Safety Analysis Opportunities Using Pavement 

Surface Characterization Based on 3D Laser Mapping 

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Safety Analysis Opportunities Using Pavement Final Report
	

Surface Characterization Based on 3D Laser Imaging September 2017
	

1. Report No. 

FHWA-SA-17-046 

2. Government Accession No. 

Technical Report Documentation Page 

5. Report Date 

September 2017 

6. Performing Organization Code 

3. Recipient’s Catalog No. 

4. Title and Subtitle 

Safety Analysis Opportunities Using Pavement Surface Characterization 

Based on 3D Laser Imaging 

7. Author(s) 

Kelvin C. P. Wang, Qiang “Joshua” Li 

9. Performing Organization Name and Address 

School of Civil and Environmental Engineering 

Oklahoma State University (OSU) 

HSA 

8. Performing Organization Report No. 

10. Work Unit No. (TRAIS) 

HSST 

11. Contract or Grant No. 

RFQ 50-53-14064 

13. Type of Report and Period Covered 

Safety Analysis Report 

March 2014 to March 2016 

14. Sponsoring Agency Code 

HSA 

Stillwater OK 74078 

12. Sponsoring Agency Name and Address 

Federal Highway Administration - Office of Safety 

U.S. Department of Transportation 

1200 New Jersey Avenue, SE 

Washington, DC0590 

15. Supplementary Notes 

Joseph Cheung (joseph.cheung@dot.gov), Office of safety served as the Technical Manager for FHWA. The following 

FHWA staff members contributed as technical working group members, reviewers and/or provided input or feedback to 

the project at various stages: Frank Julian, Jim Sherwood, Andy Mergenmeier, Mike Moravec (retired), Mark Swanlund. 

16. Abstract 

Pavement surface properties play critical roles in maintaining proper interaction between tire and pavement, and 

necessary drainage runoff for both pavement and safety management. Currently, data collection and analysis of 

pavement surface properties, such as texture and profiling, have primarily relied on single line of measurements using 

multiple vehicles to obtain a comprehensive evaluation of pavement surface properties. This project serves to 

demonstrate the use of state-of-the-art 3-D Ultra Laser imaging data collection methodology with integrated data 

platform, higher data resolution, full-width pavement coverage, less interruption to traffic, and more robust software 

solutions will result in a more efficient and cost-effective data collection process and may potentially lead to new 

measurement standards. Specifically, three applications based on 3D Ultra have been investigated in this study for 

various aspects of pavement surface safety analysis: 

• Effectiveness and performance of High Friction Surface Treatment (HFST) at a national scale, 

• Investigation of geometric texture indicators for pavement safety with 1mm 3D data, 

• Evaluation of pavement surface hydroplaning with 1mm 3D data. 

17. Key Words 

pavement safety, pavement texture, High Friction surface Treatments, 

pavement friction, surface characteristics, 3D Laser Imaging, 

hydroplaning 

18. Distribution Statement 

No restrictions. 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 63 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 

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TABLE OF CONTENTS
	

1.		 INTRODUCTION .............................................................................................1
	

2.		 DATA COLLECTION DEVICES ...................................................................3
	

PaveVision3D Ultra.............................................................................................................. 3
	

AMES 8300 High Speed Profiler......................................................................................... 4
	

Dynatest 6875H Highway Friction Tester.......................................................................... 4
	

3.		 EFFECTIVENESS AND PERFOMANCE OF HFST AT A NATIONAL
	

SCALE ................................................................................................................6
	

Introduction .......................................................................................................................... 6
	

HFST Data Collection .......................................................................................................... 6
	

Pavement Surface Characterization ................................................................................... 7
	

Evaluation of HFST Effectiveness .................................................................................... 10
	

HFST Friction Performance.............................................................................................. 14
	

4.		 GEOMETRIC TEXTURE INDICATORS FOR SAFETY ANALYSIS ...21
	

Introduction ........................................................................................................................ 21
	

Geometric Texture Indicators ........................................................................................... 22
	

Correlation Analysis........................................................................................................... 27
	

Pavement Friction Model Development and Case Study ............................................... 30
	

5.		 EVALUATION OF PAVEMENT SURFACE HYDROPLANING ...........37
	

Introduction ........................................................................................................................ 37
	

Hydroplaning Prediction Models...................................................................................... 37
	

Data Preparation ................................................................................................................ 41
	

Case Study........................................................................................................................... 44
	

6. SUMMARY ......................................................................................................48
	

REFERENCES.......................................................................................................50
	

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LIST OF FIGURES
	

Figure 2.1. PaveVision3D Ultra...................................................................................................... 3
	

Figure 2.2 AMES 8300 high speed profiler.................................................................................... 4
	

Figure 2.3 Dynatest 6875H highway friction tester........................................................................ 5
	

Figure 3.1. HFST sites .................................................................................................................... 7
	

Figure 3.2. Example friction data ................................................................................................... 8
	

Figure 3.3. Example MPD data...................................................................................................... 9
	

Figure 3.4. Average friction numbers and MPDs for HFST sites ................................................ 12
	

Figure 3.5. Average rutting for HFST sites .................................................................................. 13
	

Figure 3.6. HFST friction performance vs. installation age and average temperature ................. 16
	

Figure 3.7. HFST friction performance vs. aggregate type ......................................................... 17
	

Figure 3.8. HFST friction performance prediction ...................................................................... 20
	

Figure 4.1. Schematic diagram of pavement surface characterization techniques ...................... 22
	

Figure 4.2. A general procedure for MPD calculation................................................................ 23
	

Figure 4.3. Spatial parameters (a) anisotropic; (b) isotropic ....................................................... 25
	

Figure 4.4. Schematic diagram of the interfacial area ................................................................. 26
	

Figure 4.5. 3D rendering of rigid pavement test specimens from 3D Ultra ................................. 28
	

Figure 4.6. 3D rendering of flexible pavement test specimens from 3D Ultra............................ 28
	

Figure 4.7. Correlation analysis results with MPD...................................................................... 29
	

Figure 4.8. Correlations analysis results with RMS .................................................................... 30
	

Figure 4.9. Correlation analysis results with skewness ............................................................... 30
	

Figure 4.10. Correlation result between Kurtosis and SBI .......................................................... 30
	

Figure 4.11. AL-I 65 HFST test site ............................................................................................ 31
	

Figure 4.12. Friction measurement results on AL I 65 ramp....................................................... 32
	

Figure 4.13. Correlation results between the predicted and measured FNs................................ 32
	

Figure 4.14. Comparison of the measured and predicted FNs from six texture indicators ......... 33
	

Figure 4.15. Residual plots of the four variables......................................................................... 34
	

Figure 4.16. Non-linear models ................................................................................................... 35
	

Figure 4.17. Correlation results between the predicted and measured FNs................................. 36
	

Figure 5.1. Schematic diagram of cross slope, longitudinal grade, and flow path ...................... 38
	

Figure 5.2. Vehicle travelling on pavements with (a) longitudinal grade; (b) horizontal curve.. 39
	

Figure 5.3. Sensitivity of improved hydroplaning models .......................................................... 41
	

Figure 5.4. Cross slope calibration based on IMU and 1mm 3D data ......................................... 43
	

Figure 5.5. Software interface for automated hydroplaning prediction....................................... 43
	

Figure 5.6. Pavement geometry of the testing site....................................................................... 45
	

Figure 5.7. WFDs and EMTDs of test site................................................................................... 45
	

Figure 5.8. Detection of potential hydroplaning risk................................................................... 47
	

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LIST OF TABLES
	

Table 3.1. Paired t-test results for friction numbers and MPDs.................................................... 10
	

Table 3.2. Potential influencing factors of HFST friction performance ....................................... 15
	

Table 3.3. Multivariate analysis results ........................................................................................ 18
	

Table 4.1. Multivariate regression results from the six texture indicators.................................... 33
	

Table 4.2. Multivariate regression results from MPD, Skewness, TAR, and SBI........................ 33
	

Table 4.3. Multivariate regression results from Skewness, TAR, NEW_MPD and NEW_SBI .. 35
	

Table 5.1. Precipitation in Spavinaw station ................................................................................ 44
	

Table 5.2. Sample 3D and IMU data for hydroplaning speed calculation.................................... 46
	

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1. INTRODUCTION
	

Pavement surface properties play critical roles in maintaining proper interaction between tire and 

pavement, and necessary drainage runoff for both pavement and safety management. Currently, 

data collection and analysis of pavement surface properties, such as texture and profiling, have 

primarily relied on single line of measurements. Frequently highway agencies have to use 

multiple vehicles to obtain a comprehensive evaluation of pavement surface properties. Further, 

despite of continuous improvements of sensing methodologies in the past decades, many of the 

instrumentations used today are still based on decades old technologies. Therefore implementing 

art-of-the-state data collection methodology with integrated data platform, higher data resolution, 

full-width pavement coverage, less interruption to traffic, and more robust software solutions 

will result in a more efficient and cost-effective data collection process and may potentially lead 

to new measurement standards. 

This project is a response to the requisition number RFQ 50-53-14064 solicited by the Federal 

Highway Administration (FHWA). The 3D laser imaging technology, named as PaveVision3D 

Ultra (3D Ultra for short) developed by the WayLink Systems Corporation in collaboration with 

the Oklahoma State University (OSU), is used in this study for multiple safety and pavement 

evaluation purposes. 3D Ultra is designed on a single-pass and complete lane-coverage platform 

for data collection on roadways capable of operating at highway speeds up to 60mph (96.5 km/h) 

at 1mm resolution, and can collect data for automated pavement measurements for texture, 

smoothness, friction, and distresses with necessary software tools. Specifically, three 

applications based on 3D Ultra have been investigated in this study for various aspects of 

pavement surface safety analysis: 

	 Effectiveness and performance of High Friction Surface Treatment (HFST) at a 

national scale: Although HFST has been widely installed in recent years in many states, 

validation efforts considering various aggregate types and the bonding materials, 

installation ages, environmental conditions, and traffic volumes are not as comprehensive 

as desired. The use of high-speed data collection systems as demonstrated in this project 

may improve such validation efforts. . Utilizing 3D Ultra and FHWA’s fixed-slip 

continuous friction tester, this study collects comprehensive pavement surface data at 21 

HFST sites in 11 states at highway speeds. Measurements on HFST and untreated 

pavements are compared to determine the effectiveness of HFST. Multivariate analyses 

are conducted to investigate the impacts of various factors listed above on HFST friction 

performance. Through the use of 3D Ultra, friction models are developed to aid highway 

agencies in managing HFST. 

	 Investigation of geometric texture indicators for pavement safety with 1mm 3D 

data: Surface texture and friction are two primary characteristics for pavement safety 

evaluation. Understanding their relationship is critical to reduce potential traffic crashes 

especially in wet conditions. Currently Texture data obtained from existing systems are 

limited to either a small portion of the pavement surface or one single line of longitudinal 

profile, and the currently used texture indicators, such as Mean Profile Depth (MPD), and 

Mean Texture Depth (MTD) only reveal partial aspects of texture properties of interest. 

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With the 1mm 3D data collected from 3D Ultra, four types of texture indicators 

(amplitude, spacing, hybrid, and functional parameters) are calculated to represent 

various texture properties for pavement friction estimation. The relationships among 

those texture indicators and pavement friction are examined. The contributing texture 

parameters are identified for pavement friction prediction, and subsequently a 

multivariate regression pavement friction model is developed to aid the evaluation of 

pavement safety for project- and network- level pavement surveys. 

