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Protocols for Network-Level Macrotexture Measurement (2021)

Chapter: Chapter 1 - Introduction

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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2021. Protocols for Network-Level Macrotexture Measurement. Washington, DC: The National Academies Press. doi: 10.17226/26225.
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3   The various components of pavement surface texture have a high influence on vehicle-road interactions. In particular, macrotexture features contribute to pavement friction, tire-pavement noise, splash and spray, and rolling resistance. Two of these properties—friction and splash and spray—have a significant impact on safety, and several research studies (Parry and Viner 2005; Roe et al. 1991; Roe et al. 1998) have shown that wet-weather crashes are influenced by the macro- texture of the pavement surface. The PIARC (World Road Association) defines macrotexture as “surface irregularities of a road pavement with horizontal dimensions ranging between 0.5 mm and 50 mm (see Figure 1) and vertical dimensions between 0.2 mm and 10 mm” (PIARC 2016). As water film thickness increases, the pavement’s macrotexture provides drainage paths for water beneath the tire to escape, reducing hydroplaning potential and allowing for greater tire/pavement adhesion (a function of the pavement’s microtexture). In addition, macrotexture provides friction through hysteresis (energy loss due to asymmetrical deformation of the tire). The hysteresis effect increases exponentially with increasing vehicle speed, accounting for up to 95% of available friction at speeds above 65 mph (Hall et al. 2009). Therefore, the availability of robust macrotexture data in pavement management systems would significantly aid highway agencies in assessing the adequacy of pavement surface macrotexture and determining if corrective actions are required. Traditional methods of measuring macrotexture, such as the sand patch test, are cumbersome, time consuming, provide information on a limited portion of the pavement surface, and expose workers to risk. Even the current state-of-the-practice method that uses the circular texture meter (C.T. Meter) to measure macrotexture maintains many of the same limitations. Consequently, a need exists for reliable and repeatable methods for measuring macrotexture of the pavement surface at the network level. Several manufacturers of pavement evaluation equipment have developed systems that can collect macrotexture at highway speeds. These systems have been integrated into multipurpose pavement evaluation equipment; however, not all the devices produce the same measurements, and the current parameters used for characterizing the macro- texture (e.g., mean profile depth, abbreviated MPD) may not be optimal for current technolo- gies and users of the information. Recent research by Flintsch et al. (2003) and Mogrovejo et al. (2016) suggests that improved data collection protocols, quality management approaches, and characterization parameters for macrotexture can enhance pavement management practice. 1.1 Project Objective The objective of NCHRP Project 10-98 was to develop recommended protocols for test methods, equipment specifications, and data QA practices for network-level macrotexture measurement. Specific tasks included the following: 1. Identify equipment, environmental, and operational factors that influence macrotexture measure- ment and the macrotexture characterization parameters used to represent the macrotexture; C H A P T E R   1 Introduction

4 Protocols for Network-Level Macrotexture Measurement 2. Develop improved methods for network-level macrotexture measurement that address these factors and parameters; and 3. Develop protocols for equipment specifications, operational procedures for collecting data, and certification procedures for equipment to facilitate the use of these methods. 1.2 Literature Review International Organization for Standardization (ISO) document 13473-1 describes macro- texture as a pavement surface’s deviation from a true planar surface for texture wavelengths from 0.5 mm to 50 mm. The standard further defines texture wavelength (λ) as “the horizontal dimension of the irregularities of a texture profile” (Item 3 in Figure 2). Texture profiles (Item 2 Figure 1. Pavement texture characteristics (PIARC 2016). Figure 2. Basic terms describing pavement surface texture (ISO 13473-1 [2019]).

Introduction 5   in Figure 2) are defined as “a continuous 2D sample along a [horizontal] line of the pavement surface” (ISO 13473-1 2019). In recent years, the C.T. Meter and the MPD have become widely accepted among practi- tioners for measuring and characterizing macrotexture, respectively. However, like traditional volumetric tests (e.g., the sand patch test), the C.T. Meter is a static test that is not suitable for continuous network-level measurements. It succeeds in eliminating much of the operator dependence of the volumetric tests, but it remains labor intensive and time consuming, requires an expensive lane closure, and exposes the operator and equipment to hazardous conditions of live traffic and/or construction activity. To gather the complete macrotexture data and overcome the limitations of static test methods, dynamic methods using high-speed laser equipment (HSLE) have been developed and applied for texture measurement (McGhee and Flintsch 2003). With HSLE, significant resolution of texture measurements has been achieved at highway speeds; however, two issues related to HSLE still need to be addressed: 1. What factors influence macrotexture measurements, and how should these factors be addressed? 2. What are the best parameters to characterize macrotexture? Recent research efforts have tried to address some aspects of both issues. For example, HSLE macrotexture measurements have been known to be contaminated with spike (outlier) readings, and the detection of these spikes has been investigated by Katicha et al. (2015). Regarding param- eters to characterize macrotexture, recent research acknowledges that the interactions between the tire and the pavement result in an “enveloping profile” (representing the actual profile of the tire when it rolls over the pavement surface). The parameters that take into account this enveloping profile relate better to other pavement surface characteristics such as friction, tire-pavement noise, and rolling resistance (El Gendy and Shalaby 2007; Goubert 2007; Harvey et al. 2016; Klein and Hamet 2004; Mogrovejo et al. 2016; Sandberg et al. 2011; Von Meier et al. 1992). For readers’ convenience, the primary standards and specifications referenced during the course of the research have been listed here: • ASME B46.1 (2009). “Surface Texture (Surface Roughness, Waviness, and Lay).” The American Society of Mechanical Engineers. • ASTM E965 (2015). “Standard Test Method for Measuring Pavement Macrotexture Depth Using a Volumetric Technique.” ASTM International. • ASTM E1960 (2007). “Standard Practice for Calculating International Friction Index of a Pave- ment Surface.” ASTM International. • ASTM E1845 (2015). “Standard Practice for Calculating Pavement Macrotexture Mean Profile Depth.” ASTM International. • ASTM E2157 (2015). “Standard Test Method for Measuring Pavement Macrotexture Properties Using the Circular Track Meter.” ASTM International. • ASTM E2380 (2015). “Standard Test Method for Measuring Pavement Texture Drainage Using an Outflow Meter.” ASTM International. • Austroads AP-T290-15 (2015). “A Common Data Output Specification for Texture, Cracking, Strength and Skid Resistance.” • British Standards Institution (1979). “Precision of Test Methods I: Guide for the Determination and Reproducibility for a Standard Test Method.” British Standard Institution London. • ISO 4287 (1997). “Geometrical Product Specifications (GPS)—Surface Texture: Profile Method— Terms, Definitions and Surface Texture Parameters.” • ISO 4288 (1996). “Geometrical Product Specifications (GPS)—Surface Texture: Profile Method— Rules and Procedures for the Assessment of Surface Texture.”

