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Highway Safety Behavioral Strategies for Rural Areas (2023)

Chapter: Define Rural Area Roads (Task 1)

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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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Suggested Citation:"Define Rural Area Roads (Task 1)." National Academies of Sciences, Engineering, and Medicine. 2023. Highway Safety Behavioral Strategies for Rural Areas. Washington, DC: The National Academies Press. doi: 10.17226/27196.
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4 Define Rural Area Roads (Task 1) Identifying behavioral countermeasures that improve rural road safety required a consistent definition of rural areas and rural roads because crashes and other safety outcomes can differ significantly depending on where, and in what context, they occur. Therefore, the research team proposed a rural road safety classification system. The proposed system drew from a combination of past efforts to develop area and road categories, including an FHWA project completed in 2021 (Stephens et al. 2021). The system supported later tasks in this project by providing a lens through which to: analyze detailed crash data, locate jurisdictions that have experienced safety gains, and identify context-sensitive behavioral countermeasures. Key Considerations There were several things to consider when developing a rural road safety classification system. These included the level of spatial detail available for analysis, social and physical context, level of categorical detail, flexibility of the system when subject to change, and compatibility of the system with this research project. These considerations are discussed in more detail below. • Spatial Detail: This refers to the geographic units of analysis used in a classification system. A classification system needs to have enough spatial detail to permit analysis of safety data while not being so detailed that it becomes difficult to identify general trends. Also, too much spatial detail could limit practitioners’ ability to analyze safety outcomes if the data is too difficult or time- consuming to collect. • Social and Physical Context: Driver behavior, settlement patterns, local culture, and physical design influence road safety in any given area. A safety-oriented classification system that accurately represents local conditions—especially one specifically aimed at behavioral countermeasures—should consider both cultural and physical characteristics that influence safety risk. • Categorical Detail: This refers to the number of categories included in a classification system. Using too few categories would fail to generate meaningful differences among rural areas and roads. However, too many categories would make the analysis overly detailed and less useful for safety officials designing behavioral countermeasures for large segments of the rural population. As with spatial detail, too much categorical detail could limit the classification system’s usefulness among safety practitioners. • Flexibility: The classification system needed to enable separation of categories and geographic units of analysis when analyzing safety data. Thus, categories should be mutually exclusive. This allowed the research team to generate meaningful insights that support behavioral countermeasures targeting different combinations of rural areas and roads.

5 • Compatibility: The classification system should serve the specific needs of this project, which were to identify behavioral countermeasures that address specific rural safety issues. The classification system should therefore reflect important differences in behavior-related rural safety outcomes. For example, crashes involving speeding or seatbelt use may be more common in certain county types than others.1 Existing Area Classification Systems Rural America is a heterogenous combination of areas that covers 97% of the country’s land mass and has jurisdiction over 68% of its lane-miles (U.S. Census Bureau 2017, U.S. Department of Transportation [USDOT], 2020a). Because of this, an area classification system should include multiple rural categories. The research team built on prior research by reviewing several existing classification systems and borrowing the most relevant elements from each. Using the key considerations described above, they identified the rural area classification systems listed in Table 1 for in-depth review. Table 1. Summary of Rural Area Classification Systems Classification System Source Categories Metropolitan Statistical Areas (MSA) Office of Management and Budget Three categories based on population density and/or commuting patterns. Rural-Urban Continuum Codes (RUCC) U.S. Department of Agriculture Economic Research Service (ERS) Nine categories based on population and adjacency to a metropolitan area. Urban Influence Codes (UIC) U.S. Department of Agriculture ERS Twelve categories based on population, land area, and presence of towns. County Typology Codes U.S. Department of Agriculture ERS Six mutually exclusive categories based on economic dependence and six overlapping categories based on policy-relevant themes. The RUCC and UIC systems are similar in that they both classify counties by population size and adjacency to a metropolitan area. However, the UIC system also classifies counties according to the size of their largest city or town. Both RUCC and UIC build on the MSA classification system, which distinguishes between metropolitan, micropolitan, and non-core counties. The ERS County Typologies system provides an entirely different view focused on employment and earnings by industry. The following pages provide a detailed overview of each classification system. 1 Members of the research team are investigating similar questions in an ongoing research project for the Federal Highway Administration on rural mobility and safety issues.