	 Evaluation of pavement surface hydroplaning with 1mm 3D data: During high 

intensity rainfall events, hydroplaning may occur and affect driver safety especially on 

sharp curves. Past studies indicate that an increase in hydroplaning risk occurs with an 

increase of the Water Film Depth (WFD), which is dependent on surface texture 

properties, flow path slope, flow path length, rainfall intensity, and pavement surface 

type. However, little research work has been conducted to investigate pavement surface 

drainage at network levels because the existing data acquisition systems are incapable of 

continuously measuring related data sets at high speeds. This application utilizes 1mm 

3D texture data continuously collected by 3D Ultra, and roadway geometric data acquired 

with an Inertial Measurement Unit (IMU) system for the prediction and evaluation of 

pavement surface hydroplaning risk. Due to the fact that the presence of longitudinal and 

cross slopes would decrease the wheel load of vehicles perpendicular to the pavement 

surface and increase hydroplaning risk, two improved models based on the existing 

Gallaway and University of South Florida (USF) models are presented in this study. 1mm 

3D pavement surface data is used to estimate texture information for the models in lieu of 

traditional spot-laser based texture measurement devices. A 4.35 km pavement section 

with five horizontal curves is selected to investigate and compare the speeds that lead to 

hydroplaning predicted from Gallaway and USF models, and the two improved models. 

Through this effort, pavement segments with potential hydroplaning risk are identified by 

comparing predicted speeds with posted speed limits. 

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2. DATA COLLECTION DEVICES 

PaveVision3D Ultra 

The PaveVision3D laser imaging system has evolved into a sophisticated system to conduct full 

lane data collection on roadways at highway speeds up to 60mph (96.5 km/h) at 1mm resolution. 

Figure 2.1 demonstrates the Digital Highway Data Vehicle (DHDV) equipped with 

PaveVision3D Ultra, which is able to acquire both 3D laser imaging intensity and range data 

from pavement surfaces through two separate sets of sensors. Recently, two 3D high resolution 

digital accelerometers have been installed on the system, capable of reporting compensated 

pavement surface profiles and generating roughness indexes. The collected data are saved by 

image frames with the dimension of 2,048 mm in length and 4,096 mm in width. In summary, 

the 1mm 3D pavement surface data can be used for: 

 Comprehensive evaluation of surface distresses: automatic and interactive cracking 

detection and classification based on various cracking protocols; 

 Profiling: transverse for rutting and longitudinal for roughness (Boeing Bump Index and 

International Roughness Index); 

	 Safety analysis: including macro-texture in term of mean profile depth (MPD) and mean 

texture depth (MTD), hydroplaning prediction, and grooving identification and 

evaluation; 

	 Roadway geometry: including horizontal curve, longitudinal grade and cross slope. 

Figure 2.1. A PaveVision3D Ultra data collection vehicle used for the research 

In addition, an Inertial Measurement Unit (IMU) with high accuracy has been integrated and 

synchronized into the 3D Ultra data vehicle for geometrical information capture. IMU is a selfcontained sensor consisting of accelerometers, fiber-optic gyroscopes, and integrated GPS 

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antennas, whose data contain GPS coordinates, horizontal curve, longitudinal grade, and cross 

slope, and are utilized for hydroplaning speed prediction. 

AMES 8300 High Speed Profiler 

The AMES Model 8300 High Speed Profiler is designed to collect macro surface texture data 

along with standard profile data at highway speeds. Multiple texture indexes such as Mean 

Profile Depth (MPD) can be calculated from the testing data. This High Speed Profiler meets or 

exceeds the following requirements: ASTM E950 Class 1 profiler specifications, AASHTO PP 

51-02 and Texas test method TEX 1001-S. The texture specifications of the Profiler include: 

 Capable of collecting measurements at speeds between 25 and 65 mph 

 Laser height sensor with a range of 180 mm and a resolution of 0.045 mm 

 Horizontal distance measured with an optical encoder that has a resolution of 1.2 mm 

 Pavement elevation sampling rate 62,500 samples per second 

 Profile wavelength down to 0.5 mm 

Figure 2.2 AMES 8300 high speed profiler used to collect Macro 

Surface Texture data and profile data 

Dynatest 6875H Highway Friction Tester 

The Federal Highway Administration (FHWA) is offering demonstrations of its Dynatest 6875H 

Highway Friction Tester (HFT) to state departments of transportation (DOTs). The HFT is a selfcontained testing vehicle that maps friction at one-foot intervals continuously along a pavement 

section. Agencies can use friction data provided by the HFT for both network-level and projectlevel applications. Continuous friction testing can improve agencies’ ability to measure friction 

through intersections and around curves, regardless of radius. As well as providing continuous 

friction testing, the HFT uses a fixed-slip test method to deliver a coefficient of friction more 

representative of conditions experienced by vehicles with modern anti-lock braking systems 

(ABS). 

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Figure 2.3 A Dynatest 6875H highway friction tester that maps friction continuously 

along a pavement section. 

(From http://www.thetranstecgroup.com/fhwa-providing-friction-tester-demonstrations/) 

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3.		EFFECTIVENESS AND PERFOMANCE OF HFST AT A NATIONAL 

SCALE 

Introduction 

High Friction Surface Treatments (HFST) were firstly applied in the United Kingdom in 1960s 

to maintain pavement skid resistance and reduce the fatalities and injuries from crashes that 

occur at or near horizontal curves [1]. Recently, the Surface Enhancements At Horizontal Curves 

program (SEaHC) administered under the FHWA Every Day Counts 2 program, HFST were 

installed at numerous horizontal curves throughout the U.S. due to higher friction demand of 

vehicles on curves than that on other pavement sections [2]. Through various HFST projects, the 

effectiveness of HFST in improving skid resistance and reducing crashes has been demonstrated 

[3, 4, 5, 6, 7, and 8]. Generally pavement friction and macro-texture are tested before and after 

HFST installation to quantify the changes in surface skid resistance. Pavement friction is 

measured primarily using the Dynamic Friction Tester and agency-owned locked-wheel skid 

testers, while macro-texture is measured using stationary or low speed devices such as the 

Circular Track Meter, ASTM E 965 “Sand Patch” Method, or RoboTex [4, 7]. Most of these 

devices require lane closure to perform the tests, and highway agencies must perform multiple 

data collection processes to gather different pavement surface characteristics. These limitations 

have constrained continuous evaluation and monitoring of the surface characteristics of installed 

HFST sites in the longer term after they are opened to traffic. In addition, based on our literature 

search, no study has been conducted to evaluate HFST performance at a national scale under 

various traffic conditions, environments, and HFST materials. 

HFST Data Collection 

HFST Sites 

This 3D Ultra technology, which offers a single-pass and complete lane coverage platform at 

prevalent traffic speed, provides an ideal solution to evaluate the surface characteristics of HFST 

without interrupting traffic [9]. In addition, pavement friction data on HFST is collected using 

the FHWA fixed-slip continuous friction tester, which uses a standardized smooth-tread test tire 

to measure friction in terms of a unitless friction number, Mu. 

The data collection effort described herein includes pavement friction and surface characteristics 

testing of 21 HFST sites in 10 states as instructed by FHWA. The locations of the HFST sites are 

shown in Figure 3.1. Considering both directions and total number of lanes, 41 data collection 

events were conducted, with each 3D data collection covering a full traffic lane. To determine 

the effectiveness of HFST in improving surface properties, surface characteristics including 

pavement cracking, rutting, macro-texture, friction, roadway geometry are measured for each 

HFST site. All data were collected beginning from 300 ft to 500 ft before the target HFST road 

section and 300 ft to 500 ft after each HFST section to capture these data for the existing 

pavement as well. 

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Figure 3.1. HFST sites 

Pavement Surface Characterization 

To determine the effectiveness of HFST in improving surface properties, the following surface 

characteristics were measured on-site at each HFST site before, within, and after each HFST 

section. 

 Pavement cracking, 

 Pavement rutting, 

 Pavement macrotexture, 

 Pavement friction, 

 Roadway geometry. 

Pavement Cracking 

The AASHTO Designation PP67-10 outlines the procedures for quantifying cracking distresses 

at the network level [10]. The protocol is designed for fully automated surveys, while minimal 

human intervention is needed in the data processing. Three cracking types: transverse cracking, 

longitudinal cracking, and pattern cracking, are defined based on the orientation of the cracking 

spanning. The five traffic zones divide the entire lane coverage into wheel-path and non-wheel 

path areas. The total cracking length and average cracking width of each cracking type are 

reported for each zone. No cracking was observed on the pavement surface for majority of the 

HFST sites. Cracks were found only on two older HFST sites. 

Pavement Rutting 

Rutting is defined as the permanent traffic-associated deformation within pavement layers. The 

recent provisionally-approved AASHTO Designation PP69-10 [11] has been implemented into 

the 3D Ultra system for rutting characterization and cross slope measurements. Rutting in the left 

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and right wheelpaths are averaged into average rutting in inches for each image frame for each 

data collection. Rutting data are not calculated for rigid pavement sections, which are 

represented with zero rutting values. 

Pavement Friction 

Skid resistance is the ability of the pavement surface to prevent the loss of tire traction. The 

friction value from the HFST was reported every 1 ft over the length of the section tested in 

order to show any variations in friction. Example friction data plots are shown in Figure 3.2. 

Some sites show clear improvements of skid resistance (as shown in Figure 3.2a) and the 

differentiation of the HFST section from abutting pavement, while others don't show any trend 

(as shown in Figure 3.2b). 

Figure 3.2. Example friction data
	

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Pavement Macro-texture 

The methodologies for texture measurement can be grouped into two categories: static and highspeed methods. Traditionally the measurement of pavement macro-texture at high-speed is based 

on single line measurement of longitudinal profile in the wheelpath. Mean Profile Depth (MPD) 

is one of the widely used texture indexes. Example MPD data plots from the 3D Ultra system are 

shown in Figure 33. Similar to friction data, some sites exhibit much higher MPD values on 

HFST surface in contrast to the abutting pavement, while others don't exhibit noticeable 

differences. 

Figure 3.3. Example MPD data
	

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Roadway Geometry 

The Inertial Measurement Unit (IMU) mounted on the PaveVision3D data collection vehicle can 

measure the Euler angles, which include roll (Euler angle about x-axis), pitch (Euler angle about 

y-axis) and yaw (Euler angle about z-axis). The roll angle is widely accepted to represent 

pavement cross slope, and pitch angle is widely used to represent the pavement longitudinal 

grade. The cross slope, longitudinal grade and radius of each HFST site are calculated based on 

collected IMU data. 

Evaluation of HFST Effectiveness 

The 1mm 3D data are collected 300ft to 500ft before and/or after each HFST section so that the 

measured surface characteristics before, after, and on the HFST sites can be compared and 

statistical analyses performed to determine the effectiveness of the HFST sites in improving 

surface characteristics. The beginning and end locations of each HFST section are determined 

based on “event markers” manually recorded by the field friction data collection crew, and 

visually from collected 3D data sets. A paired t-test with equal variance is performed for each 

HFST site. The t-test investigates the difference between the means of the non-HFST and HFST 

treatment sections. At 95% confidence interval, if P-value is smaller than 0.05, the null 

hypothesis is rejected and the mean of the two groups are significantly different. 

The friction and MPD data are reported at 1.0 ft interval. The t-test results for friction number 

(FN) and MPD for each data collection are summarized in Table 3.1. It is evident that HFST 

surfaces have significantly different friction numbers (with an average P = 0.01 for all the HFST 

sites) and surface texture MPD values (with an average P = 0.01 for all the HFST sites) than 

those on the abutting pavement. The average friction number of all HFST sites is 86.76, while 

the friction of non-HFST surface has an average of 56.56. The average MPD of all HFST sites is 

0.0522 inches (1.32 mm), while the MPD of non-HFST surface has an average of 0.0410 inches 

(1.04 mm). 