6 Protocols for Network-Level Macrotexture Measurement • ISO 13473-1 (1997, updated 2019). “Characterization of Pavement Texture by Use of Surface Profiles, Part 1: Determination of Mean Profile Depth.” • ISO 13473-2 (2002). “Characterization of Pavement Texture by Use of Surface Profiles, Part 2: Terminology and Basic Requirements Related to Pavement Texture Profile Analysis.” • NZTA T10 (2013). “Specification for State Highway Skid Resistance Management.” New Zealand Transport Agency. 1.2.1 Macrotexture Measurement Technologies The research team reviewed available technologies and methods for measuring pavement macrotexture. The technologies included static and walk-along devices, which can be considered reference devices used to define the “ground truth” for verifying and certifying high-speed net- work-level equipment. The various devices make three main types of measurements: volumetric, two-dimensional (2D) profiles, and three-dimensional (3D) area measurements (see Table 1). Volumetric Measurements The volumetric methods began with sand patch measurements (ASTM E965), which were used to evaluate the macrotexture of a road pavement. A similar approach, the grease patch, has been used by the National Aeronautics and Space Administration (NASA). Although it was used as the ground truth against which to compare new technologies, the sand patch method has steadily fallen out of favor due to the advent of new and improved technologies as well as the high variability in the sand patch test results due to operator error. Developed in the 1960s, a less-direct method uses an outflow meter (e.g., the HydroTimer) to correlate the flow of water to the pavement macrotexture (ASTM E2380). The method uses the permeability concept by relating the time it takes to empty a known volume of water from a gauged container. The time is inversely proportional to the macrotexture of the surface tested. Measurements Using 2D Profiles In the 1970s, several devices were developed using a stylus pointer with a magnification mech- anism to measure surface texture (Ashkar 1970). These instruments scribe a magnified profile of the surface texture using a “feeler” that provides a graphical representation of the actual surface texture; however, the method requires “engineering judgment” to interpret and use the results. As the technology of laser measurement equipment has matured, static measurement devices like the C.T. Meter (ASTM E2157) and, more recently, systems like the ELAtextur® from Germany and the Ames Laser Texture Scanner have emerged. These measurement systems are faster and less operator-dependent than the traditional equipment and methods used for volumetric measurements. Another related device is the Digital Surface Roughness Meter (DSRM), which uses a mix of laser and optical technologies to estimate the macrotexture parameters of the surfaces. However, these systems and methods still have the disadvantage of requiring lane closures for testing and providing only limited samples of the overall road texture. Newer high-speed and higher-frequency laser-based profilers can conduct continuous mea- surements of the pavement macrotexture at traffic speed and have therefore become the de facto standard for network-level data collection. Measurements Using 3D Pavement Surface Maps Early research in 3D measurements used stereo photographic principles to describe pavement surface texture (Schonfeld 1970). This approach has been further updated and improved, yield- ing promising results (El Gendy and Shalaby 2008; de Leon Izeppi et al. 2008; Goodman 2009).

Macrotexture Method Specification Type Speed Equipment Cost Texture Parameter * Technology Reference Volumetric Sand Patch E-965 Volumetric N/A < $100 MTD Manual Current Standard Grease Patch NASA ** Volumetric N/A < $100 MTD Manual HydroTimer E-2380 Volumetric N/A < $1,000 MTD Mechanical HydroTimer (n.d.) Manual Profile Recorder -- 2D Profile N/A Unknown Average Peak Height Mechanical Ashkar (1970) El Gendy and Shalaby (2007) Manual Depth Gauge -- 1-D Depth N/A < $20 Depth Tine and Groove Depth Stationary Laser Systems C.T. Meter E-2157 2D Profile N/A $ 25,000 MPD Laser Current standard ELAtextur E-1845 2D Profile N/A $10,000–15,000 MPD/ETD Laser IWS Messtechnik (n.d.) Laser Texture Scanner E-1845 2D Profile N/A $10,000–15,000 MPD/ETD/ RMS Laser Ames (2013) Digital Surface Roughness Meter (DSRM) -- 2D Profile N/A $10,000– 15,000 MPD Laser & Optics Stationary Optical Imaging Systems Stereo Vision System -- 3D Area Measurement N/A < $5,000 MPD/MTD Digital Stereovision de Leon Izeppi et al. (2008) Photometric Stereo -- 3D Area Measurement N/A < $5,000 MPD/RMS Surface Normal Vector Maps: 4-point Photo Stereo and Integration El Gendy and Shalaby (2008) Pavement Surface Imager *** Mark 1/2 -- Surface Normal Vectors for 2D Analysis N/A < $5,000 MTD/others Surface Normal Vectors -Polynomial Texture Mapping Goodman (2009) Walking-Speed Laser System ARRB Walking Profiler -- 2D Profile < 5 mph < $25,000 MPD Laser (Single Spot) ARRB (n.d.) TM2 Texture Meter ISO 13473 2D Profile < 5 mph < $25,000 MPD Laser (Line) WDM Ltd. (n.d.) ROBOTEX -- 2D Profiles (from 3D scan) < 5 mph < $25,000 MPD Laser (Line) TRANSTEC (n.d.) High-Speed Laser Equipment (HSLE) HSLE (Single-Spot Laser) E-1845 2D Profile 0–60 mph > $100,000 MPD Laser Various HSLE (Line Laser) E-1845 2D Profile 0–60 mph > $100,000 MPD Laser (Line) Various 3D Laser/Camera -- 3D Area < 60 mph > $100,000 MPD/ETD Laser and Line Scan Camera Various * MTD = mean texture depth, ETD = estimated texture depth, MPD = mean profile depth, RMS = root mean square. ** NASA = National Aeronautics and Space Administration. *** Based on Schonfeld (1970) and Howerter et al. (1977), with newer digital imaging technology. Table 1. Macrotexture measurement methods, devices, and technologies.

