U.S. Department of Transportation
Federal Highway Administration
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The Federal Highway Administration (FHWA) and other federal agencies routinely work with state and local agencies to improve the safety and connectivity of bicycling and walking networks. Part of this effort has been to promote a data-driven approach to identifying and mitigating safety problems.
Pedestrian and bicyclist exposure to risk is often mentioned as a missing piece of the puzzle. The lack of readily-available pedestrian and bicyclist exposure data often make it difficult to accurately identify and then prioritize high-crash (or high-risk) locations or interpret year-to-year trends in citywide, state, or national crash statistics. Even when pedestrian or bicyclist exposure data are used, inconsistency can be present in the formulation and calculation of exposure measures between regions. Exposure has been defined based on direct counts, population, hours of travel, miles of travel, and others. Having diverse measures and definitions make direct comparisons difficult, if not impossible.
Pedestrian and bicyclist exposure is also a critical element in better understanding pedestrian and bicyclist crash causation. For example, exposure is a key variable in developing pedestrian and bicyclist safety performance functions, which are used to identify those factors that best predict when and where crashes are likely to occur. If exposure is not included, then the effects of other factors may be biased or overstated due to the omission of a key variable (exposure).
In May 2016, the FHWA’s Office of Safety initiated this project to develop a standardized approach to estimate pedestrian and bicyclist exposure to risk in the form of a Scalable Risk Assessment Methodology (ScRAM). This approach will make it easier to assess pedestrian and bicyclist exposure to risk and inform project priorities and funding decisions, and will include several methods according to the scale of the needed exposure estimate.Â The project objectives are three-fold:
This report documents the findings of Task 3, which sought to review and synthesize the variety of methods used to estimate and evaluate exposure in pedestrian and bicyclist safety analyses (i.e., first objective in bullet list above). Later tasks in this project will develop and promote the risk and exposure assessment methods (i.e., second and third objectives in bullet list above).
Chapter 2 summarizes basic definitions and concepts for risk and exposure, and then discusses these terms in the context of pedestrian and bicyclist safety analysis. It is important to define these terms and related concepts in the early stages, such that subsequent development work in this project has a clear and unambiguous foundation.
In the literature, most authors agree on the theoretical definition of risk as a measure of the probability of a crash to occur given exposure to potential crash events. The relationship between risk and exposure is implied in some definitions, but more explicit in others; exposure is a normalization factor (i.e., denominator) to equalize for differences in the quantity of potential crash events in different road environments. There is also general agreement in the literature on this relationship between risk and exposure:
Most theoretical definitions of exposure in the literature are similar, in that exposure is a measure of the number of potential opportunities for a crash to occur. However, there is wide divergence on operational definitions of exposure, and an even wider range of exposure measures being used in practice. For example, exposure measures in the literature have been quantified in terms of:
Several key factors may explain this wide divergence in exposure measures used:
Much similarity exists between how risk is conceptualized and estimated between the fields of public health and transportation safety. There are many examples in the literature of epidemiological studies that use similar methods for estimating time at-risk or some other metric needed to compute risk or rates. Differences between the two fields exist for terminology such as relative risk, which needs to be considered when interpreting and comparing findings across the two fields.
Many articles in the literature emphasize the importance of geographic scale in estimating exposure. Different data sources and methods are available or feasible at different geographic scales, and this is likely to explain some of the wide divergence in the exposure measures used in pedestrian and bicyclist safety analyses. In this report, exposure scale is defined as the geographic level for which an exposure measure is desired. The literature review indicated that most exposure analyses could be grouped into one of these four scales (see Figures ES-1 and ES-2):
Future methodological development in this project could benefit from the use of clear, unambiguous terms for various scales. For example, the Highway Capacity Manual is widely used for street and highway analysis and provides clearly-defined terms for various roadway system elements, such as points, segments, facilities, corridors, areas, and systems.Â Similarly, the United States Census Bureau has defined several different area geographies, including Census tracts, block groups, and blocks. Also, some metropolitan planning organizations define traffic analysis zones for use in their travel demand models. Traffic analysis zones are typically composed of multiple Census blocks, but sometimes deviate from Census geography units to accommodate local conditions.
Most theoretical definitions of pedestrian and bicyclist exposure include references to contact with harmful vehicular traffic or opportunities for a pedestrian or bicyclist crash. For pedestrians, this occurs most explicitly during a street crossing. But several authors in the literature have posed a series of questions about when and where pedestrians or bicyclists can be considered exposed. For example, are pedestrians exposed while walking along a sidewalk that is separated from motor vehicle traffic? Are bicyclists exposed when they travel in a bike lane immediately adjacent to a motor vehicle travel lane, but then not exposed if they are riding in a separated bikeway?