Table 3.1. Paired t-test results for friction numbers and MPDs 

MPDMean MPDMean – P Sig. df 

Data 

Collection 

ID 

FNMean 

– HFST 

FNMean – 

Non-HFST df P 

value 

Sig. 

Diff? 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

98.08 51.79 

103.49 58.04 

87.89 68.3 

81.69 70.38 

107.86 63.86 

112.42 63.85 

104.05 60.55 

98.74 57.76 

100.6 55.84 

91.35 44.1 

102.39 61.77 

94.59 49.12 

57.64 43.83 

83.17 52.64 

65.55 64.93 

64.28 53.3 

1060 

990 

1540 

1541 

1477 

1499 

1088 

984 

1113 

826 

1019 

1194 

1272 

768 

925 

1677 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.38 

0 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

No 

Yes 

–HFST 

0.0498 

0.0514 

0.0548 

0.0526 

0.0625 

0.0634 

0.0513 

0.0497 

0.0517 

0.0501 

0.049 

0.0496 

0.0383 

0.043 

0.0362 

0.0456 

Non-HFST value Diff? 

0.0345 1274 0 Yes 

0.0284 1172 0 Yes 

0.0677 1739 0 Yes 

0.0581 991 0 Yes 

0.0465 1184 0 Yes 

0.0459 957 0 Yes 

0.0342 978 0 Yes 

0.0321 1454 0 Yes 

0.0346 1075 0 Yes 

0.03 691 0 Yes 

0.036 1243 0 Yes 

0.0351 1119 0 Yes 

0.0283 1132 0 Yes 

0.0192 285 0 Yes 

0.0217 351 0 Yes 

0.0332 1600 0 Yes 

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Data 

Collection 

ID 

FNMean 

– HFST 

FNMean – 

Non-HFST df P 

value 

Sig. 

Diff? 

MPDMean 

–HFST 

MPDMean – 

Non-HFST df P 

value 

Sig. 

Diff? 

17 59.14 52.73 1767 0 Yes 0.0291 0.0289 2143 0.41 No 

18 73.12 54.51 831 0 Yes 0.0419 0.0313 616 0 Yes 

19 94.98 60.43 791 0 Yes 0.09 0.0825 1181 0.03 Yes 

20 72.81 54.5 471 0 Yes 0.0351 0.0323 645 0 Yes 

21 68.93 54.32 367 0 Yes 0.0397 0.0257 418 0 Yes 

22 64.49 45.09 244 0 Yes 0.0423 0.0812 303 0 Yes 

23 64.14 37.95 253 0 Yes 0.0539 0.1083 365 0 Yes 

24 84.14 76.23 524 0 Yes 0.0384 0.0498 399 0 Yes 

25 52.1 69.09 1940 0 Yes 0.041 0.0356 1308 0 Yes 

26 56.5 76.24 1995 0 Yes 0.038 0.0383 1814 0.01 Yes 

27 74.62 60.83 900 0 Yes 0.0548 0.0449 844 0 Yes 

28 72.79 55.66 267 0 Yes 0.0562 0.0402 859 0 Yes 

29 101.45 66.22 310 0 Yes 0.0579 0.0465 429 0 Yes 

30 59.5 55.74 414 0 Yes 0.0548 0.0514 769 0 Yes 

31 90.25 41.1 2543 0 Yes 0.0572 0.0293 2100 0 Yes 

32 93.37 42.45 2561 0 Yes 0.0581 0.027 1981 0 Yes 

33 107.81 53.06 1968 0 Yes 0.0592 0.0313 588 0 Yes 

34 108.33 55.36 1734 0 Yes 0.0734 0.0382 1376 0 Yes 

35 102.6 57.24 1069 0 Yes 0.0671 0.0452 1306 0 Yes 

36 92.68 49.37 1635 0 Yes 0.0526 0.0397 1286 0 Yes 

37 111.46 51.08 1468 0 Yes 0.0474 0.035 489 0 Yes 

38 97.06 54.02 969 0 Yes 0.0692 0.0354 1803 0 Yes 

39 124.28 50.44 1962 0 Yes 0.0588 0.0354 1840 0 Yes 

40 88.41 58.68 1864 0 Yes 0.0608 0.0381 1054 0 Yes 

41 88.32 66.57 1369 0 Yes 0.0663 0.0419 989 0 Yes 

Figure 3.4 shows the difference between the two means of friction numbers and MPDs for each 

HFST data collection. The majority of the HFST sites have much higher friction numbers and 

MPD values comparing to the non-HFST surfaces. However, there are several exceptions. Sites 

25 and 26 have smaller friction numbers on HFST surfaces than those on the non-HFST surfaces. 

The presence of foreign materials observed on the two HFST locations during data collection 

may result in the lower friction numbers of the sites. Approximately identical friction numbers 

are observed for Site 15 on HFST and non-HFST surfaces. In addition, the friction on HFST of 

Site 30 is slightly higher than that on the non-HFST surface even though the treatment was 

applied only one year prior to data collection. For MPD, 5 out of 41 collections report smaller 

MPDs on HFST surfaces, such sections including Sites 3, 4, 22, 23, and 24. It is also observed 

that over 1⁄2 of the flint sites have the lowest FN Ratios. 

The comparisons of the average rutting on HFST and non-HFST of asphalt pavement surfaces 

are shown in Figure 3.5. No consistent statistical conclusion can be made based on the t-test 

results: some sections have significantly different rutting while others don't between HFST and 

non-HFST segments. This is logical since HFST treatments do not correct rutting problems on 

existing pavement surfaces. The rutting on an HFST surface is dependent on the pavement 

condition before the treatment. The average rutting are 4.24 mm and 4.53 mm for HFST and 

non-HFST respectively. The average P-value is 0.14, which indicates that on average no 

significant difference is observed for rutting on non-HFST versus HFST surfaces. 

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Figure 3.4. Average friction numbers and MPDs for HFST sites
	

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Figure 3.5. Average rutting for HFST sites 

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HFST Friction Performance 

Potential Influencing Factors 

Influencing factors relating to pavement friction are generally categorized as pavement surface 

characteristics, vehicle operational parameters, tire properties, and environmental factors [12]. 

The influence of asphalt mixture type and Portland cement concrete surface textures on 

pavement friction has been widely researched [13]. Several pavement friction models have been 

developed, some of which are established based on macro- and micro-texture of mix aggregates 

[14]. Operational factors including water film thickness, test speed, or temperature are found to 

affect friction measurement [15, 16]. Studies also find that temperature could affect pavement 

friction in short-term and long-term [17, 18, and 19]. 

Based on data availability in this study, precipitation, average temperature, HFST installation age, 

aggregate type, and annual average daily traffic (AADT) for each HFST site are identified as the 

potential influencing factors to evaluate the HFST friction performance in the long-term and at a 

wider scale. Precipitation and average temperature data are obtained from the climate station close 

to each HFST site. Two indicators, friction number on HFST (FNHFST) and the ratio of friction 

number (FN Ratio) are used to evaluate HFST pavement friction performance. Herein, FN Ratio 

is the friction number on HFST (FNHFST) divided by the friction number on Non-HFST (FNNonHFST). 

ிேಹಷೄ೅ FN Ratio =		 (3.1) ிேಿబ೙షಹಷೄ೅ 

The potential influencing factors and corresponding friction information for each data collection 

are provided in Table 3.2. FN Ratio, FNHFST, and FNNon-HFST for each data collection are evaluated 

considering the aforementioned factors: 

	 As shown in Figure 3.6, FN Ratio and FNHFST show decreasing trend with the increase of 

HFST installation age and average temperature. 

	 Based on the data trend shown in FIG, 6(B), when HFST sites are approximately 60 month 

of installation age, FN Ratio approaches approximately 1.0, which indicates in general that 

the average life of a HFST surface is around 5 years and the benefit of HFST in friction 

effectiveness is nearly lost by that time. However, it should be emphasized that this 

observation is solely based on the monitoring data from a limited number of HFST sites 

included in this study. Moreover, all the sites that are 60 months or older are constructed 

using flint as the aggregates. 

	 HFST sites installed with calcined bauxite aggregates exhibit better friction performance 

than those with flints (Figure 3.7). 

	 No trend is observed between friction performance (FN Ratio and FNHFST) and 

precipitation, AADT respectively, neither does the accumulated traffic repetitions (which 

is AADT times 365 days and HFST installation age). 

	 There is no obvious relationship between FNNon-HFST and the five influencing factors. 

In warmer region, higher temperature may leads to softer polymeric resin binder compared with 

that in colder region, and generates lower FN Ratio and FNHFST. As time goes by, older installation 

endures more influence of traffic wear and environment, therefore lower FN Ratio and FNHFST 

come with older HFST sections. AADT should have an impact on pavement friction development 

since pavement wears and friction values decrease with repetitive traffic applications. However, 

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the majority of the HFST sites are located either on ramps or multiple-lane highways. Detailed 

traffic data on ramps and for each lane of multiple-lane sections are not available and the AADT 

values have to be estimated based on engineering judgment. With more accurate traffic data, the 

relationship between AADT and friction may be better revealed. 

Table 3.2. Potential influencing factors of HFST friction performance 

Data Precip. Air Data Install. FN 

Collection (Inch) Temp. Collection Age Aggregate AADT FNHFST Ratio 

ID (°F) Speed (MPH) (Month) 

1 48.4 53.1 30 4 Bauxite 6400 98.08 1.89 

2 48.4 51.7 30 4 Bauxite 9810 103.49 1.78 

3 37.7 86.2 40 30 Bauxite 13500 87.89 1.29 

4 37.7 85.2 40 30 Bauxite 13500 81.69 1.16 

5 55.4 43.8 40 17 Bauxite 2100 107.86 1.69 

6 55.4 44 40 17 Bauxite 2100 112.42 1.76 

7 34.6 51.3 40 30 Bauxite 14167 104.05 1.72 

8 34.6 51.8 40 30 Bauxite 14167 98.74 1.71 

9 34.6 51.9 40 30 Bauxite 14167 100.60 1.80 

10 34.6 51.4 40 30 Bauxite 14167 91.35 2.07 

11 34.6 52.3 40 30 Bauxite 14167 102.39 1.66 

12 34.6 51.8 40 30 Bauxite 14167 94.59 1.93 

13 35.5 33.9 40 64 Flint 26165 57.64 1.32 

14 35.5 33.7 30 64 Flint 1717 83.17 1.58 

15 35.5 33.8 30 64 Flint 1717 65.55 1.01 

16 34.3 36.5 40 64 Flint 6350 64.28 1.21 

17 30.7 44.5 30 51 Flint 10510 59.14 1.12 

18 34 50.8 30 51 Flint 4291 73.12 1.34 

19 30.2 41.7 30 51 Bauxite 3300 94.98 1.57 

20 30.2 44.3 30 51 Bauxite 3400 72.81 1.34 

21 30.2 44.2 30 51 Bauxite 3400 68.93 1.27 

22 30.2 49.4 20 51 Bauxite 2750 64.49 1.43 

23 30.2 45.1 20 51 Bauxite 2750 64.14 1.69 

24 12.8 38.5 30 63 Flint 2760 84.14 1.10 

25 14 39 40 63 Flint 5955 52.10 0.75 

26 14 38.5 40 63 Flint 5955 56.50 0.74 

27 44 58.6 30 9 Bauxite 1300 74.62 1.23 

28 44 58.6 30 9 Bauxite 1300 72.79 1.31 

29 44 55.8 30 9 Bauxite 1300 101.45 1.53 

30 44 55.3 30 9 Bauxite 1300 59.50 1.07 

31 27.2 50 40 4 Bauxite 400 102.60 1.79 

32 27.2 50.9 40 4 Bauxite 400 92.68 1.88 

33 27.2 50.7 40 4 Bauxite 400 111.46 2.18 

34 27.2 37.5 40 4 Bauxite 1720 97.06 1.80 

35 27.2 37.8 40 4 Bauxite 1720 124.28 2.46 

36 26.4 31.4 40 4 Bauxite 8420 90.25 2.20 

37 26.4 32.1 40 4 Bauxite 8420 93.37 2.20 

38 26.4 32.4 40 4 Bauxite 8420 107.81 2.03 

39 26.4 31.9 40 4 Bauxite 8420 108.33 1.96 

40 36.8 47 40 39 Bauxite 15500 88.41 1.51 

41 36.8 46.3 40 39 Bauxite 15500 88.32 1.33 

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Figure 3.6. HFST friction performance vs. installation age and average temperature
	

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Figure 3.7. HFST friction performance vs. aggregate type
	

Multivariate Analysis Results 

Multivariate analysis is conducted to analyze the effect of the influencing variables on FN Ratio, 

FNHFST, and FNNon-HFST. Precipitation, average temperature, HFST installation age, AADT are 

continuous independent variables, while aggregate type is a categorical variable and should be 

properly coded and quantified before multivariate analysis could be performed. Herein bauxite 

aggregate is represented as ‘1’ while flint is coded as ‘0’ in data preparation of model 

development. P-value is used to evaluate the significant level of each influencing variable on the 

dependent outcomes (which are the two friction performance measures). The multivariate 

analysis result is shown in Table 3.3. 