8 Protocols for Network-Level Macrotexture Measurement One advantage of these 3D approaches is that they allow for the computation of area-based macro- texture characterization parameters; however, they remain stationary methods and therefore require traffic control. To date, efforts to use these 3D techniques for high-speed measurements have not been proven. More recently, 3D technologies have combined camera and laser technology to measure pave- ment macrotexture at traffic speed. Research conducted by the Texas Department of Trans- portation (Texas DOT) has demonstrated the capabilities of such technologies, although the accuracy of the results depends on the camera shutter speed (Huang et al. 2013). Scanning systems (e.g., Pavemetrics, Waylink Systems) that normally are used for pavement distress surveys have also been proposed to determine macrotexture. These systems use high-speed cameras and high- power laser projection systems to acquire both intensity and range laser-imaging data. Evalua- tion of this technology is ongoing, and the currently reported resolution, even by the proponents of the technology, remains low for accurate measurement of macrotexture. Liu et al. (2016) proposed that a 3D line-laser scanner provides better measurements of macro- texture (and part of the microtexture spectrum) than does the C.T. Meter, which is the current 2D standard for stationary measurements. In this study, a 100 mm × 100 mm sample area pro- vided 5 million data points (17 times the resolution of the C.T. Meter) at a horizontal sample interval finer than 0.05 mm and a vertical texture height accuracy better than 0.05 mm. These results are, again, for a stationary device, which does not lend itself well to network-level macro- texture measurement. Nonetheless, an experiment consisting of six pavement sections found that the parameters gathered from a 3D line-laser scanner that digitally simulated 3D mean texture depth (MTD3) and the RMS deviation (RMSD3) had high correlations to the 2D parameters used most frequently by most state transportation agencies (i.e., MPD and RMS). The experiment used the linear regression models expressed in Equation 1 and Equation 2: ( )= + =0.12 0.76 3 , yielding 0.94, and (1)2MPD MTD r ( )= + =0.046 0.895 3 , yielding 0.98, (2)2RMS RMSD r where r2 = the coefficient of determination, and MPD, MTD3, RMS, and RMSD3 are expressed in mm. 1.2.2 Reference Surfaces A common concern when using precision measurement equipment for both research and real-world data gathering is calibration and verification. Various test tracks have been used throughout the years to compare equipment and even calibrate agency owned equipment. As more tests were run on these test tracks, more data were gathered, and the ground truth parameters for pavement macrotexture and friction were refined for future testing on each specific track. With the more pervasive use of laser equipment, it has become even more possible to assess, verify, validate, and certify network-level technologies and equipment using artificial manufactured surfaces. This approach can also be used as an alternative to using reference devices (such as the C.T. Meter) to ensure the accuracy and adaptability of the proposed devices. Huang et al. (2013) used machined steel surfaces that were tested with several different laser configurations (Figure 3). The use of the steel surfaces in the study also determined the optimal travel speed for the laser/camera system used. Use of these artificial surfaces, either affixed to or inlaid with the pavement surface, may require moving sensors to avoid damaging the host vehicle’s tires, the equipment, or the surfaces. Materials

Introduction 9   less damaging than steel (e.g., aluminum, ceramics) have been proposed. Another approach is to perform stationary tests in which the manufactured surfaces move under the macrotexture sensors (as suggested by ISO 13473-1) at a comparable speed to that used in network-level measure- ments (see Figure 4). ISO 13473-1 also describes typical profiles that could be manufactured to test the devices. 1.2.3 Equipment Comparisons In recent years, several macrotexture measuring equipment comparisons and harmonization exercises have been performed, usually associated with experiments to harmonize friction measure- ments. It is no coincidence that these two efforts often occur in conjunction. Many correlations between surface friction and macrotexture have been developed. These relationships are espe- cially important for high travel speeds (Ferne 2015; Roe et al. 1998; Srirangam et al. 2015). Many agencies are interested in using macrotexture measurements as a surrogate for direct (contact) 8-mm block 5.5-mm block 3-mm block 2.5-mm block 8 Unit: mm 4 8 5.5 Unit: mm 2 5.5 3 Unit: mm 1.5 3 2.5 Unit: mm 2.5 Figure 3. Pre-fabricated surface used to evaluate equipment (Huang et al. 2013). Figure 4. “Calibrator” moving surface (ISO 13473-1).

