National Academies Press: OpenBook

Development of Clear Recovery Area Guidelines (2024)

Chapter: Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities

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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Suggested Citation:"Chapter 4 - Encroachment Variable Distributions and Marginal Probabilities." National Academies of Sciences, Engineering, and Medicine. 2024. Development of Clear Recovery Area Guidelines. Washington, DC: The National Academies Press. doi: 10.17226/27593.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

19   Encroachment Variable Distributions and Marginal Probabilities NCHRP Web-Only Document 341 Crash Database The research team used the crash database from NCHRP Web-Only Document 341 to develop univariate distributions for the encroachment parameters in the simulation matrix (3). NCHRP Web-Only Document 341 crash records originated with the NASS-CDS database. The developers used a scaled scene diagram and other available information (e.g., vehicle crash data) to clini- cally reconstruct both impact and encroachment conditions. Additional supplemental roadway and roadside site data were also collected. Based on the sampling scheme developed for the NASS-CDS, each crash record has a sampling weight that indicates the number of crashes the investigated crash represents. These sampling weights are intended to be used in any analyses of the crash data. Thus, before any analyses were performed or encroachment variable distribu- tions developed, crash records from NCHRP Web-Only Document 341 were linked to their cor- responding NASS-CDS sampling weights. The research team reviewed both NCHRP Web-Only Document 341 and NASS-CDS coding manuals and determined the keys to link the tables in the NCHRP Web-Only Document 341 database (3) to the NASS-CDS case files. All the 2,151 crash records from NCHRP Web-Only Document 341 database were matched to the corresponding crash records in the NASS-CDS data files. After merging all events to the NASS-CDS, it was found that the sampling weights assigned to the 2,151 crash records had the following features: • Range = 1.5 to 5,155.09. • Median = 107.75. • Mean = 338.99. • Standard Deviation = 594.57. The resulting distribution, which has a heavy right tail, is shown in Figure 6. In addition, NCHRP Web-Only Document 341crash records were filtered for the facility types of interest in this project, which were 2-lane undivided (2U) and 4-lane divided (4D) roadways (3). The crash records were filtered for facility type by using the median width and number of lanes. The identification of 2U roads was 1 lane in each direction of travel and a median width of zero. The 4D roads were similarly identified by the number of lanes and the presence of a median. Roadways with a two-way left-turn lane were excluded from the analysis. Encroachment Parameter Distributions and Marginal Probabilities The encroachment parameters included in the computer simulation matrix are encroachment speed, encroachment angle, vehicle orientation at departure (i.e., tracking or non-tracking), and driver input (e.g., steering and/or braking). The researchers developed univariate distributions C H A P T E R   4

20 Development of Clear Recovery Area Guidelines for the selected encroachment parameters for both 2U and 4D roadways. These distributions were used to determine conditional probabilities for the values of the variables used in the simulation matrix. The marginal probabilities were used as weighting factors to support the aggregation of simulation results for the development of lateral extent of encroachment and encroachment severity relationships. Encroachment Speed The research team used the NCHRP Web-Only Document 341 crash data to develop weighted distributions for encroachment speed for the two facility types of interest—2U and 4D road- ways. The weighted distributions of encroachment speeds and select percentiles and quantiles are shown in Figure 7, where the speeds are given in km/h, the original units in the dataset (3). In Figure 7, the distributions of encroachment speeds for 2U roadways significantly differ from the distribution for 4D roadways. There is a distinct shift of the distribution to the right for 4D roadways, indicating higher encroachment speeds for 4D roadways compared to 2U roadways. The reason for this difference is likely associated with higher speed limits for 4D facilities coupled with geometric design in accordance with higher design speeds. However, it is interesting to note that although the lower percentiles are clearly higher for 4D facilities (e.g., the 50th percentile is 20 km/h or 12.4 mph higher for 4D facilities), the two facility types tend to become more similar at their extreme values on the right. The difference between 95th percentile encroachment speeds is reduced to about 10 km/h or 6.2 mph. The team used the weighted distributions of encroachment speed shown in Figure 7 to evalu- ate marginal probabilities associated with different sets of selected encroachment speed ranges for use in the simulation matrix. The researchers decided to move forward with the encroach- ment speed bins presented in Table 7. The encroachment speed values presented in Table 7 pro- vided the most balanced set of probabilities across both 2U and 4D roadways. These probabilities were used as weighting factors for the encroachment speeds used in the simulation matrix. The 25-mph bin represents all crashes with a reported encroachment speed of less than 30 mph. The 35-mph bin represents all crashes with a reported encroachment speed greater 0 0 20 0 40 0 60 0 80 0 1, 00 0 1,000 2,000 3,000 4,000 5,000 Fr eq ue nc y Weight Value Figure 6. Distributions of sampling weights for NCHRP Web-Only Document 341 database crashes (3).