At 95% confidence interval, if P-value is smaller than 0.05, the corresponding variable has 

significant effect on the dependent variables. The P-values for average temperature and HFST 

installation age are much smaller than 0.05, which means they have significant effect on FN 

Ratio and FNHFST. The corresponding coefficient of those two dependent variables are negative, 

which indicates that FN Ratio and FNHFST decrease as those two variables increase. P-value of 

aggregate type (larger than 0.05) shows that it is not a significant factor for FN Ratio and 

FNHFST. However the corresponding coefficients of aggregate type are positive, which implies 

HFST using bauxite (coded as ‘1’) will add a positive number into the predicted FN Ratio or 

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FNHFST while HFST using flint (coded as ‘0’) doesn’t include such positive contribution to 

friction numbers. This statistic results support the data shown in Figure 3.8 that FN Ratio and 

FNHFST for HFST using bauxite are generally greater than those using flint. For FNNon-HFST, Pvalues for all the five variables are greater than 0.05, which indicates that the independent 

variables have insignificant impacts on pavement friction for non-HFST sections. 

Table 3.3. Multivariate analysis results (considering all five independent factors) 

Variables FN Ratio 

Coefficients 

FN Ratio 

P-value 

FN HFST 

Coefficients 

FN HFST 

P-value 

FN Non-HFST 

Coefficients 

FN Non-HFST 

P-value 

Intercept 3.113 0.000 146.258 0.000 40.368 0.012 

Precipitation 

(Inch) 0.016 0.074 1.027 0.020 -0.210 0.495 

Average 

Temperature -0.037 0.003 -1.626 0.005 0.459 0.250 

(°F) 

Installation 

Age (Month) -0.011 0.000 -0.473 0.001 0.061 0.523 

Aggregate 0.064 0.684 1.978 0.791 -1.517 0.780 

AADT 0.000 0.265 0.000 0.462 0.000 0.619 

Table 3.4. Multivariate analysis results (considering only the two significant factors)
	

Variables FN Ratio 

Coefficients 

FN Ratio 

P-value 

FN HFST 

Coefficients 

FN HFST 

P-value 

FN Non-HFST 

Coefficients 

FN Non-HFST 

P-value 

Intercept 2.912 2.62E-10 130.027 2.07E-09 42.056 0.000 

Average 

Temperature -0.019 0.006 -0.507 0.111 0.229 0.272 

(°F) 

Installation 

Age (Month) -0.013 1.05E-07 -0.575 4.07E-07 0.094 0.141 

Subsequently, multivariate analysis considering only the two significant influencing factors 

(average temperature and installation age) is conducted and the results are appended in Table 3.4. 

Both factors remain to be significant for the prediction of FN Ratio. However, the P-value of 

average temperature on FNHFST is larger than 0.05, which indicates that the impact of average 

temperature on FNHFST is not as significant as that of installation age, which supports the data 

shown in Figure 3.6(c) that FNHFST decreasing trend is not as significant as that of installation age. 

The multiple linear regression models are therefore developed as shown in Equation (3.2) to 

predict FN Ratio and FNHFST: 

FN Ratio = 2.912 − 0.019 ∗ Average Temperature − 0.013 ∗ Age
	

FNுிௌ் = 130.027 − 0.507 ∗ Average Temperature − 0.575 ∗ Age (3.2)
	

The predicted and measured FN Ratio and FNHFST for all the 41 data collections are plotted in 

Figure 3.8. The predictions follow similar development trend as the actual measured FN Ratio 

and FNHFST, and the R-squared values are 0.55 and 0.50 respectively. There are several potential 

reasons may cause the moderate R-squared values. Several factors of many HFST sites may take 

the same values, which reduces the variability of the data sets. For example, if HFST sites are 

close to each other in distance, the climate data for these sites are obtained from one weather 

station and the same values of precipitation and average temperature are used. For HFST sites 

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with multiple lanes, AADT data remains the same for the site for all lanes. More detailed and 

accurate data could result in better friction prediction models with higher R-squared values.
	

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Figure 3.8. HFST friction performance prediction
	

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4. GEOMETRIC TEXTURE INDICATORS FOR SAFETY ANALYSIS 

Introduction 

Pavement surface texture is defined as the deviation of the pavement surface from a true planar 

surface or an ideal shape [20]. These deviations occur at several distinct levels of scale, each 

defined by wavelength (λ) and peak to peak amplitude (A) of its components. Per the texture 

definition by Permanent International Association of Road Congresses (PIARC), pavement 

surface texture can be divided into four categories [12, 21]: 1) Micro-texture (λ < 0.5 mm, A ∈ [1 

to 500 μm]); 2) Macro-texture (λ ∈ [0.5 to 50 mm], A ∈ [0.1 to 20 mm]); 3) Mega-texture (λ ∈ 

[50 to 500 mm], A ∈ [0.1 to 50 mm]); 4) Roughness or unevenness (λ>500 mm). 

It is widely recognized that pavement surface texture affects many different pavement–tire 

interactions [22, 23]. Wet pavement friction, interior and exterior noise, splash and spray are 

mainly dependent on macro-texture properties. Dry pavement friction and tire wear are highly 

associated with micro-texture characteristics. Other tire-pavement interactions e.g. rolling 

resistance and ride quality are affected by the mega-texture and roughness. Therefore the study 

on macro-texture property places a vital role in evaluating pavement safety performance. In this 

study texture indicator is defined as an index or parameter to represent attributes of pavement 

surface texture. 

Currently several texture indicators have been used to characterize pavement surface texture. 

Mean Profile Depth (MPD) is the one of the commonly used texture indicator measured using 

the Circular Track Meter [24] or other laser based measuring systems [25]. The other 

standardized index is Mean Texture Depth (MTD), which is either measured using Sand Patch 

Method [26] or transformed via MPD [25]. Root Mean Square (RMS) is measured by several 

data collection systems, and it can be used as an indicator to represent the amplitude distribution 

of profile elevations [27, 28]. In addition, some other texture indicators such as Hessian Model 

[29], Power Spectral Density (PSD) [30], and Fractal Dimension (FD) [31] are also explored to 

characterize pavement surface texture. However, these parameters only disclose partial aspects 

of surface texture properties, e.g. MPD only reflects the height property of pavement surface. 

Pavement friction is a measure of the force generated when a tire loses traction on a pavement 

surface, and is dependent on a large number of factors including road types, tire properties, 

vehicle suspension system, traveling speed, ambient temperature, and the presence of 

contaminants such as oil and water [12]. Skid resistance is the contribution of roadway surface 

texture to form or develop this friction, and its value relies on the interaction between pavement 

surface and vehicle tires. The measurement of skid resistance is a process for monitoring 

pavement safety performance and preventing crashes on wet roadways. However, frictional 

measurement devices are relatively complex and costly to operate and maintain. During data 

collection, in most cases a truck carrying a large water tank is needed to saturate pavement 

surface with a prescribed layer of water during measurements, which is challenging for network 

level pavement friction measurement, so the estimation of skid resistance is becoming 

increasingly of interest [16, 32-35]. 

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Over the years, many studies have been performed to investigate the relationships between 

texture indicators and frictional indexes, some of which attempt to establish acceptable 

mathematical models to correlate skid resistance with texture characteristics [13, 36-44]. 

However, there are several limitations on the use of the existing models to predict pavement 

friction with texture data in the project- or network- level pavement safety surveys due to the two 

factors: 1) models are developed in laboratories with good correlations using high resolution data 

that are normally difficult to acquire in the field; 2) models are developed in fields with low 

correlations using one single line measurement of profile data, primarily in terms of MPD. 

Therefore there is a need to develop a reliable model for network level pavement friction survey 

based on improved texture data with broader pavement surface coverage using a wide range of 

texture indicators. 

Geometric Texture Indicators 

In the past decades several surface characterization techniques have been proposed for various 

application, and are generally grouped into two categories: scale-dependent and scaleindependent, as shown in Figure 4.1 [45, 46]. 

Figure 4.1. Schematic diagram of pavement surface characterization techniques 

The scale-independent parameters indicate texture characterization results are independent of the 

measurement scales (data resolution). Fractal analysis based indicator falls into this category. 

The scale-dependent parameters mean texture characterization results are dependent on the 

measurement scales. In other words, the analysis results might be quite different when different 

measurement scales are used. The scale-dependent parameters can be grouped into five 

categories: amplitude parameters, functional parameters, spectral analysis, spacing (or spatial) 

parameters, and hybrid parameters [45]. 

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In this study the four scale-dependent parameters (amplitude, spacing, hybrid, and functional 

parameters), also termed as geometric texture indicators, are used as the dependent variables to 

estimate pavement friction. To avoid the use of the two highly correlated texture indicators, the 

relationships among these texture indicators are investigated as well. Finally pavement safety 

property in terms of pavement friction is evaluated through a mathematical model developed 

from the geometric texture indicators. 

Amplitude or Height Parameters 

Amplitude parameter only considers the height or elevation information of surface texture, while 

ignores the impacts of data spacing on texture properties. For amplitude-related parameters, five 

texture indicators namely MPD, MTD, RMS, Skewness, and Kurtosis are presented [45, 46]. 

Mean Profile Depth (MPD) 

MPD is a widely accepted and used texture indicator. It is defined as the average of all mean 

segment depths of all segments of the profile. According to the MPD computation practice [21, 

25], the calculation of MPD can be described as follows: the measured profile is divided into 

different segments which have a length of 100±2 mm, then the segment is divided in two equal 

halves and the height of the highest peak in each half segment is determined. The average of 

these two peak heights minus the average of all heights is the mean segment depth. The average 

value of the mean segment depths for all segments making up the measured profile is reported as 

the MPD, as illustrated in Figure 4.2. 

Figure 4.2. A general procedure for MPD calculation 

Simulated Mean Texture Depth (SMTD) 

MTD can be viewed as a representation of 3D surface characteristics because it is obtained using 

volumetric measuring technique [26]. The measured result can be reported as the ground truth. 