10 Protocols for Network-Level Macrotexture Measurement friction measurements, due primarily to issues arising from the dependency of various contact methods on the test tire properties (Ferne 2015). The World Road Association In 1992, the World Road Association (PIARC) published the results of the International Experiment to Compare and Harmonize Skid Resistance and Texture Measurements (Wambold et  al. 1995). The experiment included comparative measurements of 37 friction-measuring devices and 14 macrotexture measuring devices on 26 sites in Spain and 28 sites in Belgium. MPD was chosen to characterize macrotexture because this parameter had the best reproduc- ibility when comparing all participating devices. NASA Wallops Workshops From 1993 to 2008, annual workshops were hosted by NASA at the Wallops Flight Facility in Virginia (Yager 2005). Wide varieties of devices (differing each year) were demonstrated in these workshops, measuring macrotexture, friction, and longitudinal profile on a growing number of test surfaces (up to about 30 over the years). United Kingdom The UK has long recognized the importance of texture measurements on their road networks. As such, they perform Traffic Speed Condition Surveys (Ferne 2015) on an annual basis. In these surveys, multipurpose vehicles equipped with single-spot lasers are used to collect the macrotexture data. The parameter sensor-measured profile depths (SMPD) are computed from the collected macrotexture data on most of their roadways annually. SMPD values are easily trans- formed into an estimated texture depth for comparison to legacy sand patch data. Macro- texture measurements are used as a complement to friction data collected network-wide using Sideways-force Coefficient Routine Investigation Machines (SCRIM) and high-speed friction measurements. The SCRIM data is regarded as microtexture data due to the relative speed under which the test is carried out. Correlations exist between high- and low-speed friction data and parameters computed from macrotexture measurements. Research is being performed in the hope that eventually a contactless measurement method can be used to determine roadway friction levels. Other European Studies The TYROSAFE (Tyre and Road Surface Optimisation for Skid Resistance and Further Effects) initiative sought to harmonize friction measurements on roadways (Scharnigg et al. 2011). The study identified macrotexture as a major contributor to this critical safety-related road-tire interaction. The HERMES (Harmonization of European Routine and Research Measuring Equipment for Skid Resistance) project tested a proposed European Friction Index, and also found macrotexture to be a primary contributor to a roadway’s coefficient of friction (Descornet et al. 2006). The harmonization activities in Europe tried different approaches with friction and macro- texture parameters. For example, Project ROSANNE (ROlling resistance, Skid resistance, ANd Noise Emission) measurement standards for road surfaces (ROSANNE n.d.) was initiated to study pavement surface-related phenomena more closely. This project was principally con- cerned with phenomena such as rolling resistance, skid resistance, and tire-pavement noise. However, macrotexture was also an item of interest in this study, as it is a major contributor to each of the phenomena evaluated. The project also assessed the implementation of 3D macro- texture measurement equipment on European roads (Goubert 2016).

Introduction 11   Pavement Surface Properties Consortium Experiments In the United States, the Pavement Surface Properties Consortium has pooled-funds to con- duct periodic equipment comparisons since 2007 (e.g., TPF-5[141] n.d. and TPF-5[345] n.d.). Among other efforts, the researchers have evaluated the International Friction Index (IFI) coef- ficients for five devices on 24 pavement test sections on the Virginia Smart Road, which includes a broad range of surface textures (Flintsch et al. 2009). Macrotexture data were collected using a C.T. Meter (per ASTM E2157), and those results were used to calculate Speed Constant (Sp) values using the equation recommended by ASTM E1960: ( )= +14.2 89.7 , (3)S MPDp where MPD is expressed in mm and Sp is expressed in km/h. The issue of outliers in macrotexture measurements was documented in a research project that suggested a methodology to identify and remove outliers in high-speed laser (HSL) macro- texture measurement. Such a method needs to be developed before computing macrotexture parameters from the data (Perera and Wiser 2013). A method to solve this problem was proposed by Katicha et al. (2015). 1.2.4 Parameters for Characterizing Macrotexture Various parameters or indices used for characterizing and quantifying macrotexture have been identified. The following sections summarize the most relevant current and emerging parameters. Currently Adopted Parameters The mean texture depth (MTD) is a parameter based on a 3D representation of the macro- texture traditionally used with the sand patch or other volumetric methods of macrotexture measurement. In the sand patch test, a known volume of sand (or glass beads) is spread on a pavement surface to form a circle, thus filling the surface voids with sand. The diameter of the circle that is formed is measured and used to calculate the MTD. Although the sand patch test is not suitable for network-level macrotexture measurement, the recent claims of some 3D devices to have the capability to simulate digital sand patch measurements could still make the sand patch test and the derived MTD parameter relevant. MTD has been used as a method to calibrate more sophisticated devices. For 2D measurements, the MPD and the RMS of texture are typically used with laser devices, such as the C.T. Meter, or vehicle-mounted devices to characterize macrotexture. The RMS is essentially the standard deviation of macrotexture profile measurements. It has traditionally been more popular in Europe than in the United States, where MPD is more predominant. Proponents of RMS argue that it is a more reliable amplitude-based macrotexture parameter than MTD (Liu et al. 2016). A standard method for determining the MPD from the macrotexture profile of the pavement is provided in ASTM Standard E1845. The MPD is a 2D estimate of the 3D MTD. To calculate the MPD, the measured profile of the pavement macrotexture is divided into segments for analysis purposes. The slope, if any, of each segment is suppressed by subtracting a linear regression of the segment. The segment is divided into two segments, and the highest peak in each half-segment is determined. The difference between the resulting peak level for each segment and the average level of the whole segment is referred to as mean segment depth (MSD), and these differences are averaged together (Figure 5) and reported directly as the MPD.