6 Metropolitan Statistical Areas (MSA) This is the most compact classification system, with just the following three categories: Metropolitan, Micropolitan, and Non-core. It is foundational to many other systems because the categories are based on U.S. Census Bureau population and commuting data. The system is useful for defining metropolitan areas but provides too little detail to identify different types of rural counties. Still, our proposed system uses MSA categories to filter out urban counties. – Metropolitan county: Located in an MSA and has a population density greater than 1,000 people per square mile or a total population greater than 250,000 people – Micropolitan: Contains one or more urbanized area with a population over 10,000 but less than 50,000, or is adjacent to and linked economically with such a county – Non-core: Not a Metropolitan or Micropolitan county Rural-Urban Continuum Codes (RUCC) There are nine categories in the RUCC system, which offer greater nuance than the MSA system. RUCC considers population and adjacency to metropolitan (metro) areas but does not consider other characteristics like resource-dependence. The nine categories are: – Counties in metro areas with a population of 1 million or more – Counties in metro areas with a population of 250,000 to 1 million – Counties in metro areas with fewer than 250,000 people – Urban population of 20,000 or more, adjacent to a metro area – Urban population of 20,000 or more, not adjacent to a metro area – Urban population of 2,500 to 19,999, adjacent to a metro area – Urban population of 2,500 to 19,999, not adjacent to a metro area – Completely rural or with an urban population of less than 2,500, adjacent to a metro area – Completely rural or with a population of less than 2,500, not adjacent to a metro area Urban Influence Codes (UIC) The UIC system includes 12 categories that consider population, adjacency to a metropolitan area, and the presence of towns and other population clusters that are too small to be considered urban. This system has the advantage of providing nuance but could fail to reveal meaningful differences between safety performance measures given the high number of categories. The categories are: – Large metro area with at least 1 million residents or more – Small metro area with fewer than 1 million residents – Micropolitan adjacent to a large metro area – Non-core, adjacent to a large metro area – Micropolitan adjacent to a small metro area – Non-core, adjacent to small metro, town of at least 2,500 residents – Non-core, adjacent to small metro, does not contain a town of at least 2,500 – Micropolitan, not adjacent to a metro area – Non-core, adjacent to micro area, contains town of 2,500-19,999 residents – Non-core, adjacent to micro area, does not contain town of at least 2,500 – Non-core, not adjacent to a metro/micro area, contains town of 2,500 or more – Non-core, not adjacent to a metro/micro area, does not contain town of at least 2,500

7 ERS County Typology System The six-category County Typology system is different from the others because it defines categories based on a county’s employment and earnings by major industry sector. There are six mutually exclusive categories based on a county’s economic dependence and six overlapping categories based on different policy-relevant characteristics. Economic dependence categories include the following: – Farming-dependent counties – Mining-dependent counties – Manufacturing-dependent counties – Federal or state government-dependent counties – Recreation counties – Nonspecialized counties Policy-relevant categories include the following: – Low education counties – Low employment counties – Persistent poverty counties – Persistent child poverty counties – Population loss counties – Retirement destination counties

8 Existing Road Classification Systems The U.S. categorizes roads for regulatory and design purposes, including safety. FHWA and AASHTO collaborated on a functional classification system and series of manuals for evaluating road design and after-treatment effectiveness, including the Highway Capacity Manual and the Highway Safety Manual (HSM) (FHWA, 2013; AASHTO, 2009). The review of safety analysis literature and government regulatory guidance revealed that physical (i.e., design and operation) and social (i.e., behavioral and cultural) characteristics play related but distinct roles in road safety outcomes. Research on both elements was reviewed to develop the suggestions for future actions in subsequent sections (see Data Analysis Task 2). Design-Based Classification Systems FHWA’s Highway Functional Classification System distinguishes roads based on their design and operational characteristics. Ranging from high-speed and access-controlled Interstates to relatively low- speed local roads, the functional classification system defines the role a particular roadway segment plays in serving traffic flows through the network (Figure 1). Source: U.S. Department of Transportation Figure 1. Highway Functional Classification System Hierarchy