Generally MTD can be either measured in field or transformed from MPD [25, 26]. However, in 

this study the MTD would be calculated with image processing techniques in the 3D domain 

[47]. 

A 3D digital image is composed of many discrete height data which are stored in computers as 

2D matrix. Assume the sampled pavement surface data can be divided into several areas; each 

area has a size of N x M mm, and the SMTD can be computed using Equation (4.1): 

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ವ ∫∫ [ிబିி(௫,௬)]ௗ௫ௗ௬ ∑ಿ ∑ಾ 

ೣసభ ೤సభ [ிబିி(௫,௬)] SMTD = బ = (4.1) ஽ ஽ 

Where: F(x, y)- the eight information at point (x, y), D - the integral area which equals to the M 

x N pixels, F଴- the height value being equivalent to the maximum peak in each area D (M x N 

pixels) 

Root Mean Square (RMS)

RMS is a general measurement of surface texture deviation property. If a larger RMS is measured 

on pavement surface, it indicates there is a significant deviations in surface texture characteristics 

[45]. This parameter can help interpret contact areas between vehicle tires and pavement surface, 

and thus is highly associated with surface bearing capacity. Its calculation can be mathematically 

described with Equation (4.2): 

∑ ∑ ಿ ಾ ௭(௫,௬)మ ஽ ೣసభ ೤సభ S௤ = ට∫∫ [z(x, y)]dxdy = ට (4.2) ଴ ெ×ே 

Where: M - the number of points per profile, N - the number of profiles, z(x, y)- the elevation 

difference between point (x, y) and the mean plane, S௤ - the root mean square of the surface. 

Skewness (Ssk) and Kurtosis (Sku)

Skewness and Kurtosis are used to represent 3D surface texture height distribution properties. 

Figuratively, a histogram of the heights of all measured points is computed. The symmetry and 

deviation from an ideal Normal Distribution is represented by Ssk and Sku, and their 

mathematical descriptions are given as Equations Error! Reference source not found.(4.3) and 

(4.4) [45]: 

∫∫ ವ ൣ௭(௫,௬)య൧ௗ௫ௗ௬ ∑ ∑ ಿ ಾ ௭(௫,௬)య ೣసభ ೤సభ Ssk = బ 

య = య (4.3) ௌ೜ ெ×ே× ௌ೜ 

∫∫ ವ ൣ௭(௫,௬)ర൧ௗ௫ௗ௬ ∑ ∑ ಿ ಾ ௭(௫,௬)ర ೣసభ ೤సభ Sku = బ = (4.4) ௌ೜

ర ெ×ே× ௌ೜ర 

Where: M - the number of points per profile, N - the number of profiles, z(x, y) - the elevation 

difference between point (x, y) and the mean plane, S௤ - root mean square of the surface. Ssk

represents the degree of symmetry surface heights about the mean plane. The sign of Ssk

indicates the predominance of peaks (Ssk > 0) or valley structures (Ssk < 0) comprising the 

surface. Sku indicates the presence of the inordinately high peaks/deep valleys (Sku >3.00) 

making up the texture. If surface heights are normally distributed, then Ssk is 0.00 and Sku is 

3.00. Similarly, surface heights are positively skewed (Ssk > 0) or negatively skewed (Ssk < 0). 

Surface height distributions can be considered as the slow variation (Sku < 3) or extreme peaks 

or valleys (Sku > 3). The less the Sku is, the smaller the height variation is. The larger the Sku is, 

the larger the height variation is. 

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Spacing or Spatial Parameters 

Texture on pavement surface may have anisotropic or isotropic patterns. Autocorrelation 

Function (ACF) is one of the most effective and robust approach for texture pattern recognition 

[45]. The ACF is determined by taking a duplicate surface Z((x − ∇x), (y − ∇y)) of the 

measured surface Z(x, y) with a relative lateral displacement (∇x, ∇y)and mathematically 

multiplying the two surfaces. Subsequently, the resulting function is integrated and normalized to 

yield a measure of the degree of overlap between the two functions. The ACF is a measure of 

how similar the texture is at a given distance from the original location. 

Generally the ACF of the anisotropic pavement surface has the fastest decay along the direction 

perpendicular to the predominant texture direction and the slowest decay along the texture 

direction, as shown in Figure 4.3a. The ACF of isotropic pavement surface has the similar texture 

aspects in all direction, so it is difficult to determine the fastest and slowest decay of the test 

sample, as shown in Figure 4.3b. For isotropic pavement surface, it is impossible to normalize 

the ACF of the fastest and slowest decay to 0.2 that is a threshold to determine the fastest and 

slowest decay. 

Figure 4.3. Spatial parameters (a) anisotropic; (b) isotropic 

Texture Aspect Ratio (TAR) is a measure of the spatial isotropy or directionality of the surface 

texture. The length of fastest decay is a measure of the distance over the surface such that the 

new location will have minimal correlation with the original location. On the other hand, the 

length of the slowest decay is a measure of the distance over the surface such that the new 

location will have maximum correlation with the original location. The TAR is computed as the 

ratio of the length of fastest decay to the length of the slowest decay, as mathematically 

described in Equation Error! Reference source not found.: 

୘୦ୣ ୢ୧ୱ୲ୟ୬ୡୣ ୲୦ୟ୲ ୲୦ୣ ୬୭୫ୟ୪୧ୱୣୢ ୅େ୊ ୦ୟୱ ୲୦ୣ ୤ୟୱ୲ୣୱ୲ ୢୣୡୟ୷ ୲୭ ଴.ଶ ୧୬ ୟ୬୷ ୮୭ୱୱ୧ୠ୪ୣ ୢ୧୰ୣୡ୲୧୭୬ 0 < TAR = ≤1 ୘୦ୣ ୢ୧ୱ୲ୟ୬ୡୣ ୲୦ୟ୲ ୲୦ୣ ୬୭୫ୟ୪୧ୱୣୢ ୅େ୊ ୦ୟୱ ୲୦ୣ ୱ୪୭୵ୣୱ୲ ୢୣୡୟ୷ ୲୭ ଴.ଶ ୧୬ ୟ୬୷ ୮୭ୱୱ୧ୠ୪ୣ ୢ୧୰ୣୡ୲୧୭୬

(4.5) 

In principle, the texture aspect ratio has a value between 0 and 1. Larger values, say TAR>0.5, 

indicate stronger isotropic or uniform texture aspects in all directions, whereas the smaller 

values, say TAR <0.3, indicate the stronger periodic texture properties. 

Hybrid Parameters 

Hybrid parameter is used to overcome some weaknesses of amplitude and spatial parameters. Its 

calculation depends on both the height and spacing information, and thus any changes that occur 

in either amplitude or spacing may have an effect on the hybrid property [45, 46]. This parameter 

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can be computed as: the ratio of the interfacial area of a surface over the sampling area. The areal 

element can be expressed using the smallest sampling quadrilateral ABCD, as shown in Figure 

4.4. 

Figure 4.4. Schematic diagram of the interfacial area 

Since the four corners of the quadrilateral may not be on the same plane, the interfacial area of 

the pile-up element may be considered to consist of two triangles, either ABC &ACD or ABD 

&BCD. The interfacial area of the quadrilateral is defined as an average of two sets of triangle 

areas (ABC &ACD and ABD&BCD) and its computation principle is given by Equation (4.6): 

భ 

A௜௝ = 

ଵ 

ସ ൫ หABሬሬሬሬሬ⃗| + |CD ሬሬሬሬሬሬሬ⃗ห൯൫หADሬሬሬሬሬ⃗| + |BC ሬሬሬሬሬሬሬ⃗ห൯ = 

ଵ 

ସ ൝൭൤∆yଶ + ቀf൫x௜, y௝൯ − f൫x௜, y௝ାଵ൯ቁ ଶ ൨ మ 

+ 

భ భ 

൤∆yଶ + ቀf൫x௜ାଵ, y௝ାଵ൯ − f൫x௜ାଵ, y௝ାଵ൯ቁ ଶ ൨ మ + ൤∆xଶ + ቀf൫x௜, y௝൯ − f൫x௜ାଵ, y௝൯ቁ ଶ ൨ మ + 

భ 

൤∆xଶ + ቀf൫x௜, y௝ାଵ൯ − f൫x௜ାଵ, y௝ାଵ൯ቁ ଶ ൨ మ ൱ൡ (4.6) 

The total interfacial area on the surface can be computed using Equation (4.7): 

A = ∑ ∑ A௜௝ 

ெିଵ 

௜ୀଵ 

ேିଵ 

௝ୀଵ (4.7) 

Then the calculation of surface areal ratio is given as Equation (4.8): 

SAR = 

(஺ି(ெିଵ)(ேିଵ)×∆௫×∆௬) 

(ெିଵ)(ேିଵ)×∆௫×∆௬ (4.8) 

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The developed interfacial area ratio reveals the hybrid property of surfaces. A large value 

indicates the significance of either the amplitude or the spacing or both. 

Functional Parameters 

The functional parameters are highly related to their functions i.e. wearing or friction. In this 

study Surface Bearing Index (SBI) was found to have a very close relation with the wearing 

properties of the surface [45], and equals to the ratio of the root mean square to the surface 

height at a 5% bearing area, as described using Equation (4.9). 

ට∫∫ವ[௭(௫,௬)]ௗ௫ௗ௬ 

SBI = బ = S௤/ Hହ% (4-9) ுఱ% 

Where S௤- root mean square; Hହ% - the surface height at 5% bearing area. 

Correlation Analysis 

Relationships among the geometric texture indicators are explored in this study. If the two 

texture indicators have a high correlation with the R2

 value greater than 0.8, one of them is 

excluded since they reveal the similar texture properties. However, for two texture indicators 

having a poor correlation (e.g. R2

 ≤0.6), both are considered to include two different texture 

properties, and thus the two texture indicators are kept in the model development. 

Two groups of samples are chosen to examine the relationships among different geometric 

texture indicators. The first sample group includes six test specimens. Each specimen is 

constructed with a different texturing technique. Figure 6a demonstrates the six rigid pavements 

with turf dragged texture (Figure 4.5a), transversely tined texture (Figure 4.5b), longitudinally 

tined texture (Figure 4.5c), longitudinally grooved texture (Figure 4.5d), transversely grooved 

texture (Figure 4.5e), and Next Generation Concrete Surface (NGCS) (Figure 4.5f). 

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Figure 4.5. 3D rendering of rigid pavement test specimens from 3D Ultra 

The second sample group contains three test specimens. Each specimen has obvious different 

texture properties: AC pavement constructed with dense graded surface (Figure 4.6a), AC 

pavements with exposed aggregate surface (Figure 4.6b), and high friction treated surface 

(Figure 4.6c). 

Figure 4.6. 3D rendering of flexible pavement test specimens from 3D Ultra
	

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Correlation analyses are performed among the indicators with the following observations: 

	 Figure 4.7 shows there is no good correlation between MPD and other texture indicators 

with one exception of SMTD. In this case MPD is applied in model development to 

describe the amplitude property of surface texture. 

	 Figure 4.8 indicates a good correlation is observed between RMS and SAR, with an Rsquared value of 0.9, and thus SAR is used to describe the hybrid property of surface 

texture. 

 Figure 4.9 indicates no good agreements exist between Skewness and Kurtosis or SBI. 

Both of them should be kept to disclose surface texture properties. 

 Figure 4.10 shows there is a poor correlation between Kurtosis and SBI. 

Based on correlation analysis results, MPD, Skewness, Kurtosis, TAR, SAR, and SBI are capable 

of disclosing different aspects of surface texture properties, and are used for the development of 

pavement friction prediction model. 

