Safe roads require pavement surfaces that provide adequate friction to tires, helping to bring vehicles to a complete stop in a timely manner that avoids accidents. The current manual methods of evaluating surface friction of roads and bridges are not only dangerous for the inspectors and motorists on the road but they are also very time-consuming and subjected to inspector’s judgment. Currently, macrotexture is quantified by a Mean Texture Depth (MTD) index. In past, several attempts are made to correlate MTD with sound generated from tire pavement interaction. Here the approach is to estimate MTD indirectly from the sound generated by the tire-pavement interaction in a moving vehicle. In this article, the possibility of estimating friction of pavement surface through pavement macrotexture from noise generated under the body of a moving vehicle is reviewed.


Research in the U.K. has shown that investment in a safe road infrastructure program could yield a ⅓ reduction in road deaths, saving as much as 500 billion rupees per year (Hill, 2008). The majority of these car accidents happen due to the lack of friction on the pavement surface. The main function of the pavement is to provide a safe and smooth ride to the road users. So, the functional characteristics of pavement such as ride quality, safety, and noise must be optimized for the wearing surface mixes. Safety-related properties of HMA (hot mix asphalt) pavements such as texture and skid resistance are not yet quantitatively included in most design specifications.

It is necessary to measure the texture of a pavement surface to evaluate the pavement’s ability in assisting a driver of a vehicle in bringing the vehicle to a stop to prevent an accidental collision. This ability of texture is referred to as skid resistance or friction. The texture of a pavement tells an engineer a lot about the condition of the pavement.  For example, from the pavement texture, one could even determine if segregation of aggregate is taking place. Changes in pavement macrotexture have been used to identify pavement segregation. Segregation refers to the separation of the coarse and fine fractions of aggregate in the paving mixture. Coarse areas tend to have lower asphalt content, lower density, and higher permeability. These areas tend to fail prematurely. Areas with high levels of segregation are reported to increase the life-cycle cost to the agency by as much as 50 percent. Most of the departments of transportation evaluate the pavement’s overall condition by its mere surface. All of the above are just some of the reasons why pavement texture needs to be evaluated in an inexpensive and real-time manner. In more recent times measurement of pavement surface texture has become more vital due to traffic noise. This is due to the existence of a probable correlation or relationship between the texture of the pavement surface to the noise generated by a tire-pavement interaction.

The current methods for monitoring pavement friction through pavement surface texture are expensive, subject to human judgment, and dangerous (ASSHTO 2008). A change to the monitoring procedure is needed to keep roads in a satisfying condition with minimal spending of financial resources (ASCE 2009, FHWA 2006). Therefore, monitoring pavement macrotexture through the tire-pavement generated sound can be proposed as an automated, inexpensive, and real-time method. 


Since surface macrotexture can be measured quite efficiently and since it provides important information regarding pavement safety and HMA construction quality (uniformity), this parameter could be efficiently included in the quality control procedures. The macrotexture of a pavement surface results from the large aggregate particles in the mixture. The two most promising applications of surface macrotexture measurements include pavement safety assessment and segregation detection. Surface macrotexture is a predominant contributor to wet-pavement safety (Anderson et al., 1998). Macrotexture measurements can be divided into two main classes: static measurements and dynamic measurements. Common static macrotexture measurement methods include the sand patch method, the outflow meter, and the circular texture meter.

A quick summary of different techniques of pavement macro structure evaluation is presented below. 

2.1. Profilometer

A profilometer is a measuring instrument used to measure a surface’s profile, in order to quantify its roughness. They measure and record the longitudinal profile in one or both wheel tracks. Inertial profilometers are capable of measuring and recording road surface profiles at speeds between 10 and 70 miles per hour. The profilometer measures and computes the longitudinal profile of the pavement through the creation of an inertial reference by using accelerometers placed on the body of the measuring vehicle. Relative displacement between the accelerometers and the pavement surface is measured with a noncontact light or acoustic measuring system mounted with the accelerometer on the vehicle body. Usually, a two-person crew is required to operate the van, and profilometer equipment is mounted in the van. To measure the distance traveled a distance encoder is typically used. The data processing is performed in real-time as the vehicle is driving. Also, profilometers usually measure an index called mean profile depth (MPD) which is a little bit different from the MTD. The problem with profilometers is that they are really expensive.