9 Planners use the classification system to design an efficient and cost-effective transportation network. FHWA describes the system in the following way: Most [roadway] travel occurs through a network of interdependent roadways, with each roadway segment moving traffic through the system towards destinations. The concept of functional classification defines the role that a particular roadway segment plays in serving this flow of traffic through the network. Roadways are assigned to one of several possible functional classifications within a hierarchy according to the character of travel service each roadway provides. Planners and engineers use this hierarchy of roadways to properly channel transportation movements through a highway network efficiently and cost effectively (FHWA, 2013). The functional classification of roads incorporates different design principles to accommodate various travel volumes, speeds, and vehicle mixes. Geometric and operational characteristics take precedence: roads with higher volumes, higher speeds, and a greater emphasis on mobility (rather than accessibility) have wider lanes and tend to include shoulders to facilitate uninterrupted service. Planners and engineers subsequently match countermeasures to operational conditions rather than classifying roads according to their safety risk during the initial design process, i.e., when functional classification takes place (Richard et al., 2015; Jenior et al., 2018; Lord et al., 2008). Systems Arising from Safety Analysis Whereas the functional classification system seeks to preemptively sort roads into categories based on their design and intended operational characteristics, safety analysis selects mitigation strategies by considering crashes and their causes retrospectively. The FHWA Office of Safety’s guidance on safety indicates that these strategies may include design changes, but that safety analysis occurs in the context of ongoing transportation systems management and operations (Figure 2). Figure 2. Relationship Between Project Development and Safety Management (Behar, 2016; Herbel et al., 2010)

10 The safety analysis literature is broad and deep, with principal focus on characterizing incidents and understanding important factors before, during, and after a crash. AASHTO and the FHWA Office of Safety publish the HSM, which includes lengthy instructions for analyzing and predicting safety impacts on roadways (AASHTO, 2009). The FHWA Office of Safety guidance describes categories of methods for countermeasure selection, which includes using a Haddon Matrix2 and professional judgement, matching countermeasures to past studies of similar hazardous behaviors, or using a statistics-driven approach (Herbel et al., 2010). However, the analytical framework is considerably more granular, incident specific, and geographically constrained than the functional classification system (Table 2). Table 2. Key Geometric and Operational Features for Safety Analysis (Lord et al., 2008) Intersections Segments • Illumination • Turning Lanes • Signalization and Phasing • Approach Speed • Channelization • Traffic Control • Median Width and Type • Shoulder Width and Type • Shoulder Rumble Strips • Roadside Hazards, Clear Zone, and Sideslope • Posted and Operating Speed • Horizontal and Vertical Alignment • Density of Accesses, Driveways, And Median Openings One example drawn from the Highway Safety Improvement Program (HSIP ─ https://highways.dot.gov/safety/hsip) describes a detailed investigation of factors contributing to a specific crash type involving cyclists and pedestrians. It notes how vehicles approached a specific intersection, the presence of pavement markings and parked vehicles, the density of curb cuts, and observed vehicle speeds (Herbel et al., 2010). The parameters considered in this example (e.g., road geometry, intersection density, speeds) do not factor into the classification of the roads discussed. This example also hints at the role of driver culture on safety outcomes, which other literature discusses in detail (Moeckli and Lee, 2007; Preusser et al., 2008; Richard et al., 2018; Jenoir et al., 2018; Zegeer et al., 2013). Clearly, any classification system arising from standard safety analysis would focus on information generally representative of similar incidents. The research team therefore recommend that these safety analysis components enter into the roadway classification process. Proposed Classification System The following section describes the proposed rural road safety classification system and presents a method for combining area and road categories into a single system to support subsequent tasks. 2 The Haddon Matrix is a two-dimensional framework for site-level safety analysis. It relates phase of injury (pre- crash, crash, post-crash) to four influencing factors related to the human(s) involved, vehicle/equipment, physical environment, and socioeconomic context.