Figure 4.7. Correlation analysis results with MPD 

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Figure 4.8. Correlations analysis results with RMS 

Figure 4.9. Correlation analysis results with skewness 

Figure 4.10. Correlation result between Kurtosis and SBI 

Pavement Friction Model Development and Case Study 

Route Description 

To explore the relationships between the six surface texture indicators and pavement friction, one 

pavement section is chosen as the test bed in this study. AL-I 65 data collection starts at GPS 

coordinate of 32.387859, -86.322212, and ends at GPS coordinate of 32.390949, -86.321396, 

with a total length of approximately 393 m. The data collection site is the ramp from NB I-65 to 

EB SH152 (Northern Blvd.), as shown in Figure 4.11. 

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The route consists of two surface types: High Friction Surface Treatment (HFST) and the regular 

AC pavement surface type. HFST is located in the middle of the test section. The regular surface 

is located at the lead-in and lead-out segments. 

Figure 4.11. AL-I 65 HFST test site 

Friction Field Measurement 

In this study the friction data are acquired with the FHWA fixed-slip continuous friction tester, 

and 1mm 3D texture data are collected using DHDV with Pavesion3D Ultra. The test section is 

sampled into 84 segments, and each segment has a length of 4.57m (two 3D image long). The 

HFST segment starts from approximately 95 m and ends at approximately 301m, as marked in 

Figure 4.12. 

To validate the reliability of the collected friction data, three repetitive measurements are 

conducted. Note that the three measurements show consistent results with the correlation 

coefficients of 0.95, 0.98, and 0.95, respectively. In this study the mean friction numbers (FNs) 

from the three measurements are used for model development and validation. 

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Figure 4.12. Friction measurement results on AL I 65 ramp
	

Pavement Friction Prediction 

Model Development

As presented in Section 4, six texture indicators, namely MPD, Skewness, Kurtosis, TAR, SAR, 

and SBI are selected for pavement friction model development. Based on the multivariate 

regression analysis, pavement friction (FN௣) can be estimated with the six texture indicators, as 

mathematically described in Equation (4.10). 

FN௣ = 52.41MPD + 6.91Skewness − 1.15Kurtosis + 15.32TAR − 108.92SAR + 

63.67SBI − 140.69 (4.10) 

Figure 4.13 shows the correlation results between the predicted and measured FNs based on the 

multivariate regression analysis, with an R-squared value of 0.868. The predicted and measured 

friction numbers are shown in Figure 4.14. In addition, the sensitivity analyses of the predicted 

FNs to the six texture indicators indicate that Kurtosis and SAR have no significant influences on 

the predicted FNs based on the p-values (e.g. p >0.05), as shown in Table 4.1. Accordingly 

pavement friction enables to be estimated with the four indicators: MPD, Skewness, TAR, and 

SBI. 

y = 0.8681x + 10.145 

R2 = 0.8681 

0 

20 

40 

60 

80 

100 

120 

140 

0 20 40 60 80 100 120 140 

Predicted FN 

Measured FN 

Figure 4.13. Correlation results between the predicted and measured FNs
	

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0.00 

20.00 

40.00 

60.00 

80.00 

100.00 

120.00 

140.00 

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 

Friction Number (FN) 

Sample ID along Test Section 

Measured_FN Predicted_FN 

Figure 4.14. Comparison of the measured and predicted FNs from six texture indicators
	

Table 4.1. Multivariate regression results from the six texture indicators 

Standard Lower Upper Lower Upper 

Indicator Coefficients Error t Stat p-value 95% 95% 95.0% 95.0% 

Intercept -140.69 29.02 -4.85 0.00 -198.48 -82.90 -198.48 -82.90 

MPD 52.42 6.59 7.95 0.00 39.29 65.54 39.29 65.54 

Skewness 6.91 1.77 3.90 0.00 3.38 10.45 3.38 10.45 

TAR 15.32 5.47 2.80 0.01 4.42 26.23 4.42 26.23 

SBI 63.67 5.02 12.69 0.00 53.68 73.67 53.68 73.67 

Kurtosis -1.15 3.50 -0.33 0.74 -8.13 5.82 -8.13 5.82 

SAR 108.92 81.24 1.34 0.18 -52.89 270.72 -52.89 270.72 

The multivariate regression analysis indicates the correlation coefficient between the predicted 

and measured FNs is around 0.86 when the four variables are used to estimate pavement friction, 

and the corresponding model coefficients are given in Table 4.2. Note that the p-value for each 

variable is less than 0.05, indicating the developed model is statistically significant for pavement 

friction prediction. The developed model can be mathematically described using Equation (4.11). 

FN௣ = 48.27 MPD + 7.38Skewness + 12.34TAR + 59.42SBI − 105.58 (4.11) 

Table 4.2. Multivariate regression results from MPD, Skewness, TAR, and SBI 

Standard Lower Upper Lower Upper 

Indicator Coefficients Error t Stat p-value 95% 95% 95.0% 95.0% 

Intercept -105.58 9.79 -10.78 0.00 -125.08 -86.09 -125.08 -86.09 

MPD 48.27 5.96 8.09 0.00 36.39 60.14 36.39 60.14 

Skewness 7.38 1.31 5.63 0.00 4.77 9.99 4.77 9.99 

TAR 12.34 5.06 2.44 0.02 2.27 22.41 2.27 22.41 

SBI 59.42 4.10 14.49 0.00 51.26 67.59 51.26 67.59 

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Model Verification and Improvement

In this model the effects of each independent variables (e.g. MPD) on dependent variables (e.g. 

FN) are assumed to be linear. If the effects of the independent variables on the dependent 

variables appear to be non-linear, this model may not be the appropriate fit for the data. In this 

study the residual plots is used to investigate the linear effects of independent variable on the 

dependent variable. The residual plot shows the residuals (the differences between the measured 

and predicted values) on the vertical axis and the independent variable on the horizontal axis. If 

the points in a residual plot are randomly distributed around the horizontal axis, a linear 

regression model may be appropriate for the data; otherwise, a non-linear model is more 

appropriate [48]. Figure 4.15 shows the residual plots of the four variables. 

Note that the Figures 4.15b and 4.15d show a random dispersion around the horizontal axis, 

indicating the linear models can be applied on these two variables to predict pavement friction. 

Figures 4.15a and 4.15c show non-random patterns (U-shaped or inverted U-shaped) are 

observed for the Skewness and TAR, indicating the non-linear models should be used for the two 

variables. Based on Figure 4.16(a), a non-linear model should be developed to fit the FNs with 

the independent variable “MPD”. After several trial-and-error, a three-order polynomial model is 

employed with the largest R-squared value. Similarly, exponential model is developed for the 

SBI to fit the measured FNs as shown in Figure 4.16b. Subsequently, data transformation is 

performed. 

Figure 4.15. Residual plots of the four variables
	

a) MPD; b) Skewness; c) SBI; d) TAR
	

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Figure 4.16. Non-linear models 

a) MPD and b) SBI 

In the subsequent multivariate analysis, the original MPD and SBI are replaced by the 

transformed MPD and SBI calculated from the developed models, and the multivariate 

regression analysis results are given in Table 4.3. Note that the p-values are approaching to the 

zero, indicating the newly developed model are statistically more significant for pavement 

friction prediction. As a result, a new model can be developed with the four variables: MPD, 

Skewness, TAR, and SBI, as mathematically described in Equation (4.12). 

FN௣ = −714.15 MPDଷ + 2256.43MPDଶ − 2264.432MPD + 7.04Skewness + 13.43TAR + 

5.89e଴.ଽସௌ஻ூ + 743.93 (4.12) 

Table 4.3. Multivariate regression results from Skewness, TAR, NEW_MPD and NEW_SBI 

Indicator Coefficients Standard 

Error t Stat P-value Lower 95% 

Upper 

95% 

Lower 

95.0% 

Upper 

95.0% 

Intercept -15.64 4.68 -3.34 0.00 -24.96 -6.32 -24.96 -6.32 

Skewness 7.04 1.18 5.96 0.00 4.69 9.39 4.69 9.39 

TAR 13.43 4.43 3.03 0.00 4.61 22.26 4.61 22.26 

NEW_MPD 0.58 0.06 9.50 0.00 0.46 0.70 0.46 0.70 

NEW_SBI 0.60 0.07 8.64 0.00 0.46 0.74 0.46 0.74 

Correlation between the Predicted and Measured FNs 

The measured FNs are correlated with the predicted FNs from the Equation 12, with an Rsquared value of 0.895. Apparently there are three outliers among the test samples due to their 

large deviations from the fitting line, as illustrated in Figure 4.17a. After the influences of the 

outliers on the developed models are eliminated, a new linear model can be developed, with an 

R-squared value of 0.947, as shown in Figure 4.17b. 

As a result, pavement friction can be estimated based on the four texture indicators: MPD, 

Skewness, TAR, and SBI. MPD and Skewness belong to the amplitude parameters representing 

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surface height distribution. TAR belongs to the spacing parameters, describing pavement surface 

texture pattern. SBI belongs to the functional parameters, disclosing surface bearing capacity and 

pavement frictional properties. 

Figure 4.17. Correlation results between the predicted and measured FNs
	

a) with outliers; b) after outlier removal
	

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5. EVALUATION OF PAVEMENT SURFACE HYDROPLANING 

Introduction 

Pavement hydroplaning occurs when water pressures build up in front of a moving tire resulting 

in an uplift force sufficient to separate the tire from the pavement. The loss of steering and 

traction force produced during hydroplaning may cause the vehicle to lose control, especially 

when a steering tire is involved [49]. Past studies indicated the event of hydroplaning is highly 

associated with several factors, including pavement texture, cross slope, longitudinal grade, 

pavement width, pavement types, pavement condition, tire characteristics, and rainfall intensity 

[50, 51]. 

Numerous field studies were dedicated to developing hydroplaning prediction models in the past 

decades [52]. The models can be grouped into two categories: empirical models and analytical 

models [53]. The empirical methods use experimental data and equations to predict 

hydroplaning, including Road Research Laboratory (RRL) equations to estimate water film depth 

(WFD) [54], National Aeronautics and Space Administration (NASA) models developed based 

on aircraft tire and airport pavement data (4), and Gallaway model to predict roadway 

hydroplaning [55]. The analytical methods attempt to mathematically model hydroplaning of the 

sheet flow and its interaction with a tire, including PAVDRN computer program developed by 

Pennsylvania State University [56], and the University of South Florida (USF) model based on 

Ong and Fwa's numerical prediction [57]. 

Pavement slope, also termed as flow path slope, consists of cross slope and longitudinal grade, 

which exerts a tremendous influence on hydroplaning prediction [58]. To maintain constant 

water film, hydroplaning simulation tests in past studies were conducted on pavements at tangent 

and flat terrain[59, 60]. For pavement segments with horizontal curve and severe down grade, a 

smaller uplift force of water can cause hydroplaning issues due to the reduced vertical wheel 

load caused by large slopes. However, past studies on hydroplaning prediction neglected the 

influences of pavement slope on vertical wheel loads of vehicles. The existing hydroplaning 

prediction models overestimate hydroplaning speed, and are particularly not suitable to analyze 

pavements with steep pavement slope. 

Hydroplaning Prediction Models 

Gallaway and USF Models 

The Gallaway model is an empirical method developed by Gallaway et al. [55] for the US 

Department of Transportation. The method described in Equation (5.1)-(5.5) was adopted in the 

Texas Department of Transportation (TxDOT) Hydraulic Design Manual [55]. The flow path, an 

important factor on hydroplaning prediction model, can be defined in Figure 5.1 and calculated 

with Equation (5.1). The USF model is an analytical hydroplaning prediction model developed at 

the University of South Florida based on Ong and Fwa's comprehensive numerical prediction, 

shown in Equation (5.6). The USF model can be used to predict the hydroplaning speeds for 

different light vehicles that employ tires compatible with the locked-wheel tester tires [57]. 