12 Protocols for Network-Level Macrotexture Measurement The MPD is based on two peaks in the measured macrotexture profile that determines the (free) area below those two peaks. The version of ISO 13473-1 released in 2019 provides better definition for the low-pass filtering and addresses high-pass filtering to be performed when computing MPD. Recent approaches that better estimate the enveloping profile of the tire have been proposed and evaluated by many researchers (Klein and Hamet 2004; Mogrovejo et al. 2016; Sandberg et al. 2011). Mogrovejo et al. (2016) reported an improved correlation between friction and tire-pavement noise with their newly developed parameters when compared to MPD. Other Potential Macrotexture Parameters While MPD and RMS are currently the most prevalent parameters analyzed for 2D macro- texture profiles, other parameters exist that could enhance the characterization of the pavement surface. For example, Liu et al. (2016) proposed the previously discussed MTD3 and RMSD3 as 3D versions of their 2D counterparts. Skewness and kurtosis were used in the same study and are measures of texture distribution and peak characteristics, which can be gathered via both 2D and 3D sensors. Macrotexture data can be used to calculate unique indices, such as the Texture Profile Index and band-passed filtered elevation and slope variance, from data collected by devices such as the Ames Texture Scanner (Ames 2013). Still, other 3D scanners capture similar profile data, and some researchers (Sanders et al. 2014) have proposed evaluating the total space beneath all peaks through the use of a proposed 3D void volume parameter. Power spectral density (PSD) of macrotexture is a characterization that can be correlated to surface friction, which can also be used to identify particular macrotexture configurations (i.e., repeating wavelengths, parameter distributions, etc.) through spectral analysis of macrotexture wavelengths (Kargah-Ostadi and Howard 2015). Similarly, discrete wavelet transforms can be employed in decomposing data from laser profilers. The use of discrete wavelet transforms by Liu et al. (2016) separated a signal from a 3D line-laser system into a macrotexture constituent and a constituent that covered part (0.05 mm to 0.5 mm) of the microtexture spectrum. Another transformation suggested in the literature as having a good correlation to friction is the Huang– Hilbert transform (Rado and Kane 2014). Figure 5. Example of MPD calculation scheme (ASTM E1845 [2015]).

Introduction 13   Other lesser-known methods of texture characterization are based on pavement surface classifi cation systems. The Nakkel (1973) system classified road surfaces into seven types according to geometric form and effects. ASTM E559 was a classification method based on height, width, angularity, and density of the distribution of large particles. Schonfeld (1970) proposed a method based on seven groups of texture parameters, with scales of shapes, sizes, and/or degrees. Other parameters that have been considered include harshness and rugosity. None of the currently used macrotexture parameters addresses the free space between the actual tire and the pavement (tortuosity), although most engineers recognize its importance and the difference that exists in tortuosity between asphalt surfaces and the tining/grooving in concrete pavements. This phenomenon is demonstrated in Figure 6, where MPD is always a flat plane between the two highest peaks of a profile. Ferne (2015) suggested using the following new parameters to characterize (or enhance characterization) of pavement macrotexture: 3D surface void volume (based on a stereo imaging system), percentage pressed area (based on pavement/ tire contact), tire penetration depth, volume of void below tire, and volume of void occupied by tire. One promising parameter that addresses this free space between the tire and the pavement is the effective area of water evacuation (EAWE) proposed by Mogrovejo et al. (2016). EAWE is a single parameter that places the enveloping profile (Klein et al. 2004) of a tire over the measured pavement surface to identify the free space between the two. An enveloping profile of a tire accounts for deformations in the tire surface due to its placement on the rough pavement’s surface. Mogrovejo et al. (2016) found that the use of this parameter as a descriptor of macrotexture better characterized the pavement’s ability to evacuate water beneath the tire than MPD alone. MPD Location (mm) M ea su re m en t Figure 6. MPD calculation of unfiltered data.

14 Protocols for Network-Level Macrotexture Measurement tended to overestimate a surface’s ability to evacuate water, as shown in Figure 7. Correlation to friction and tire-pavement noise were also improved using this parameter. Desired Characteristics of the Parameter(s) for Characterizing Macrotexture A parameter that relates well to the key macrotexture-dependent properties of traveled surfaces is highly desirable. The capabilities of the sensing equipment must correspondingly support the needs of this parameter. While most network-level macrotexture measurement systems use high- speed single-spot laser systems, the potential for line-laser and 3D surface scanning systems also needs to be considered. Water evacuation potential, which is the predominant factor in wet-weather handling and splash-and-spray context, can be estimated from 2D profiles (from single-spot laser systems). However, the ability of a parameter such as MPD to exhaustively describe road-tire inter- action has been called into question (Leandri and Losa 2015). In a 3D environment, the prospects for meaningfully estimating a traveled surface’s ability to move water are enhanced. Improved correlation with tire-pavement noise and fuel consumption should also be expected with the 3D characterization of surface macrotexture. Tsai and Wang (2015) have experimented with collecting highway speed 3D line-laser data to automatically detect raveling on asphalt surfaces. There are geometric features of a pavement surface that affect the ability to fully characterize macrotexture. These features likewise affect the minimum capabilities of the measurement systems. For example, surfaces with isotropic features (e.g., dense-graded asphalt surface) can be character- ized by high-resolution 2D profiles, and look the same regardless of which direction the profile is taken—parallel or perpendicular to traffic, or, as is the case with the C.T. Meter, in an arc or circular pattern. On the other hand, surfaces with anisotropic features (e.g., tined or grooved concrete) are more challenging and may be better characterized by 3D technologies, especially if important features are oriented in the longitudinal direction. Calculations reflect a 100-mm sample from Asphalt Section K—OGFC, with data points collected every 0.5 mm. Stiffness (d*) calculations are as follows: (a) d* = 0.054, (b) d* = 0.027, (c) d* = 0.010, and (d) d* = 0.001. OGFC = open-graded friction courses. Figure 7. Illustration of enveloping profile calculated for different tire stiffnesses for a porous asphalt mix (Mogrovejo et al. [2016]).