11 Rural Areas The proposed area classification system from the 2021 FHWA project includes eight rural categories3 and a metropolitan category to enable comparisons between rural and urban areas (Stephens et al. 2021). The research team determined that a county-level area classification system is most practical for classifying rural areas. This is because (a) most safety datasets support county analysis zones and (b) counties are compatible with state, multi-state, and national geographies. The research team classified all U.S. counties according to the definitions below. The following five categories were based on population size, density, or spatial or economic relationship with a metropolitan area (Stephens et al. 2021): • Metropolitan (defined for purposes of comparison): Defined by their presence in a Metropolitan Statistical Area (MSA) and whether they have a population density greater than 1,000 people per square mile or a total population greater than 250,000 people. Also includes counties that are part of an MSA but did not meet the criteria for inclusion in any of the other categories. • Fringe: Non-metropolitan counties that are (1) adjacent to metropolitan counties based on RUCC or (2) are within MSAs but relatively rural as determined by (a) having more than 50% of the population living outside of an urbanized area or cluster or (b) having a population density lower than 100 people per square mile. • Micropolitan: Defined by the U.S. Office of Management and Budget as being in a Micropolitan Area. Micropolitan counties contain one or more urbanized areas with a population over 20,000 but less than 50,000 or are adjacent to and linked economically with such a county. The Micropolitan definition also includes micropolitan areas located next to metropolitan areas as indicated by Rural-Urban Continuum Codes and UIC. • Rural Towns: Not adjacent to a metropolitan area but have an “urban” population of 2,500-20,000 based on Rural-Urban Continuum Codes and UIC. • Remote: Has a population density of less than seven people per square mile or has an Urban Influence Code that defines the county as a non-metropolitan rural area that does not contain a town of at least 2,500 people. The following four categories were based on distinct characteristics that are known to influence safety performance (Stephens et al. 2021): • Agriculture & Extraction: Mining- and farming-oriented counties as defined by County Typology Codes. • Older-age: Counties where 33% or more of the population is over 60 years of age. Thirty-three percent captures counties that are in (approximately) the 95th percentile in terms of the number of people over age 60. 3 The classification system builds on existing work members of the research team are conducting for FHWA on rural mobility and safety issues.

12 • Destination: Defined using the methodology for County Typology Codes4 and based on employment, earnings, and seasonal housing data used to identify counties with a significant amount of recreational activity. Also based on migration trends indicating whether counties are retirement destinations in a manner consistent with the County Typology Codes definition of retirement-destination counties. • Tribal: Where 50% or more of the land area is designated tribal territory.5 Classification Process The research team assigned 3,142 U.S. counties to individual categories in a sequential fashion (Figure 3) (Stephens et al. 2021). First, they filtered out metropolitan counties exceeding specific population or population density thresholds, then assigned the remaining counties in the following order: tribal, agriculture & extraction, older-age, destination, remote, rural towns, micropolitan, and fringe. Lastly, the research team manually classified three counties based on professional judgement.6 Assigning counties in this order ensured that those with unique characteristics were separated out first (e.g., tribal counties that could also be considered remote). 4 A detailed methodology is located at https://www.ers.usda.gov/data-products/county-typology- codes/documentation/. 5 Tribal lands are defined by U.S. Census Bureau TIGER shapefiles, and include American Indian, Alaska Native, and Native Hawaiian areas. More details are provided here: https://catalog.data.gov/dataset/tiger-line-shapefile- 2017-nation-u-s-current-american-indian-alaska-native-native-hawaiian-area. 6 The research team manually reclassified three counties. These counties have population characteristics that made them outliers within their original category. They reclassified the Agriculture & Extraction county of Lafayette Parish, Louisiana, as a Metropolitan county due to its relatively high population density. They also reclassified the Agriculture & Extraction county of St. Tammany Parish, Louisiana, and the Destination counties of St. Johns County, Florida, and Baldwin County, Alabama, as Fringe counties due to their relatively high populations and proximity to Metropolitan counties.

13 Figure 3. County Classification Process Justification for Proposed Categories The proposed rural area classification system combines elements from the four existing systems: RUCC, UIC, MSA, and County Typology Codes. The research team also drew inspiration from several reports and studies that consider what factors make rural areas unique including National Highways Research Program (NCHRP) Project 20-122, which considers tribal status, tourism, natural resource-dependence, metro adjacency, and remoteness (Sullivan et al., 2021). They also drew from ICMA’s Putting Smart Growth to Work in Rural Communities report and NCHRP Report 582 to develop the destination county category (ICMA, 2020). The ICMA report also identifies small communities with compact street networks, on which the research team based their rural towns category. Three sources also pointed to the need for a metropolitan and exurban (i.e., Fringe) category. These include the ICMA report, FHWA’s Planning for Transportation in Rural Areas report, and NCHRP Report 582 (ICMA, 2020; Dye Management Group, 2001; Twaddell and Emerine, 2007). Beyond the review of other studies, the research team performed a preliminary analysis of safety performance measure data. It confirmed the existence of important differences in outcomes across the nine categories shown in Figure 3. Lastly, they considered the key category development factors described earlier in this memo. This included making sure the categories take into account different social and physical contexts and provide enough detail, flexibility, and compatibility to support robust analysis.

14 Table 3 summarizes key differences among the proposed rural area categories. On the following page, Figure 4 displays the categories spatially.