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ଶ ଶ)ଵ/ଶ S୤ = (S୪ + Sୡ (5.1) 

L௙ = W௣ × (S௙⁄S௖) (5.2) 

WFD = 0.01485ൣ(MTD଴.ଵଵ ଴.ସଷ ଴.ସଶ × L௙ × I଴.ହଽ) (S ⁄ ௖ )൧ − MTD (5.3) 

ଵଶ.଺ଷଽ ቀ ቁ + 3.50 ௐி஽బ.బల A = Max. of ቐ (5.4) ଶଶ.ଷହଵ [ቀ ቁ − 4.97] × MTD଴.ଵସ 

ௐி஽బ.బల

v௣ = 0.9143 × SD଴.଴ସ × P௧

଴.ଷ × (TD + 0.794)଴.଴଺ × A (5.5) 

଴.ହ ଴.଼ଶ 

v௣ = W଴.ଶ × P௧ × [( ) + 0.49] (5.6) ௐி஽బ.బల

Where, WFD: Water film depth (mm); MTD: Mean texture depth (mm) calculated from the 

macro texture data; v୮: Hydroplaning speed (km/h); L୤: Pavement flow path length (m); Sୡ: 

Cross slope (m/m); S୪: Longitudinal grade (m/m); W୮: Pavement width (m); I: Rainfall intensity 

(mm/hr); P୲: Inflation pressure (Kpa); SD: Spin down ratio; TD: Tire tread depth (mm); W: 

Wheel load (N). 

Figure 5.1. Schematic diagram of cross slope, longitudinal grade, and flow path 

Effects of Pavement Slope on Vertical Wheel Load 

Typically cross slope or longitudinal grade would reduce the vertical wheel load of vehicles on 

pavement surface [61]. Hydroplaning occurs when the vertical wheel load is equivalent to the 

uplift force by water (Equation (5.7)), and the steering and traction force would be lost during 

hydroplaning. 

Figure 5.2 (a) shows the pavement section with a large longitudinal grade. When the vehicle 

travels on this pavement segment, the vehicle gravity center would be partitioned into two 

components of forces: one (wheel load) is perpendicular with the travelling surface, and the other 

one (traction force) is parallel with pavement surface. The wheel load would decrease with the 

increase of longitudinal grade (Equation (5.8)), and the reduced wheel load would increase the 

hydroplaning risk. 

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Figure 5.2 (b) shows the pavement section with horizontal curves or large cross slope. Similarly, 

the vehicle gravity center is partitioned into two components of forces when the vehicle travels 

on the horizontal curve. One component of force is the wheel load, and the other one is the 

centripetal force shown in Figure 5.2 (b). The wheel load on horizontal curve would decrease 

with the increase of super-elevation (Equation (5.9)). Finally the wheel load can be calculated 

with flow path slope by combining the cross slope and longitudinal grade, as given in Equation 

(5.10). 

F௎௣ = W (5.7) 

W௅ = G × cos(β) (5.8) 

W஼ = G × cos(α) (5.9) 

W = G × cos(ρ) (5.10) 

Where: F୙୮ -- Minimum uplift force causing hydroplaning (N); W -- Wheel load (N); W୐ --

Wheel load in longitudinal section (N); Wେ -- Wheel load in cross section (N); G -- Gravity of 

vehicle (N); β -- Angle of longitudinal grade (degree); α-- Angle of cross slope (degree); ρ -- 

Angle of flow path slope (degree). 

Figure 5.2. Vehicle travelling on pavements with (a) longitudinal grade; (b) horizontal curve 

Improved Hydroplaning Speed Prediction Models 

In the current models, the influences of flow path slope on vertical wheel load are not taken into 

account in hydroplaning prediction models. Therefore this study aims at improving the existing 

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Gallaway and USF models by considering the effects of flow path slope on wheel loads, as 

shown in Equations (5.11) and (5.12). 

v௣ = 0.9143 × SD଴.଴ସ × (P௧ × cosρ)଴.ଷ × (TD + 0.794)଴.଴଺ × A (5.11) 

v௣ = (W × cosρ)଴.ଶ × (P௧ × cosρ)଴.ହ × (0.82 ⁄WFD଴.଴଺ + 0.49) (5.12) 

Where: W -- Wheel load (N); WFD -- Water film depth (mm); P୲ -- Inflation pressure (Kpa); SD -

- Spin down ratio; TD -- Tire tread depth (mm); A -- Maximum value of Equation (4); ρ -- Angle 

of flow path slope (degree). 

Sensitivity Analysis of the Improved Models 

To explore the sensitivity of cross slope and longitudinal grade on hydroplaning speed, the cross 

slope and longitudinal grade change by ± 25%, ± 50%, and ± 75% individually while the other 

variables are maintained constant values. The constant values of each factor is assumed to be the 

average values of that factor measured for test site, as provided as follows: 

 Cross slope: S௖ = 1.53% 

 Rainfall intensity: I = 148.4 mm/hr 

 Mean texture depth: MTD = 1.2 mm 

 Longitudinal grade: S୪ = 1.32% 

The results of sensitivity analysis from the improved Gallaway and USF models to cross slope 

and longitudinal grade are given in Figure 3. It can be seen that the resulting change in 

hydroplaning speed, "VP", is apparent along the increase of cross slope and longitudinal grade. In 

the two improved models, the hydroplaning speed is affected by both the vertical load and the 

flow path length. Typically the increase in cross slope or longitudinal grade would diminish the 

vertical wheel load. The increase in cross slope would shorten the flow path length, while the 

increase in longitudinal grades would extend the flow path length. Both the decrease in vertical 

load and the increase in flow path length would reduce the hydroplaning speed. 

Accordingly the hydroplaning speed should decrease with the increase of longitudinal slope, and 

may either increase or decrease with the increase of cross slope depending on effects of vertical 

load and flow paths. Figure 5.3 shows that hydroplaning speed goes up with the increase of the 

cross slope, indicating the effect of flow path length on hydroplaning speed is greater than that of 

wheel load. It is shown that hydroplaning speed is more sensitive to the cross slope than 

longitudinal grade in the two improved models. 

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Figure 5.3. Sensitivity of improved hydroplaning models 

(a) longitudinal grade; (b) cross slope 

Data Preparation 

Estimated Mean Texture Depth (EMTD) 

The methodologies for texture measurements can be grouped into two categories: static and 

high-speed methods. The static test methods include Sand Patch Method [26], Circular Track 

Meter [24], and Outflow Meter [62], and their measurements are conducted on the marked or 

specified small areas. The traditional high-speed test techniques are characterized with the laser 

based data acquisition systems [63] with a spot laser resulting in a single line of measurement 

along the longitudinal direction of pavement. The measurements are continuously conducted on 

test sections, which can be regarded as an efficient tool for network level pavement survey. 

The widely used texture indicators include the Mean Profile Depth (MPD) and Mean Texture 

Depth (MTD) [25, 26]. In this study the MTD methodology is applied since the estimation of 

water film depth is dependent on the MTD in the hydroplaning models. However, as the manual 

process to obtain MTD through the Sand Patch Method is a standard, time-consuming, and 

somewhat not reliable enough [47], the 3D pavement surface captured with the 3D Ultra 

technology is therefore used as an alternative to be used as input to calculate area texture 

simulating the Sand Patch Method. The alternative substantially improves data collection 

efficiency and reliability of computing surface texture. As a volumetric method, the Estimated 

MTD (EMTD) is therefore introduced in the research by simulating the Sand Patch Method with 

1mm 3D laser imaging data of the entire lane, as shown in Equation 5.13 [47]. EMTD and MTD 

are assumed to be equivalent in the presented research. 

ವ ೣసಿ ೤సಾ ∫∫ [ிబିி(௫,௬)]ௗ௫ௗ௬ ∑ೣసభ ∑೤సభ [ிబିி(௫,௬)] ௄ బ ௄ EMTD = (1⁄k) × ∑ = (1⁄k) × ∑ (5.13) ௜ୀଵ ௜ୀଵ ஽ ஽ 

Where: F(x, y) – The pixel depth at point (x, y); D – The integral or gridded area containing of 

M×N pixels; F଴ – The maximum peak in each area D; K – The number of grids within the test 

sample. 

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Cross Slope Calibration 

A properly designed and constructed cross slope is important for safe travelling since inadequate 

cross slopes may result in low efficiency in drainage and steep cross slopes may lead to vehicle 

maneuvering difficulties. Therefore the accurate measurement of cross slope is important for 

hydroplaning speed prediction. In this study, 1mm 3D pavement data and IMU data are 

combined together to reproduce the cross slope of pavements. 

IMU mounted on the vehicle can measure three Euler angles, which are termed as roll (Euler 

angle about x-axis), pitch (Euler angle about y-axis) and yaw (Euler angle about z-axis) 

respectively. The roll angle is to represent pavement cross slope, and pitch angle is traditionally 

used to represent pavement longitudinal grade based on the assumption that the vehicle floor is 

parallel with pavement surface during travelling. However, in real world the vehicle floor is not 

parallel with pavement surface during travelling, which can be caused by: 1) uneven gravity 

distribution of the vehicle; 2) vibration of the vehicle during travelling; 3) pavement surface 

geometry and condition. 

This study attempts to measure the vehicle's body roll angle in X coordinate (angle γ) using the 

collected 3D laser imaging data. Two sensors mounted on the rear of the DHDV are capable of 

covering the entire lane. The “true” cross slope of pavements can be approximately determined 

with two parameters: the tilt of the vehicle floor and the slope of pavement surface captured by 

3D cameras [47]. As Figure 5.4 shows, the IMU system measures the angle θ of the vehicle 

relative to a level datum. γ is the vehicle vibration angle in X coordinate which can be calculated 

in Equation (5.14). The "true" cross slope can be obtained by Equation (5.15). However, in real 

world the angle θ and γ are very small, so the cross slope can be directly computed as the 

difference in slope of θ and slope of γ (Equation (5.16)) [64].

୷మି୷భ γ = act tan ( ) (5.14) ୐ 

α = tan(θ + γ) (5.15) 

α = tan(θ) + tan(γ) (5.16) 

Where: α – Angle of cross slope (degree); γ – The body roll angle of vehicle (degree); θ – IMU 

roll angle (degree); L – The distance between left and right laser (m); yଵ – The vertical distance 

from left sensor to the pavement surface (m); yଶ – The vertical distance from right sensor to the 

pavement surface (m). 

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Figure 5.4. Cross slope calibration based on IMU and 1mm 3D data 

Software Interface for Automated Hydroplaning Prediction 

A software program named Automated Hydroplaning Prediction Program (AHPP) is developed 

in this study to implement data processing and analysis. Figure 5.5 shows the main interface of 

AHPP. Once users import the IMU and 3D image data into AHPP, the two types of data (1mm 

3D laser imaging data and IMU data) can be automatically matched by Distance Measurement 

Instrument (DMI) pulses, and the calibrated cross slope can be produced by the integration of 

IMU data and 3D data. In AHPP, users can manually assign the local rainfall intensity and 

pavement types. The AHPP outputs include EMTD, WFD, calibrated cross slope, longitudinal 

grade, and predicted hydroplaning speeds from various models. 