Introduction 15   In addition, the orientation of the texture is also important; asphalt concrete surfaces typically provide a relatively neutral macrotexture, which means the predominant features are neither upward-oriented asperities nor valleys between them (Flintsch et al. 2003). Other common asphalt surfaces with more positive macrotexture (mostly upward-oriented asperities) include chip seals. An open-graded friction course is an example of an asphalt surface with a negative macrotexture. Concrete surfaces designed for high-speed traffic usually incorporate a highly directional feature that is either raked into the surface when the paste is still plastic or ground/grooved into the hard- ened material. The result of these finishing strategies is a negative macrotexture feature. Figure 8(a) shows an unfiltered macrotexture profile collected at the Smart Road at the Virginia Tech Trans- portation Institute. The negative value for skewness indicates a negative macrotexture. The image on the right is the same profile flipped at zero on the horizontal axis. The value for skewness now becomes positive, indicating a positive macrotexture, which is to be expected for a profile with opposite peak and valley characteristics. An adequate measurement system is one with sufficient resolution to “see” the predominant macrotexture geometric features at highway speeds. Open-graded asphalt mixtures present another challenge for characterizing macrotexture. These surfaces offer many functional advantages in terms of drainage but challenge the capa- bility of profile-based macrotexture measurement. Open-graded mixtures exhibit mostly negative features where the macrotexture is made up of voids that extend downward from the general plane of interaction with the tire. These voids are usually interconnected, which permits at least some water to move below the plane of tire-pavement interaction. While remaining isotropic, evacuation potential calculated from a 2D or 3D surface profile will not fully represent the ability of these surfaces to improve wet-weather traction and visibility. Likewise, the relationships between macrotexture parameters and tire-pavement noise and rolling resistance are more complicated. 1.2.5 Network-level Macrotexture Measurements The collection of network-level macrotexture measurements present some unique opera- tional and data processing challenges, which are discussed below. (a) (b) Figure 8. Examples of (a) negative and (b) positive macrotexture (negative skewness indicates negative macrotexture).

16 Protocols for Network-Level Macrotexture Measurement Operational and Environmental Factors Affecting Data Collection The following operational and environmental factors that may affect the various macrotexture parameters were identified: • Speed and acceleration: Macrotexture lasers record data in the time domain, and varying the speed above or below a specified speed will result in either less data or more data being collected over the same distance. The problem is amplified in camera systems, as shutter speed can also significantly affect the accuracy of the data gathered (El Gendy and Shalaby 2007; Huang et al. 2013). Speed also affects the computation of macrotexture parameters, as these are spatial- based parameters (ISO 13473-1 [2019]). • Ambient light: Ambient light may influence the data collection process (Huang et al. 2013). Excessive ambient light can pose issues for devices that rely on transmitting or projecting a light and sensing the contrast between the projected light and the pavement surface. • Pavement color: It is well known that outliers and dropouts in laser measurements occur more often on shiny, darker pavements (ISO 13473-1 [2019]), such as new binder-rich hot-mix asphalt (HMA). Pavement markings, road debris, and/or shiny aggregates may fall under this same category. • Age: Pavement macrotexture properties change over time through weathering and traffic. Stroup-Gardiner et al. (2003) found a significant decrease in IFI measurements within 6 months of opening asphalt test sections to traffic. Bueno et al. (2011) recommended that efforts should be made to minimize the effects of age on the testing program and to evaluate the change in macrotexture over time to determine an appropriate interval of data gathering. • Pavement distresses: Cracking, spalling, raveling, and other distresses could affect macro- texture measurements and the computed macrotexture parameters (Tsai and Wang 2015). Distresses similar in size to the pavement-tire interface are typically defined as megatexture and should not be included in macrotexture calculations (Hall et al. 2009). • Transverse variability: This parameter has been identified as important (ISO 13473-1 [2019]), in particular between wheelpath and non-traveled areas (Kargah-Ostadi and Howard 2015). • Surface moisture: Moisture has a significant impact on the performance of the laser used to measure macrotexture. ISO 13473-1 (2019) recommends conducting testing at various levels of moisture conditions (e.g., dry, wet, and damp) to define the acceptable level of moisture on the road during data collection. • Vibrations: Vibrations can affect HSLE and 3D scanning systems. ISO 13473-1 (2019) lays out vibration requirements for contactless equipment. • Direction of data collection: All 2D stationary profiling equipment can be set up to gather measurements in a longitudinal, transverse, or any intermediate direction relative to travel. Variability in macrotexture based on scanner orientation is one reason the C.T. Meter tests profiles along a circular path of the test surface. Furthermore, when a line laser is oriented in the direction of travel, a measured profile overlaps the previous profile by some amount, potentially providing an averaging function for macrotexture measured. When the line laser is mounted in a direction perpendicular to the travel direction, there is no data overlap. • Sensor specification: The sensor used should be appropriate to the range measured. In the case of laser-based devices, all sensors have a measurement range (the interval within which the sensor can reliably measure displacement) and a standoff distance (the middle of the standoff range). Appropriate vertical resolution and sampling rate are also critical factors for gathering useful macrotexture data. The age of the sensor was noted as being a factor for obtaining accurate data by at least one agency from the results of the survey performed for this project. Furthermore, sensors used should be available from the manufacturer over the long-term in case replacement sensors are needed, and maintenance should be readily available and timely. These conditions suggest the use of commercial off-the-shelf sensors

Introduction 17   or sensor packages. Laser ranging equipment should be calibrated according to the manu- facturer’s directions. • Lens condition: The laser ranging equipment lens should be clean for proper data gathering. This indicates a need for rugged, road-worthy sensors or special protective enclosures for the sensors. • Vehicle alignment in the path of interest: Measurements can be made in wheelpaths (where the macrotexture is often smoother), in non-wheelpaths, or across the entire width of the lane. When wheelpath or non-wheelpath information is desired, the vehicle must maintain proper transverse alignment in the lane. This can be difficult to achieve in curves, traffic weaves, or in sections such as ramps, where lanes can be wider. Filtering Methods Computation of accurate macrotexture parameters requires that data be appropriately filtered and corrected for outliers and dropouts. Dropouts are locations that do not have the necessary intensity to be read by the laser sensor after rebounding from the surface. Data for such areas are typically “filled-in” via interpolation by using valid data from before and after the dropout loca- tion. Outliers are normally associated with spikes, which are potential errors in measurements that do not seem to be within a reasonable range for the surface being measured. Both hardware and software filters are available for addressing these issues. Spikes are a particularly pervasive issue for laser devices and can significantly affect the cal- culation of the MPD (Goubert and Bergiers 2012; Katicha et al. 2015; Perera and Wiser 2013). Goubert and Bergiers (2012) proposed a simple method, which has been adopted by ISO 13473-1 (2019), to identify the spikes based on the absolute value of the difference between two consecu- tive profile elevation readings. The method suggests that a measurement zi may be a spike if: x, (4)1− ≥ α ∆−z zi i where zi = the amplitude of the profile at point i, Δx is the sample spacing, and α = a coefficient between 2 and 10 that is selected based on trial and error. Adjusting the value of α adjusts how many spikes are removed. If a value is selected that is too low, the entire profile will be artificially smoothed out as valid larger changes are removed. If the value is too high, points that should have been identified as spikes will not be removed. The method requires post-processing and intensive review of data. Katicha et al. (2015) observed that differentiating between a spike and a valid measure- ment could be done automatically relative to the level of pavement macrotexture. Katicha’s approach consists of first determining the distribution of valid macrotexture measurements and then identifying the measurements that are outliers for the designated distribution. This is accomplished via the false discovery rate, which adapts itself to the incoming data stream. This eliminates the need for the user to subjectively select a coefficient that establishes a threshold for outlier identification, which may not be sustainable for large-scale macrotexture mea- surements. The method was successfully tested on asphalt pavements that had a broad range of macrotexture on the Virginia Smart Road using an HSLE. One limitation of the method is that it needs modification to work on longitudinally tined, ground, or grooved concrete. However, the main reason for not obtaining accurate measures on these types of surfaces is the inability of a single-spot laser to measure such surfaces rather than the method used to identify the spikes.