A standard method for determining the Mean Profile Depth (MPD) of the pavement macrotexture from a pavement profile is provided in ASTM E1845 (2009). The MPD is a two-dimensional estimate of the three-dimensional MTD. Some equipment manufacturers also use the Root Mean Square (RMS) of the profile. According to ASTM E1845 (2006), the measured profile of the pavement macrotexture is divided for analysis purposes into segments, each having a base-length of 100mm. The segment is divided in half and the highest peak in each half segment is determined. The difference between the resulting height and the average level of the segment is calculated for each half segment and the average of both halves is computed. The average peak value of both segments is reported as MPD (Fig. 1).

When MPD is used to estimate the MTD by means of a transformation equation, the computed value is called Estimated (mean) Texture Depth (ETD)

Figure 1. Schematic of Mean Profile Depth Computation (Flintsch., et al., 2003) 

2.2. Circular Texture Meter (CT Meter)

The Circular Track Meter, or CT Meter (ASTM E2157, 2009), has a laser displacement sensor mounted on an arm that rotates on a circumference with a 142mm radius and measures the texture with a sampling interval of approximately 0.9mm. Vehicle-mounted laser devices are typically used to measure macrotexture without disrupting traffic flow. It is a stationary profilometer that uses a laser to measure the profile of a circle 284 mm (11.2 inch) in diameter or 892 mm in circumference. The profile is divided into eight segments of 111.5 mm (4.4 in). The mean profile depth (MPD) is determined for each of the segments of the circle. The reported MPD is the average of all eight segment depths.

Figure 2. CT Meter (Hanson, et al., 2004) (left) and of Segments of CT Profile (ASTM E2157- 09, 2009) (right)

The circular texture meter method is suitable for the calculation of the average macrotexture depth from profile data. The results of this calculation have proven to be useful in the prediction of the speed dependence of wet pavement friction or macrotexture. This method becomes less accurate when used on porous or grooved surfaces. 

2.3. Sand Patch Method

One of the oldest, most often used, and relied on methods for measuring macrotexture of the pavement surface is the Sand Patch Method (ASTM E965, 2006). Even today most other tests are referenced to the sand patch method. This test method determines the average depth of pavement surface over a region by smoothing an area with sand. The method is designed to provide an average depth value of only the pavement macrotexture and is considered insensitive to pavement micro texture characteristics. A typical photograph of evaluation of macrotexture using the Sand Patch Method by NCAT member is shown in Fig. 3. Fig. 4 demonstrates an examples of pavement surfaces with their associated MTD values for different pavement types at NCAT: SMA with MTD = 1.2 mm (top); OGFC with MTD = 1.5 mm (center); Superpave with MTD = 0.6 mm (bottom) (Staiano, et al.,2018). The Sand Patch Method test procedure involves spreading a known volume of material, sand or glass beads, on a clean and dry pavement surface, measuring the area covered, and subsequently calculating the average depth between the bottom of the pavement surface voids and the top of surface aggregate particles. The macrostructure is quantified as MTD (Eq. 1) as per this method.            

…  (1)
V = Sampling volume mm3
D = Average diameter of the area covered by the material in mm

Figure 3. Evaluation of macrotexture using the Sand Patch Method by NCAT member (Hanson, et al.,2004)

Figure 4. Examples of pavement surfaces with their associated MTD values for different pavement types (Staiano, et al.,2018)

SMA with MTD = 1.2 mm (top); OGFC with MTD = 1.5 mm (center); Superpave with MTD = 0.6 mm (bottom)

It could be concluded that there are inherent downsides to using these methods. For both methods, the sand patch and the circular track meter, one requires the stopping of traffic that creates road congestion, which in turn causes safety hazards for both, drivers and workers. In addition, the sand patch and circular track meter methods are time-consuming.