Encroachment Variable Distributions and Marginal Probabilities 21   Figure 7. Weighted distribution of encroachment speeds. Speed (mph) 25 35 45 55 65 Facility Type P(x < 30 mph) P(30 mph <= x < 40 mph) P(40 mph <= x < 50 mph) P(50 mph <= x < 60 mph) P(x >= 60 mph) 2U 0.372 0.230 0.205 0.107 0.086 4U 0.164 0.143 0.346 0.205 0.141 NOTE: P= probability. Table 7. Marginal probabilities for encroachment speeds to be used in simulation study.

22 Development of Clear Recovery Area Guidelines or equal to 30 mph and less than 40 mph. The 45-mph bin represents all crashes with a reported encroachment speed greater or equal to 40 mph and less than 50 mph. The 55-mph bin repre- sents all crashes with a reported encroachment speed greater or equal to 50 mph and less than 60 mph. The 65-mph bin represents all crashes with a reported encroachment speed of 60 mph or greater. Encroachment Angle The encroachment angle of the vehicle relative to the roadway was extracted from the NCHRP Web-Only Document 341 database for each crash record. For left-side departures, the encroachment angle was reported between 180 and 360 degrees in the database. These values were adjusted and combined with the right-side encroachment angles to develop the weighted distributions and select percentiles for both 2U and 4D roadways as shown in Figure 8. The distributions suggest that the encroachment angle for 2U facilities is slightly higher than for 4D facilities. The team used the weighted distributions of encroachment speed shown in Figure 8 to evaluate marginal probabilities associated with different sets of selected encroachment angle ranges for Figure 8. Weighted distributions and percentiles of encroachment angle.

Encroachment Variable Distributions and Marginal Probabilities 23   use in the simulation matrix. The research team considered different sets of encroachment angle values and concluded those presented in Table 8 provided a reasonably balanced set of marginal probabilities across both 2U and 4D roadways. These probabilities were used as weighting factors for the encroachment angles used in the simulation matrix. The 5-degree bin represents all crashes with a reported encroachment angle of less than 10 degrees. The 15-degree bin represents all crashes with a reported encroachment angle greater or equal to 10 degrees and less than 20 degrees. The 25-degree bin represents all crashes with a reported encroachment angle greater or equal to 20 degrees and less than 30 degrees. The 35-degree bin represents all crashes with a reported encroachment angle of 30 degrees or greater. Sideslip Angle The simulation matrix includes both tracking and non-tracking encroachment conditions. A tracking condition is generally defined as the vehicle heading angle (i.e., vehicle orientation) and encroachment angle (i.e., the path of the vehicle’s CG) being aligned as the vehicle leaves the traveled way. A non-tracking encroachment can be generally defined as the encroaching vehicle having a sideslip angle as it leaves the roadway, where the sideslip angle is defined as the difference between the vehicle heading angle and the encroachment angle. Crash records from NCHRP Web-Only Document 341 were analyzed to help with the selection of a reasonable side- slip angle for the non-tracking encroachments in the simulation matrix. The research team calculated the sideslip angle by computing the difference between the heading angle and the encroachment angle. The sideslip angle calculation considered four different cases: both heading and encroachment angles are toward the right; both heading and encroachment angles are toward the left; the encroachment angle is toward the right, but the heading angle is toward the left; and the encroachment angle is toward the left, but the heading angle is toward the right. A positive sideslip angle indicates a larger encroachment angle than the heading angle in the direction of encroachment, and a negative sideslip angle indicates the opposite. Figure 9 and Figure 10 show the weighted histograms for right-side and left-side departures, respectively. For right-side departures, the mean sideslip angle is 4.02 degrees, and the three relevant quar- tiles (i.e., 25th, 50th, and 75th percentiles) are −1.52, 0.62, and 6.08 degrees, respectively. For left-side departures, the mean sideslip angle is 0.18 degrees, and the three relevant quartiles (i.e., 25th, 50th, and 75th percentiles) are −3.62, 1.92, and 10.72 degrees, respectively. Further statistical analysis of the sideslip angle was conducted to assist with the determination of a suitable threshold or definition between tracking and non-tracking encroachments. For this analysis, the team used a distribution of the absolute sideslip angle as a basis rather than relative to the direction of departure. Figure 11 shows the distribution for the absolute sideslip angles and key quantiles of that distribution. Encroachment Angle (degrees) 5 15 25 35 Facility Type P(x < 10) P(10 <= x < 20) P(20 <= x < 30) P(x >= 30) 2U 0.283 0.399 0.140 0.178 4D 0.377 0.383 0.037 0.203 Table 8. Marginal probabilities of encroachment angles in the simulation matrix.