15 Table 3. Rural Classification Characteristics Ordered by Total Population (Stephens et al. 2021) Categories Number of Counties Total Population (2017 ACS) % of Total Population Average Population Density (2017 ACS) % of Total U.S. Land Area % of Total U.S. VMT (2018 HPMS) Fringe 762 20,124,505 6.3% 54.6 11.2% 8.6% Micropolitan 412 19,416,351 6.0% 76.6 8.5% 7.3% Destination 219 8,244,711 2.6% 44.3 11.5% 3.1% Rural Towns 261 5,483,597 1.7% 39.5 4.9% 2.2% Agriculture & Extraction 347 4,099,380 1.3% 12.7 15.6% 2.1% Older-age 64 4,006,289 1.2% 66.9 2.3% 1.5% Tribal 94 3,078,622 0.9% 34.4 4.0% 1.3% Remote 274 2,187,895 0.7% 9.5 24.8% 1.1% All Rural Categories 2,433 66,641,350 20.7% 19.9 82.8% 27.2% Acronyms used in table: American Community Survey (ACS), Highway Performance Monitoring System (HPMS)

16 Figure 4. U.S. Counties Shaded According to Their Classification Category

17 Rural Roads The research team proposed the use of FHWA’s Highway Functional Classification System when categorizing rural roads. As discussed previously, the system distinguishes between roads by considering various design and operational characteristics shown to influence safety outcomes in the literature scan (Table 5 & Table 6). Furthermore, the functional classification system provides the following advantages over systems that are not based on design or are unfamiliar: • Transportation professionals across the U.S. already use the functional classification system in their daily work. Building a duplicative vocabulary to describe an existing system would impose an unnecessary administrative burden on practitioners, thereby diminishing the contributions of a behavioral countermeasure toolkit (Preusser et al., 2008).7 • The research literature on road safety analysis, including the HSM, does not use separate road classifications. Rather, it extrapolates incident-level risk posed by human and physical factors to estimate segment-level safety outcomes. The functional classification system already accounts for many of the primary human and physical factors discussed in the safety literature and HSM. Using an existing road classification system complements methods already in use and enables a clear and practical implementation pathway for a new toolkit. Figure 1 illustrates the hierarchy of functional classifications. FHWA further divides roads into urban and rural classifications based on whether they pass through an area with more or less than 5,000 people (USDOT, 2019). FHWA classifies more than 71% of U.S. highway centerline miles as rural, of which 49.1% are along local roads (Table 4). Major and minor rural collectors constitute 16% of U.S. highway miles, with the remaining rural miles spread across Interstates, freeways, expressways, and arterials. Although rural roads comprise over 71% of all highway miles, they carry only 30.3% of vehicle miles traveled (VMT). 7 Noting that NCHRP 622 “Effectiveness of Behavioral Highway Safety Countermeasures” discusses crash modification factors, an essential component of roadway safety analysis.

18 Table 4. Highway Road Extent and Travel by Functional Class, 2014 Rural Functional Class Share of U.S. Highway Miles Share of U.S. Highway VMT Interstate (Rural) 0.7% 7.6% Other Freeway and Expressway (Rural) 0.1% 0.9% Other Principal Arterial (Rural) 2.2% 6.2% Minor Arterial (Rural) 3.2% 4.6% Major Collector (Rural) 9.8% 5.2% Minor Collector (Rural) 6.2% 1.6% Local (Rural) 49.1% 4.1% All Rural Roads 71.2% 30.3% Source: U.S. Department of Transportation The tables on the following pages provide a detailed overview of the functional classification system for rural roads. Table 5 describes rural interstates, freeways, expressways, and arterials and summarizes their design characteristics and traffic level thresholds. Table 6 does the same for rural collectors and local roads. In addition, Appendix A provides conceptual definitions of the various road classifications.