In most cases, the feasibility and practicality of data collection has been used to operationalize the theoretical definition of exposure. Data cannot be collected on all pedestrian, bicyclist, and motor vehicle movements at all locations at all times. Therefore, most operational definitions of exposure have been based on pedestrian and bicyclist activity data that are already available (e.g., from travel surveys) or can be feasibly measured or estimated (e.g., from direct counts or models).
Chapter 3 summarizes many examples of exposure analyses that were conducted at areawide levels. In this report, areawide is a generic term that includes all geographic scales that are not facility-specific. The term areawide in this chapter includes geographic scales (some not explicitly defined) such as networks, systems, regional, city, state, etc.
The areawide exposure analyses in the literature were most often performed to quantify big picture trends in pedestrian and bicyclist safety. For example (see Chapter 3 for more details):
Most of the exposure analyses at areawide levels have used travel survey data from one or more of these sources:
Most methodologies for areawide exposure analyses fall into the category of sketch planning, which is defined as methods to estimate existing or future demand that are simpler alternatives to developing complex travel demand models.Â Often, sketch planning methods are implemented in spreadsheets or geographic information systems using existing travel survey and other data. In some cases, the results from multiple surveys are combined to provide a more complete picture of pedestrian and bicyclist activity. In a few analyses, ACS data (which includes only primary journey-to-work trips) was combined with NHTS data (which includes all trips) to provide an accounting of all pedestrian and bicyclist trips. Also, locally collected survey data (from either regional household travel surveys or other localized data collection) has been used to supplement the federally collected ACS and NHTS data. For example, Chapter 3 summarizes an analysis for the Nonmotorized Transportation Pilot Program that uses spreadsheet-based calculations to combine ACS, NHTS, and locally collected count data to derive areawide exposure estimates. Chapter 3 contains several other similar examples whereby data from one or more travel surveys were used to develop areawide exposure estimates.
The units used in areawide exposure measures varied widely. Since the primary data source for areawide exposure analyses were travel surveys, and most travel surveys gather data on specific trips, many analyses used the number of pedestrian and/or bicyclist trips. However, some analyses focused on only journey-to-work trips (directly from ACS data), whereas other analyses included total trips (from NHTS data or combining ACS and NHTS data). In other analyses, the pedestrian and bicyclist trips were converted to pedestrian and bicyclist miles of travel using estimated trip length data. A limited number of analyses estimated pedestrian and bicyclist hours of travel using the number of trips and estimated trip times.
The geographic scale of available travel surveys is an obvious limiting factor in the scale and choice of exposure measures. The most common types of travel survey data do not have facility-specific trip information (although emerging crowdsource methods may address this in five to ten years), so exposure measures are limited to the areawide geography defined in the travel survey data that are being used. Travel surveys also do not include any data about nonmotorized traffic interactions with motorized vehicles, such that certain exposure measures (such as the product of pedestrian or bicyclist volume and motorized vehicle volume, (P or B) × V) cannot easily be estimated. These limitations notwithstanding, travel survey data can be a useful input to areawide exposure analyses when big picture exposure trends are desired for more aggregate geography and time intervals.
Chapter 4 summarizes many examples of exposure analyses that were conducted on specific transportation facilities. In some cases, exposure estimates are calculated for specific facilities, but also aggregated to various areawide geographies.
The facility-specific exposure analyses were most often used to identify high-priority locations for pedestrian and bicyclist safety improvements and were typically conducted for an entire city. In some cases, the facility-specific information was also aggregated to provide overall trends for certain road types or for subareas within a city.
Most of the facility-specific exposure analyses used pedestrian and bicyclist count data from one or both of these sources:
For direct measurement of pedestrian and bicyclist counts, much progress has been made in the past ten years. Several companies now offer automated count equipment that helps to make pedestrian and bicyclist counting more efficient and cost-effective. The National Bicycle and Pedestrian Documentation Project provided early guidance and helped to promote count data collection. Since then, FHWA has included a chapter specifically devoted to nonmotorized traffic monitoring in their 2013 edition of the Traffic Monitoring Guide. In 2014, NCHRP Report 797: Guidebook on Pedestrian and Bicycle Volume Data Collection was published and is a comprehensive resource on pedestrian and bicycle count data collection. This direct measurement approach (calculating exposure from systematic traffic monitoring programs) is the current state-of-the-practice for motor vehicle exposure estimation.
For estimating pedestrian and bicyclist counts, direct demand models have been the most widely used models for facility-specific exposure estimation thus far, and typically use regression analysis to relate directly measured counts to other measured attributes of the adjacent environment (e.g., land use and form, street type, etc.). Assuming that these measured attributes are available citywide, the regression model allows one to extend the sample of facility-specific counts to all facilities citywide. Chapter 4 provides details on other types of modeling approaches that have been used on a limited basis or could be used for facility-specific exposure estimation. These approaches include regional travel demand models, geographic information system (GIS)-based models, trip generation and flow models, network analysis models, discrete choice models, and simulation-based traffic models.