24 Development of Clear Recovery Area Guidelines 10 ,0 00 20 ,0 00 30 ,0 00 40 ,0 00 0 Fr eq ue nc y Figure 9. Weighted distribution of sideslip angles (right-side departures). 5, 00 0 10 ,0 00 15 ,0 00 20 ,0 00 0 Fr eq ue nc y Figure 10. Weighted distribution of sideslip angles (left-side departures).

Encroachment Variable Distributions and Marginal Probabilities 25   A clear observation is that a large proportion of the sideslip angles are small (75% of the data has absolute sideslip angles smaller than 11.08 degrees). Current vehicle technology often allows vehicles to recover from small sideslip angles. The selected definition or threshold between track- ing and non-tracking is intended to represent a significant non-tracking condition from which the ability to recover is not certain. The research team considered different values of sideslip angles for the non-tracking thresh- old. Figure 12 shows the cumulative distribution of the absolute sideslip angles of interest for defining a non-tracking encroachment threshold and the corresponding percentage of tracking Figure 11. Absolute sideslip angles (left) and quantiles (right). Figure 12. Cumulative distribution of absolute sideslip angles and thresholds of interest.

26 Development of Clear Recovery Area Guidelines encroachments that would result for each value. For example, Figure 12 indicates that 82% of the sideslip angles are smaller than or equal to 15 degrees. Thus, if a 15-degree sideslip angle were to be selected as the threshold between tracking and non-tracking encroachments, it would result in 82% tracking encroachments and only 18% non-tracking encroachments. The researchers selected a sideslip angle of 10 degrees to serve as the definition between tracking and non-tracking encroachments. Using the sideslip angle distribution presented in Figure 12, this results in 74% tracking encroachments and 26% non-tracking encroachments. The research team considered these distributions as a reasonably representative condition. Selecting smaller sideslip angles would have resulted in a more conservative rather than representative condition. Also, most of the U.S. vehicle fleet is now equipped with electronic stability control, which is very effective at helping drivers recover from smaller sideslip angles. Figure 13 shows the truncated distributions at various thresholds and the key quantiles for each. The corresponding quantiles for the 10-degree sideslip angle are shown in the lower left truncated distribution. The 50th percentile sideslip angle of this truncated distribution would be approxi- mately 21 degrees. This was considered a reasonable sideslip angle to use for the non-tracking encroachment condition defined in the simulation matrix. The intent was to define a representative rather than a worst-case non-tracking condition, and the 50th percentile fulfills that intent. There- fore, the non-tracking encroachments in the simulation matrix were defined using a sideslip angle of 21 degrees and yaw rate of 15 degrees/sec as the vehicle departs the travelway. Driver Input Statistical analysis of driver input provided during the encroachment (i.e., none, steering, braking, or combined steering and braking) was conducted to develop marginal probabilities Figure 13. Various truncated distributions for absolute sideslip angles and quantiles.