19 Table 5. FHWA Guidelines for Rural Arterials (FHWA, 2013) Interstate Other Freeways and Expressways Other Principal Arterials Minor Arterials Description • Serve corridor movements with trip length and travel density characteristics indicative of substantial statewide or Interstate travel. • Serve all or nearly all urbanized areas and a large majority of urban clusters areas with 25,000 and over population. • Provide an integrated network of continuous routes without stub connections (dead ends). • Link cities and larger towns (and other major destinations such as resorts capable of attracting travel over long distances) and form an integrated network providing Interstate and inter-county service. • Spaced at intervals, consistent with population density, so that all developed areas within the State are within a reasonable distance of an arterial roadway. • Provide service to corridors with trip lengths and travel density greater than those served by rural collectors and local roads, with relatively high travel speeds, and minimum interference to through movement. Lane Width (ft) 12 11-12 11-12 10-12 Inside Shoulder Width (ft) 4-12 0-6 0 0 Outside Shoulder Width (ft) 10-12 8-12 8-12 4-8 Annual Average Daily Traffic (AADT) 12,000-34,000 4,000-18,500 2,000-8,500 1,500-6,000 Divided/Undivided Divided Either Either Undivided Access Control Fully controlled Partially or fully controlled Partially or Uncontrolled Uncontrolled

20 Table 6. FHWA Guidelines for Rural Collectors and Locals (FHWA, 2013) Major Collector Minor Collector Local Description Provide service to any county seat not on an arterial route, to the larger towns not directly served by the higher systems, and to other traffic generators of equivalent intra-county importance such as consolidated schools, shipping points, county parks, important mining and agricultural areas. Link these places with nearby larger towns and cities or with arterial routes. Serve the most important intra- county travel corridors. Be spaced at intervals and consistent with population density to collect traffic from local roads and bring all developed areas within reasonable distance of a major collector. Provide service to smaller communities not served by a higher-class facility. Link locally important traffic generators with their rural counterparts. Primarily serve to provide access to adjacent land. Provide service for travel over short distances as compared to higher classification categories. Constitute the mileage not classified as part of the arterial and collector systems. Most likely to include unpaved roads when compared with other categories. Lane Width (ft) 10 -12 10-11 8-10 Inside Shoulder Width (ft) 0 0 0 Outside Shoulder Width (ft) 1-6 1-4 0-2 AADT 300-2,600 150-1,110 15-400 Divided/Undivided Undivided Undivided Undivided Access Control Uncontrolled Uncontrolled Uncontrolled

21 Combined Rural Road Safety Classification Road design and the surrounding environment both influence driver behavior. The Highway Functional Classification System includes important design characteristics and is therefore appropriate for safety analysis focused on behavioral countermeasures. Similarly, a safety-oriented road classification system should be sensitive to surrounding settlement patterns and social influences (Moeckli and Lee 2007; Richard et al., 2018; Richard et al., 2015; Jenior et al., 2018; Zegeer et al., 2013). For this reason, an area classification system derived from demographic, economic, and geospatial data is also appropriate. The research team believed that distinguishing road functional classifications by area type, using the area classification system outlined in the previous section, would incorporate the key physical road safety factors while also including area-dependent human factors likely related to driver behavior. The research team proposed using a combined rural road safety classification system consisting of eight rural county types and seven rural road types for future research tasks (Table 7).8 The rural county types capture social and cultural differences while the road types capture differences in road design. As such, the safety performance of a single county or road type is unlikely to be representative of the entire U.S. Table 7 shows a matrix with county types along the side and road types across the top. The matrix provides a two- dimensional framework for analyzing crash data for different combinations of county types and road types. Using this combined system, the research team can investigate nuanced questions that support the development of tailored countermeasures. For instance: Are rural Interstate crashes more common in Fringe counties or remote counties? On which types of roads do crashes in Tribal counties typically occur? Are crashes on local roads more common in destination counties where there are more visitors who might be unfamiliar with the area? The ability to answer questions like these allowed the research team to develop behavioral countermeasures that were sensitive to both design and the surrounding environment. Table 7. Combined Rural Area / Rural Road Classification System Interstate Other Freeways & Expressways Other Principals & Arterials Minor Arterials Major Collector Minor Collector Local Agriculture & Extraction Destination Fringe Micropolitan Older-age Remote Rural Towns Tribal 8 The table is purposely empty and meant to be illustrative of the types of area/road combinations investigated in Task 2.

Next: Data Analysis (Task 2) »
Highway Safety Behavioral Strategies for Rural Areas Get This Book
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Rural roads have a higher risk of fatality or serious injury than urban roads due to factors such as varying terrain, wildlife, and long distances between services.

BTSCRP Web-Only Document 4: Highway Safety Behavioral Strategies for Rural Areas, from TRB's Behavioral Transportation Safety Cooperative Research Program, documents the overall research effort that produced BTSCRP Research Report 8: Highway Safety Behavioral Strategies for Rural and Tribal Areas: A Guide. Supplemental to the document is a PowerPoint presentation that outlines the project.

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