACS data to develop population-based exposure measures (e.g. number of commute walkers), which were used to evaluate pedestrian fatalities for various metropolitan areas.  Likewise, Salon (2016) also used both the 2009 NHTS and 2010–2012 regional household travel survey data as a primary source of exposure data in California.  Different than the previous studies described, she adopted relatively more advanced sketch planning methodology based on a small area estimation method as an improvement on per capita estimates at the regional or statewide levels. In her study, the production scale was census tracts, which were then used to develop cluster-based neighborhoods. She computed several exposure measures, including average walking and biking per different demographics (age and gender) and neighborhood types, and used category averages to estimate walking and biking by census tracts. These measures were then used to explore the crash rates at a more detailed geography.

Similar exposure measures have also been adapted in two recent reports. In their most recent benchmarking report, Alliance for Biking and Walking (2016) used ACS data and calculated pedestrian and bicyclist fatality rates for the states and the nation based on population-based exposure measures. These included fatality rates per million population (also categorized by age) and per 10,000 walking or bicycling commuters in the United States. The National Association of City Transportation Officials (NACTO) (2016) also used ACS data (i.e. number of cyclists) to compute risk of injury or death to cyclists. The exposure analysis was conducted citywide for seven cities, including Chicago, Minneapolis, New York City, Philadelphia, Portland, OR, San Francisco and Washington, D.C.

Summary and Conclusions

Exposure analysis at areawide levels is conducted at a larger scale (e.g. regional, statewide, or citywide) and adopts aggregate-level exposure measures (e.g. total number of people walking in a city). The choice of an appropriate exposure measure depends on various factors: study purpose, methodology, location of the analysis, and available resources. The main data source used for these types of analyses is the survey data. In addition to the local regional travel surveys, the NHTS and ACS have been used as key input for areawide exposure analysis. In pedestrian and bicyclist safety literature, areawide exposure analyses are mainly based on sketch planning method including aggregate demand models. These methods are composed of simple computations, do not require specific software or expertise, and are based on available data. Therefore, they provide practical and easy-to-apply solutions but with relatively low level of accuracy.

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