Similar to the areawide exposure analyses, the units used in facility-specific exposure measures varied widely. Since the primary data source was pedestrian and bicyclist count data (rather than surveyed trip data in areawide exposure analyses), the units of exposure typically were a volume count for specified time period or a distance traveled (calculated by multiplying a count by a street crossing width or road segment length). In a few cases where the exposure values were very high, the exposure was given in units of 1 million or 10 million (e.g., 1 million pedestrian miles traveled, 10 million pedestrian crossings).
Unlike areawide exposure analyses, several of the facility-specific exposure analyses did account for the interaction of nonmotorized and motorized traffic in their exposure measure. Several analyses computed the product of pedestrian or bicyclist traffic and motorized traffic ((P or B) × V) at intersections or other street crossings. A few other analyses used exposure measures such as pedestrian crossings (or pedestrian miles) per entering motorized vehicle. Thus, having more granular exposure data on specific facilities does provide a better opportunity to quantify the level of interaction between pedestrian or bicyclist traffic and motorized vehicle traffic.
Chapter 5 summarizes many risk factors (other than exposure) for pedestrian and bicyclist safety. Findings from most of these studies indicate association, not causation, between potential risk factors and crash outcomes. The risk factors were categorized into two basic groups:
For disaggregate risk factors, many studies have shown that poor facility conditions (e.g., no or inadequately designed pedestrian and bicyclist infrastructure) are a significant risk factor, as well as facilities where high-speed, high-volume motorized traffic routinely interacts with pedestrian and bicyclist traffic. Significant risk factors for individuals are age (i.e., children and seniors), intoxication, nighttime visibility, and distracted behavior.
For aggregate risk factors, many studies have documented the effects of land use (in particular, population density) on pedestrian and bicyclist safety. Several studies have also found that neighborhoods with lower-income and minority communities have a higher risk for pedestrian and bicyclist crashes.
The project team reviewed and synthesized over 280 research and technical documents (see Bibliography) on pedestrian and bicyclist risk and exposure and developed these key conclusions:
Geographic scale is a key parameter in exposure analyses: Detailed exposure data cannot be collected on all pedestrian, bicyclist, and motor vehicle movements at all locations at all times. Therefore, the scale(s) for which exposure is required will determine what data source and methods are practical and feasible. Future methodological development in this project could benefit from the use of clear, unambiguous terms for various scales. To encourage widespread consistency and adoption by practitioners, existing terms and definitions should be drawn from widely used manuals, guidebooks, or references. The U.S. Census provides standardized terms and definitions for several different areawide geography scales. Similarly, the Highway Capacity Manual provides terms and definitions for various scales of roadway system elements.
Areawide exposure measures are inconsistent despite similar travel survey data: The units used in areawide exposure measures varied widely, despite many analyses using the same two national travel surveys (i.e., ACS and NHTS) as their base data source. If areawide exposure measures use the same base travel survey data, one might expect an emerging consensus on the best approach for using the same or similar trip data to calculate areawide exposure measures. The number of pedestrian and bicyclist trips was a common exposure measure, but even with this measure, some analyses reported only on work trips whereas some reported on all trips. Even if consensus on a single areawide exposure measure cannot be achieved, future methodological development in this project should focus on identifying a few good measures that are designated as a best practice for estimating areawide exposure.
Facility-specific exposure analyses often use counts in combination with models: One of the most common approaches to estimate facility-specific exposure has been to combine pedestrian and bicyclist counts with estimation models, such that exposure can be estimated for all facilities within a defined geographic area (typically citywide). Given the wide variety of estimation models in use, it may be difficult to single out a single best practice for future methodological development in this project. This project could focus on providing additional guidance on the most common estimation model (i.e., direct demand model), while still acknowledging and providing high-level details on other estimation model approaches.
Similar to areawide exposure measures, the units used in facility-specific exposure measures varied widely. This is also despite the fact that direct measurement and estimation models both produce the same basic data item: counts of pedestrians and/or bicyclists at a point or along a street segment for a defined time interval. As with areawide exposure measures, it may be difficult to achieve consensus on a single facility-specific exposure measure. However, there would be value in defining a few good measures that are designated as a best practice for estimating facility-specific exposure.
Based on the findings and conclusions in this Task 3 report, the TTI-led project team will develop a conceptual framework and design for risk exposure estimation at several different geographic scales (Task 4.A. of this project). The conceptual framework will be based on best practices as identified in this report, as well as other practices and processes that may be in development (such as those from NCHRP 17-73, Systemic Pedestrian Safety Analyses). The first draft of the conceptual framework will be available for review in May 2017.
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