18 Protocols for Network-Level Macrotexture Measurement 1.3 Survey of State Highway Agencies A survey to determine the state of the practice on measuring macrotexture and its use for network-level pavement management by state highway agencies was developed and conducted using Virginia Tech’s survey website. The invitation to participate in the survey (via transmittal letter) and other explanatory material, including the survey questions, were submitted for approval to the National Cooperative Highway Research Program. The invitation to complete the survey was sent to all 50 states, Washington, D.C., and Puerto Rico. The survey was sent to an identified individual in each highway agency. Responses were received from the states high- lighted in green in Figure 9. In general, the questions covered the following topics: 1. Demographic information, including the respondent’s job title and responsibility; 2. Whether the agency collects macrotexture measurements at the project and network levels, the equipment used for measurements, the macrotexture parameters that are computed from the data, and procedures the state uses to check if the equipment is functioning satisfactorily; 3. Current and planned uses of macrotexture measurements at the project and network levels; 4. Documented and potential benefits of network-level macrotexture measurements; and 5. Information from network-level macrotexture measurements that can be used for supporting pavement management decisions and the performance measures used by the agency. Figure 9. States with agencies that responded to the survey.

Introduction 19   The responses to the questions were considered in determining good practices on macro- texture data collection, appropriate processing approaches, and the most suitable macrotexture characterization parameters. Some gaps in common understanding concerning what macro- texture is and how it relates to network management, were also revealed in responses. The following sections present a summary of the information gathered in the survey. 1.3.1 States That Collect Network-Level Macrotexture Data Of the respondent states, those shown in green in Figure 10 reported collecting network-level macrotexture data. The states shown in yellow indicated they have stopped collecting data and provided the following reasons: non-availability of human resources, inaccuracies in measure- ments, and data not providing information that was considered valuable at the network level. Several respondent states reported that they do not collect macrotexture data at the network level. Among these respondents, four states reported that they did not believe the data collec- tion technology was mature enough, seven states reported that the cost of data collection was an obstacle, and 10 states indicated the lack of an available workforce to collect the data. In general, the rationales provided for not collecting network-level macrotexture data could be summarized as follows: • Not yet a part of their formal data collection procedures; • Equipment issues; Figure 10. Macrotexture data collection status on the network level.

20 Protocols for Network-Level Macrotexture Measurement • Lack of use of macrotexture data in favor of microtexture information provided by friction testing (limited resources are dedicated to friction testing instead); and • Pavement features such as tining are considered to provide adequate macrotexture. In response to a question about when macrotexture measurements are performed, the major- ity of states indicated “while profiling the pavement.” Several states reported performing macro- texture measurements “while collecting friction data,” indicating that an additional sensor is attached to the friction tester, and macrotexture data is collected at the same frequency as friction data (usually annually or less than annually for the entire network). Other states indicated that macrotexture data is collected at the project level on specific surfaces, such as newly installed high-friction courses or on micro-milled surfaces. 1.3.2 States That Collect Project-Level Macrotexture Data Figure 11 shows the states that reported collecting project-level macrotexture measurements. Five states noted that they felt the technology was not yet mature for data collection, 11 states cited cost as being a deterrent, and 12 states indicated a lack of available human resources as reasons for not collecting macrotexture data at the project level. The reasons provided for not collecting macrotexture data at the project level were similar to the reasons for not collecting data at the network level, with the following additional reasons: • No trigger levels (i.e., watch, action) were established for macrotexture; • Different sensor setup between network- and project-level applications gave different results, thereby casting doubt on macrotexture measurements; and Figure 11. Macrotexture data collection status on the project level.

Introduction 21   • Macrotexture data is collected but only for projects such as applications of High-Friction Courses, where a macrotexture parameter is used for project acceptance. 1.3.3 Equipment Used to Collect Macrotexture Data Table 2 summarizes the equipment used by responding states to collect macrotexture data. The most common methods for collecting data used the C.T. Meter for project-level data and the single-spot laser for network-level data; however, several states indicated that they use 3D laser systems. 1.3.4 Parameters Used to Describe Macrotexture When asked which parameters were used to characterize macrotexture data, the majority of states collecting data reported using the MTD and the MPD. Several states also use the RMS. Table 3 summarizes the states’ responses to the question. 1.3.5 Uses of Macrotexture Data When asked, “How does your agency currently use macrotexture measurements?” states responded fairly equally that they currently use the measurements as a complement to friction Macrotexture Method Network Level Project Level Volumetric Sand Patch 1 6 Grease Patch 0 0 HydroTimer 0 2 Manual Profile Recorder 0 0 Stationary Laser Systems C.T. Meter 1 7 ELAtextur 0 0 Laser Texture Scanner 0 3 DSRM 0 0 Stationary Optical Imaging Systems Stereo Vision System 0 0 Photometric Stereo 0 0 Pavement Surface Imager Mark ½ 0 0 Walking-Speed Laser System ARRB Walking Profiler 0 0 TM2 Texture Meter 1 2 Robotex 0 0 High-Speed Laser Equipment (HSLE) HSLE-SSL (HSLE with Single-Spot Laser) 10 4 HSLE-LL (HSLE with Line Laser) 1 1 3D Laser/Camera 7 1 Other Florida Texture Meter 0 1 ARAN Pave 2DLaser Rut Measurement System (64 kHz) 0 1 In-House 3DSystem 0 1 Contour/Tire Depth Gauge for Tined and Grooved Surfaces 0 1 Table 2. Summary of equipment used to gather macrotexture data.