Figure 5.5. Software interface for automated hydroplaning prediction 

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Case Study 

Testing Site 

A flexible pavement section located in Spavinaw, Oklahoma is chosen as the test section, which 

starts from the location (Latitude: 36.329175, Longitude: -95.081696), and ends with the location 

(Latitude: 36.351066, Longitude: -95.062796), with a length of 4.35 km. The pavement of the 

test lane is in excellent condition and has a width of 3.65m. On this test section there are five 

horizontal curves. 

Selection of Sample Size 

The 3D laser imaging data collected with the 3D Ultra DHDV is stored on computer hard disk in 

the form of raw data files with the size of 4096 pixel wide by 2048 pixel long. The raw data files 

are used as basic input data sets, or samples, and subsequently data processing and analysis are 

conducted on each individual sample. In this study one raw image is considered as a sample 

(2.28m long) and the entire pavement section consists of 1915 samples. 

Local Rainfall Intensity 

The local rainfall intensity at the test site is obtained from National Oceanic and Atmospheric 

Administration's (NOAA) National Water Service database [65]. Table 5.1 shows the 

precipitation in Spavinaw Station Oklahoma from NOAA database. The two-year return period 

storm with duration of five minutes is used in Gallaway and USF models for rainfall intensity 

acquisition. Based on NOAA database, the rainfall intensity of 148.4mm/hour is used for the test 

site. 

Table 5.1. Precipitation in Spavinaw station 

Duration Recurrence Recurrence Recurrence Recurrence 

(in mm) - 1 year - 2 year - 5 year - 10 year 

5 min 10.87 12.37 14.91 17.04 

10 min 15.93 18.11 21.82 24.97 

15 min 19.41 22.09 26.67 30.48 

30 min 28.70 32.77 39.62 45.47 

Cross Slope and Longitudinal Grade 

Both longitudinal grade and cross slope are the key factors to form flow path slope. As Figure 

5.6(a) shows, the maximum longitudinal grade is 12.03%, and the standard deviation is 2.48. 

Due to the vibration of the surveying vehicle, there is some noise in the raw cross slope captured 

by IMU roll angle. Based on the 3D laser imaging data, the vehicle body roll angle can be 

measured, and then the raw cross slope is calibrated. Figure 5.6(b) shows the raw cross slope and 

calibrated cross slopes. Comparing the raw cross slope data and calibrated cross slope, the 

majority of the noise is eliminated from the raw data through the calibration. The cross slope 

presents negative values at left turn curves and positive values at right turn curves. In this test 

site, curves #1, #4, and #5 belong to left turn curve, while curves #2 and #3 belong to right turn 

curve. The statistical results of the calibrated cross slopes on test site are given as follows: (1) the 

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average cross slope on the straight road segments is 1.94%; (2) the average cross slope of curve 

#1, #2, #3, #4, and #5 are -2.06%, 4.96%, 5.80%, -3.81%, and -5.01%, respectively. 

Figure 5.6. Pavement geometry of the testing site 

(a) Longitudinal grade; (b) Cross slope. 

EMTDs and WFDs 

Figure 5.7(a) shows the corresponding WFD along the test section, with an average value of 

1.73mm and the maximum value of 8.52 mm, and Figure 5.7(b) shows the EMTDs at the test 

section, with an average value of 1.20 mm. The WFD is calculated with Gallaway WFD model 

based on pavement texture depth, flow path slope, and local rainfall intensity as inputs. 

Figure 5.7. WFDs and EMTDs of test site 

(a) WFDs; (b) EMTDS. 

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Hydroplaning Speed Estimation 

15 samples of the calculated WFD, EMTD and IMU data for hydroplaning speed prediction are 

given in Table 5.2. Gallaway model, USF model, the improved Gallaway model, and the 

improved USF model are utilized to predict hydroplaning speed, respectively, as shown in Figure 

10(a). Results indicate the predicted hydroplaning speeds from original Gallaway and USF 

model are approximately 140km/h and 165km/h, respectively, which are around 50km/h higher 

than those predicted from the improved Gallaway model (96km/h) and improved USF model 

(91km/h). The results also show as expected that the hydroplaning speeds at curves of the five 

horizontal curves in Figure 5.8(a) are lower than that on the straight road sections. 

Table 5.2. Sample 3D and IMU data for hydroplaning speed calculation 

Sample ID WFD (mm) EMTD (mm) Cross Slope (%) Longitudinal Grade (%) 

1 1.64 1.12 2.07 3.27 

2 1.51 1.11 2.26 3.18 

3 1.35 1.11 2.54 3.10 

4 1.30 1.10 2.68 3.12 

5 1.23 1.11 2.82 3.07 

6 1.21 1.09 2.87 2.89 

7 1.13 1.12 2.95 2.78 

8 1.05 1.17 3.05 2.77 

9 1.03 1.14 3.23 2.72 

10 1.00 1.10 3.44 2.67 

11 0.93 1.12 3.64 2.71 

12 0.94 1.11 3.74 2.81 

13 0.94 1.10 3.76 2.85 

14 0.86 1.17 3.79 2.79 

15 0.92 1.10 3.83 2.76 

Potential Hydroplaning Segment Detection 

Identification of locations with hydroplaning potential is based on the comparison of estimated 

hydroplaning speed with posted speed of the road section [47]. At the test site, speed limits are 

80km/h on straight sections and 56km/h on road curves. The average hydroplaning speeds 

calculated with the four models are used to detect potential hydroplaning segments, shown in 

Figure 5.8(a). Since the predicted hydroplaning speeds at the five curves are higher than posted 

speed limit, there is a low hydroplaning risk at the five curves for vehicles operating at speed 

limit. However, for several segments of the test site, the predicted hydroplaning speeds are lower 

than the posted speed limit. Therefore, these segments can be identified as potential segments for 

increased hydroplaning risk, as marked with red line in Figure 5.8(b). To minimize traffic 

crashes caused by hydroplaning, highway agencies can post a reduced speed sign at these 

locations, or take other remedial actions, such as installing High-Friction Surface Treatment 

(HFST) [66]. 

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Figure 5.8. Detection of potential hydroplaning risk 

(a) hydroplaning speed; (b) increased hydroplaning potential segments. 

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6. SUMMARY 

The 3D Ultra laser imaging technology has the capability to collect pavement surface texture 

data at full-lane coverage at 1 mm resolution in all three dimensions at speed up to 60MPH. This 

single-pass and complete lane coverage platform provides an ideal solution to evaluate pavement 

surface characteristics for safety analysis and many different data collection needs without 

interrupting traffic. Furthermore, it will saved cost by eliminating the need for maintenance of 

traffic. 

In this study, the 3D Ultra laser imaging technology with necessary software tools are utilized for 

data collection and subsequent surface characterization and safety evaluation and analysis. First, 

the 1mm 3D data for 21 HFST sites in 11 states with a total of 41 sections are collected for each 

traffic lane at the posted highway speeds. Pavement rutting and macro-texture data are calculated 

from collected 1mm 3D data sets, while pavement friction data are collected using FHWA's 

fixed-slip continuous friction tester. The measured surface characteristics on the HFST sites and 

untreated existing pavements are compared and paired t-tests are performed to determine the 

effectiveness of the HFST sites in improving surface characteristics. It is evident that the HFST 

surfaces have statistically significant higher friction numbers and surface macro-texture MPD 

values than those on the adjacent pavements without HFST. There exist several exceptions to 

such trend, 2 sections for friction data and 5 sections for MPD data. Multivariate analyses are 

conducted to investigate the impacts of five independent variables: precipitation, average 

temperature, HFST installation age, aggregate type, and AADT, on HFST friction performance. 

Average temperature and HFST installation age are identified to have significantly impact on 

friction performance of HFST (FNHFST) and the ratio of friction on HFST to that on non-HFST 

(FN Ratio). Based on the trend between FN Ratio and installation age, in general the benefit of 

HFST in friction effectiveness is lost after approximate 60 months (5 years) of service. HFST 

sites using calcined bauxite aggregates exhibit higher friction performance than those using 

flints. The regressional friction prediction models are therefore developed for HFST, which can 

be used to predict the service life of HFST installation and aid decision making within a highway 

agency. However, it should be noted that the models don’t consider the pavement conditions 

before HFST application and HFST construction quality variations as the dependent variables 

due to the unavailability of data. In addition, detailed traffic data for ramps and each lane of sites 

with multiple lanes are not available and have to be estimated during the model development. 

Surface texture and friction are two primary characteristics for pavement safety evaluation. 

Understanding their relationship is critical to reduce potential traffic crashes especially at wet 

conditions. However, the currently used texture indicators, such as Mean Profile Depth (MPD), 

and Mean Texture Depth (MTD), are calculated restricted on either a small portion on pavement 

surface or single line of profile measurement, and thus only reveal partial aspects of texture 

property. With 1mm 3D laser texture data, eight surface texture indicators are examined for their 

applications on pavement friction prediction. The eight surface texture indicators are grouped 

into four categories: amplitude parameters (including MPD, SMTD, RMS, Skewness, and 

Kurtosis), spacing parameters (TAR), hybrid parameters (SAR), and functional parameters 

(SBI). To avoid using two highly correlated texture indicators in the friction prediction model, 

correlation analyses are conducted and found that 1) SMTD are highly correlated with MPD; 2) 

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RMS is highly correlated with SAR. As a result, six texture indicators, including MPD, 

Skewness, Kurtosis, TAR, SAR, and SBI, are adopted for the development of pavement friction 

prediction model using multivariate regression analysis. The developed model can be used to 

predict pavement friction based on various texture indicators, and may be beneficial in the 

continuous measurement and evaluation of pavement safety for the project- and network- level 

pavement surveys. 

The third application of using 1mm 3D Ultra data sets is to predict and evaluate pavement 

surface hydroplaning risk. Considering the effects of flow path slope on vertical wheel load 

perpendicular to pavement surface and the resulting hydroplaning speed, the Gallaway and USF 

models are modified for improvements in this study. The sensitivity analysis shows that the 

hydroplaning speed is more sensitive to cross slope than longitudinal grade in the improved 

models. A 3D based volumetric measuring method is used to calculate the estimated MTD based 

on the full-lane 1mm 3D data. Subsequently, IMU data and 3D data are combined to model 

vehicle movements on cross slopes. Local rainfall intensity is obtained from NOAA precipitation 

database. By considering effects of cross slope and longitudinal grade on wheel load and flow 

path length, it is found that hydroplaning speed decreases with the increase of the longitudinal 

grade, but increases with the increase of the cross slope. The improved models predict lower 

hydroplaning speed than that from the original Gallaway and USF models. An important future 

work is to use a combined slope based on longitudinal grade and cross slope to demonstrate the 

validity and effectiveness of the improved models. 

This study with field pavement applications has shown that the 1mm 3D Ultra which represent a 

form of 3D Laser imaging technology is promising in real-time pavement surface 

characterization and evaluation for both pavement and safety management at network and project 

level surveys. It is anticipated that more data collection with more testing sites are to be 

performed to validate the new emerging 3D laser imaging technology as a single-pass and 

complete lane-coverage platform for multiple safety and pavement evaluation purposes. In 

addition, the long-term monitoring of the HFTS sites in multiple states is highly recommended 

due to the need to determine multi-year performance of the HFTS applications in terms of 

region, pavement condition, materials used, geometric properties, and other factors. The 

recommended long-term study would provide much needed data for both design and construction 

of HFTS for its widespread adoption. 

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Safety Analysis Opportunities Using Pavement Final Report
	

Surface Characterization Based on 3D Laser Imaging September 2017
	

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55 
 

 

 

 

For More Information:

Safety Analysis Opportunities Using Pavement Surface Characterization 

Based on 3D Laser Imaging, visit: http://safety.fhwa.dot.gov/ 

July 2017 

FHWA-SA-17-046