Encroachment Variable Distributions and Marginal Probabilities 27   for each driver input category. Additional data elements were extracted from the NASS-CDS database and merged with the NCHRP Web-Only Document 341 database (3) for this purpose. More specifically, the field “Attempted Avoidance Maneuver” was accessed in the “Pre-Crash” tab as follows: “Vehicle 1” >>> “General Vehicle” >>> “Pre-Crash.” Tracking encroachments have been defined for purposes of this project as those that have a sideslip angle less than or equal to 10 degrees. Current vehicle technology permits vehicles to readily recover from shallow sideslip angles. In non-tracking encroachments, the driver has already reacted and lost control of the vehicle. Thus, it is not significant to analyze driver input for these encroachments. Using all the data in the NCHRP Web-Only Document 341 database, the percent of encroach- ments reported as having no driver input is very high as shown in Figure 14 and Figure 15 for Figure 14. Weighted distribution of driver inputs for 2U roadways. Figure 15. Weighted distribution of driver input for 4D roadways.

28 Development of Clear Recovery Area Guidelines 2U and 4D roadways, respectively. This percentage of no driver response is not consistent with driver input responses derived from the older database in NCHRP Report 665 (16). The researchers investigated these differences to determine the most appropriate values for marginal probabili- ties to weight the simulation outcomes. Assuming the driver is not impaired or asleep, there is a perception-reaction event that occurs after a driver inadvertently leaves the travelway. The driver perceives the departure from the road and reacts to it by steering and/or braking the vehicle. If an object is present close to the roadway, it will likely be impacted before completion of the perception-reaction phase, and the crash would be coded as “no driver input.” However, this creates a truncated distribution. In the absence of striking an object so quickly, the driver may have reacted by steering and/or braking their vehicle. In this project, interest lies in the driver’s behavior during unencumbered encroachments onto the roadside. To further investigate these cases, distributions of the lateral offset of objects struck were devel- oped. As shown in Figure 16 and Figure 17 for 2U and 4D roadways, respectively, a high percentage of objects struck have a lateral offset of less than 10 ft. The lateral distance traveled during a PRT of 1 second or greater will vary with the speed and angle of the encroaching vehicle. However, in many, if not most scenarios, a lateral distance of 10 ft will be traveled prior to completion of the PRT. For example, if a vehicle leaves the travelway at a speed of 40 mph, it will travel 59 ft in 1 second. Even at an encroachment angle of only 10 degrees, the vehicle will travel a lateral distance of 10.2 ft during that time. A higher encroachment speed and/or angle will increase the lateral distance traveled during a 1 second PRT beyond 10 ft. The histograms in Figure 16 and Figure 17 demonstrate that most of the events in the data- base have very small lateral offsets, suggesting that the drivers probably did not have a chance to react before hitting a roadside object. Lateral Offset of Object Struck (ft) NOTE: Left-side departures are negative and right-side departures are positive. Figure 16. Weighted distribution of lateral offset of object struck for 2U roadways.