22 Protocols for Network-Level Macrotexture Measurement measurements (i.e., as a screening tool for problem areas where macrotexture is low and friction demand is high), for safety reasons (i.e., at specific locations in response to incidents), to add the information to their pavement management systems, and for project acceptance (i.e., for projects with specific macrotexture requirements such as open-graded friction courses [OGFC], dense-graded friction course [DGFC], high-friction surface treatments [HFST], and Portland cement concrete [PCC] surfaces). The macrotexture information is currently used to a lesser extent to characterize mixes and surface treatments. Surface characterization includes differ- entiating between mixes with differing characteristics such as gradation, nominal maximum aggregate size, binder content, and voids in the mineral aggregate. The survey was structured to differentiate between what is done with macrotexture data today versus what practitioners would like to do with the data. Some states indicated that in the future, they would likely start using the information to complement friction measurements for site-specific safety investigations and to populate their pavement management systems. Far fewer agencies reported plans to use macrotexture information for project acceptance on surfaces with macrotexture requirements; however, this is likely because states with such plans already use the data for this purpose. Some agencies would like to use macrotexture param- eters to characterize various mixes and surface treatments (seven states), compared to states that currently use the information for this purpose (two states). This finding indicates that several states may feel they are missing this information for their current surfaces and would like to be able to use the information. When asked for documented benefits, several states indicated that they collect macrotexture data but do not currently use the data. This response is encouraging, because the data are collected and seen as valuable; however, education on the uses of macrotexture data and clearer standards on what constitutes appropriate levels of macrotexture for safety purposes appear to be in order. One state also noted the benefit of collecting macrotexture data in areas where collecting friction data is problematic (i.e., curves). At the project level, two states reported using the macrotexture information to characterize surface mixes, with one state needing the information for dispute resolution on uniformity requirements on an ultra-thin bonded HMA surface treatment. 1.3.6 Other General Observations from the Survey When asked where on the pavement macrotexture data is collected, the vast majority of responding agencies (seventeen states) indicated collecting it in the rightmost travel lane with far fewer (seven states) reporting collecting it in other lanes. Agencies are much more likely to collect the information on other lanes at the project level (six states) rather than at the network Parameter Network Level Project Level Mean Texture Depth (MTD) 7 7 Mean Profile Depth (MPD) 7 6 Root Mean Square (RMS) 2 3 Estimated Texture Depth (ETD) 0 3 Other Skewness, Kurtosis, PSD 1 0 Digital Sand Patch (LCMS)* 1 0 * LCMS = Laser Crack Measurement System. Table 3. Summary of macrotexture parameters used by state DOTs.

Introduction 23   level (one state). This indicates that macrotexture data is currently collected at the network level in the travel (slow) lane in much the same way as other network-level data, such as IRI or distress information, are collected. The vast majority of agencies that collect macrotexture data (19 states) do not have a formal quality management plan for the collected data. Similarly, most states do not review any of the collected data as part of a data collection QA plan, with four states indicating they review less than 10% of the data and only two states reporting reviewing more than 10% of the collected data. Most of the states that do collect macrotexture data do perform equipment calibration or test the equipment on surfaces with known characteristics to test the equipment for proper functioning. When asked if the agencies envisioned additional changes in data collection activities over the next several years, more agencies responded that they did envision changes (13 states) than responded that they did not (10 states), with many agencies (13 states) responding that they were unsure. Agencies indicated that they were planning to add a macrotexture sensor to future equipment purchases (i.e., inertial profiler, friction-measuring equipment). One state plans to use 3D data collected from Laser Crack Measurement System (LCMS) measurements along with friction measurements to identify high-risk curves and with accident data to identify areas that require treatment; however, the state is looking for correlations between C.T. Meter readings, parameters from LCMS data, and friction readings before they move forward with the plan. One state plans to set minimum macrotexture parameter values, especially for PCC, where macrotexture can be used instead of tine-depth specifications. Another state plans to replace cumbersome sand patch tests with a 3D laser texture scanner. Several states indicated they would look at ways to report macrotexture parameters in more useful ways (e.g., using Geographic Information Systems for easier visualization and tracking of the parameters over time). Several states indicated 3D macrotexture measurements as being the next level of data granularity they are seeking to obtain. In providing final comments, many states indicated the need for standardized macrotexture data collection and analysis procedures. They are also seeking consensus on parameters/indices to use for macrotexture and seeking improvements to their QA and quality control (QC) processes.

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Macrotexture, which influences vehicle-roadway skid resistance, refers to the texture of the pavement due to the arrangement of aggregate particles. Pavement surfaces are subjected to seasonal variations, and over time the embedded aggregates become polished due to the onslaught of traffic. Research has shown that wet-weather crashes are influenced by the macrotexture of the pavement surface.

The TRB National Cooperative Highway Research Program's NCHRP Research Report 964: Protocols for Network-Level Macrotexture Measurement provides state transportation pavement engineers and other practitioners with recommended protocols for macrotexture test measures, equipment specifications, and data quality assurance practices.

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