Encroachment Variable Distributions and Marginal Probabilities 29   The maneuver distributions associated with crash events that occurred greater than 10 ft off the roadway are shown in Figure 18 and Figure 19 for 2U and 4D roadways, respectively. Comparing Figure 18 and Figure 19 with the respective distributions in Figure 14 and Figure 15, a significant drop in the percentage of no driver response is observed for both 2U and 4D roadways. Due to the small number of cases with objects struck beyond 10 ft, the researchers decided to combine the data for 2U and 4D roads to develop the marginal probabilities. The marginal probabilities associated with driver inputs used in the simulation (Table 5), including the non- tracking condition, are presented in Table 9. While these values are more consistent with the data in NCHRP Report 665, particularly for the no-response category, there are still differences (16). Most notable is the lower percentage of braking responses in the NCHRP Web-Only Document 341 data in the form of braking only as well as combined braking and steering (3). This difference may be due to the increased use of ABSs. NCHRP Report 665 data covers a period from 1996 through 2002 (16). Federal Motor Vehicle Safety Standard Number 135, Light Vehicle Brake Systems (49 CFR 571.135), requires an ABS on passenger cars manufactured on or after September 1, 2000, and on multipurpose passenger vehicles, trucks, and buses with a gross vehicle weight rating of 7,716 pounds that are manufactured on or after September 1, 2002. Although many vehicles would have had an ABS before this compulsory date, it stands to reason that many of the vehicles in the older NCHRP Report 665 database would not, while virtually all of the vehicles in the more recent NCHRP Web-Only Report 341 database would (16, 3). Because ABSs prevent wheel lockup, physical evidence of braking is much more difficult to discern. It is possible that crash investigators are not able to detect some braking responses as a result. Thus, the differences in driver response noted between the two datasets may be attributed to difficulty detecting braking with ABSs more than as a result of actual differences in driver response. Lateral Offset of Object Struck (ft) NOTE: Left-side departures are negative and right-side departures are positive. Figure 17. Weighted distribution of lateral offset of object struck for 4D roadways.

30 Development of Clear Recovery Area Guidelines Figure 18. Distribution of driver input for objects struck greater than 10 ft off roadway (2U roadways). Figure 19. Distribution of driver input for objects struck greater than 10 ft off roadway (4D roadways).

Encroachment Variable Distributions and Marginal Probabilities 31   Vehicle Probability Matrix The researchers analyzed vehicle sales data to determine the marginal probability for each vehicle platform category of interest and selected a representative vehicle from each class for use in the encroachment simulation analyses. The researchers used 2019 U.S. sales data from Good- carbadcar.net for the analysis. Although 2020 data was available at the time of the analysis, it was dramatically influenced by the COVID-19 pandemic, and the heavily skewed numbers were not considered reflective of anticipated vehicle sales moving forward. The top 10 best-selling models of 2019 and the respective vehicle platform categories they represent are presented in Table 10. The vehicle types initially considered for the simulation matrix included a small passenger car, midsize sedan, SUV, and pickup truck. To develop the marginal probabilities or weights for the different simulated vehicle platforms, a classification scheme was needed to assign each vehicle make and model to its most appropriate platform. The classification criteria of the Highway Loss Data Institute were used to group each vehicle make and model into their representative categories (23). The classification scheme presented in Figure 20 was used to classify passenger car make and models into the midsize sedan and small car model platforms. The length, width, curb weight, and other specifications were obtained from the respective official websites of the vehicle makes and two other trusted sites: www.edmunds.com and www.cars.com. For this project, the small passenger car classes (i.e., S1, S2, and S3) were grouped to represent the small car vehicle category, and all midsize and large passenger car classes (i.e., M, L1, and L2) were included in the midsize sedan category. Input Number Driver Type Weight 1 No driver input (tracking) 0.223 2 Steering (tracking) 0.061 3 Braking (tracking) 0.113 4 Steering and braking (tracking) 0.343 5 Steering and braking (non-tracking) 0.260 Table 9. Weights for driver inputs used in the simulation matrix. Rank Vehicle Classification Sales 2019 #1 Ford F-Series Pickup 896,526 #2 Ram Pickup Pickup 633,694 #3 Chevrolet Silverado Pickup 575,569 #4 Toyota RAV4 CUV 448,068 #5 Honda CR-V CUV 384,168 #6 Nissan Rogue CUV 350,447 #7 Chevrolet Equinox CUV 346,049 #8 Toyota Camry Passenger Car 336,978 #9 Honda Civic Passenger Car 325,650 #10 Toyota Corolla Passenger Car 304,850 Table 10. Top 10 best-selling vehicle models in 2019.

32 Development of Clear Recovery Area Guidelines The Insurance Institute for Highway Safety (IIHS) has three classifications for pickups based on carrying capacity and curb weight as follows: • Small (P1): curb weight of 4,000 pounds or less. • Large (P2): curb weight of more than 4,000 pounds and a carrying capacity of one-half ton. • Very Large (P3): curb weight of more than 4,000 pounds and a carrying capacity of three- quarters or 1 ton. Sales data segregated by carrying capacity is not readily available. As shown in Table 10, the available sales data tends to aggregate the different models (e.g., Ford F-Series) rather than report them by model trim (e.g., F-150, F-250, F-350). However, since all pickup trucks were included in the pickup truck category, this did not affect the outcome of the classification. IIHS divides SUVs into five classes. The smallest and largest classes of SUVs (U1 and U5, respectively) are classified on vehicle shadow (the overall length x width) and curb weight. The other classes are based only on curb weight as follows: • Mini (U1): curb weight of 3,000 pounds or less and a shadow less than 75 square feet. • Small (U2): curb weight between 3,001 and 3,750 pounds. • Midsize (U3): curb weight between 3,751 and 4,750 pounds. • Large (U4): curb weight between 4,751 and 5,750 pounds. • Very Large (U5): curb weight of more than 5,751 pounds or a shadow of more than 115 square feet. All these classes were included in the SUV model category for purposes of developing prob- abilities for this project. Regarding vans, the research team classified minivans as V1 and full-size vans as V2 based on size and weight. For probability distribution purposes, both of these classes were included in the SUV category. After some statistical analysis of the 2019 vehicle sales data, the research team developed the percentage allocations for the vehicle categories of interest as shown in Figure 21. Using this approach, the results suggested that approximately 51% of the 2019 U.S. vehicle market fell into the SUV category. Looking deeper into the SUV category data, the research team noticed a large range of curb weights and vehicle dimensions that would make it difficult for a single-vehicle model to Figure 20. Passenger car classification scheme.

Encroachment Variable Distributions and Marginal Probabilities 33   adequately represent this category in the simulations. Of particular note is the rapidly growing vehicle category that is being referred to by the manufacturers as either CUV or crossover. Such vehicles are commonly smaller and tend to be more stable than midsize or large SUVs. Thus, if this large class was to be modeled using a midsize SUV, the resulting guidelines may have tended to be overly conservative. The researchers performed additional literature reviews to further examine this issue and develop vehicle model categories that would be more representative of the changing vehicle fleet. It was decided that the SUV category would be separated into two categories. One category would be comprised of CUVs, also known as crossovers, and the second category would include more traditional SUVs. To distinguish between these two vehicle classes, researchers used the Wards’ vehicle classification criteria and information from Midwest Roadside Safety Facility (MwRSF) Research Report No. TRP-03-427-20 that resulted from NCHRP Project 20-7 Task 372, “Evaluation of MASH Test Vehicles” (24). This classification is based on vehicle body style and size. CUVs have a unibody construction, while SUVs have a constructed body on a frame. Adding another vehicle category in the matrix would have increased the simulation matrix and subsequent analysis effort beyond available resources. Therefore, the research team decided to merge the small car and midsize sedan categories into the passenger car category. It was noted that all of the highest-selling small car makes and models were part of the larger S3 class and had curb weights and shadows that were not appreciably different from the highest-selling midsize sedan. The reclassification of the 2019 U.S. vehicle sales data is shown in Figure 22. These are the weight factors or marginal probabilities that were assigned to the selected simulated vehicle categories. While the CUV category has the largest allocation, it is consistent with sales trends in the U.S. vehicle market. Figure 21. Marginal probabilities for initial vehicle categories selected for simulations.

34 Development of Clear Recovery Area Guidelines Figure 22. Sales-based probabilities for the selected vehicle categories. Pickup SUV CUV Passenger Car 2019 Market Percentage Allocation

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The clear zone concept for roadside design emerged in the mid-1960s as a single distance for lateral clearance that reduced the likelihood of an errant vehicle striking a roadside obstacle. Subsequent recovery area guidance that evolved over the next two decades provided a variable distance expressed in terms of traffic volume, design speed, sideslope, and other roadway and roadside factors.

NCHRP Research Report 1097: Development of Clear Recovery Area Guidelines, from TRB's National Cooperative Highway Research Program, develops updated guidelines for roadside clear zones expressed in terms of key roadway and roadside design parameters. These updated guidelines can aid designers in better understanding the risk associated with roadside encroachments while recognizing and working within the associated design constraints.

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