The smooth and ribbed tire methods are destructive and expensive. Every time the test is performed the tire loses its surface threads; therefore, making the test expensive and less accurate.

The laser-inertial profilometers require that external equipment be installed, which makes the vehicle longer and wider, creating inconvenience for the driver of the equipped vehicle and for other drivers on the road. Also, while the laser-inertial profilometers are extremely expensive.

The discussed method will not only provide needed macrotexture information but will also monitor road noise at a very low cost without the need for a system operator; hence, making it truly automatic. 


Many studies have been performed on the effect of pavement macrotexture on the sound
generated by the tire-pavement interaction with much success. Few of them are presented
here. Instead of trying to get rid of the noise, it is proposed to use it to monitor pavement parameters.

3.1.     Frequency Range of the Spectrum Related to Macrotexture

Previous studies tried to correlate the weighted sound signal from the tire-pavement
interaction to the roughness of the pavement without much success. Hence, the
logical sequence is that correlation should be looked at between certain frequency ranges of noise
and texture.

The average frequency spectra can be divided into three mechanisms (Anfosso-Ledee, et al., 2007):

1.     The pavement texture impact (frequency range below 800 Hz)

2.     The tire tread impact (frequency range of 800 to 1200 Hz)

3.     The air displacement mechanism (above 1200 Hz)

It can be referred that the sound signal at a frequency below 800 Hz is generated due to the macrostructure of the road surface. So, this frequency range (≤800 Hz) should be considered to find a correlation of MTD to the noise generated by the tire-pavement interaction. Further researches show that sound pressure levels at low frequencies are best for monitoring pavement surface parameters like macrotexture.

3.2.         Temperature and Wind Effect

Currently, it is generally agreed that the temperature effect on noise is about -1 dB per 10°C (i.e. a temperature coefficient of -0.1 dB/°C). This means that as the temperature increases 10° C, the noise measured in dB would decrease by 1 dB. The temperature coefficient has a different value in different ranges of frequency. The temperature coefficient of -0.05 dB/°C is reasonable over the lower frequency range.

It has been reported that any experiment studying noise generated by tire-pavement interaction
under a moving vehicle should be performed when wind speeds are less than 5 m/s (Sandberg, et al., 2002). Wind effect is a function of a vehicle’s speed. Previous research has confirmed that this would only cause serious interference at speeds above 120 km/h.

Very little is known about the mechanisms responsible for sound generation on wet surfaces. Some authors have reported that the presence of water increases noise from 0 – 15 dB compared to dry conditions.


To validate the concept that macrotexture could be monitored through the sound generated by the tire-pavement interaction various experiments were performed in past. The test vehicle was equipped with directional microphones under the body to record the sound as the van drove over the pavement surface (Figure 5, Figure 6, and figure 7)

Figure 5.  Test vehicle equipped with directional microphones (Staiano, et al., 2018)

Figure 6. Test vehicle equipped with directional microphones (Saykin et al. 2013)

Figure 7. Test vehicle equipped with directional microphones (Saykin et al. 2013)

Generally, the signal received through microphones is in the form of a time history plot. The spectral density can be estimated through the Welch’s Method (Welch, 1967), with the magnitude measured in units of dB with a reference of 20 µPa (Eq. 2). The idea here is to get spectral density from the time history plot.


Where PRef = 2 10-6 is the reference sound pressure
PPa = pressure in units of Pascal
PdB = pressure in units of decibels (dB)

f (frequency) = 1 / T (period)

Figure 8. Example of the time history plot and sound pressure level plot (Welch, 1967) 

4.1. Energy and Correlation

In order to find a correlation to MTD one needs to know the parameter to correlate with MTD. One acoustical variable having sufficiently strong relationship to MTD may be Energy. In this context the term energy is very different from its conventional meaning. The acoustical variable Energy may be estimated by picking a frequency range and using the trapezoidal integration function. This is presented mathematically by Eq. 3.

 … (3)

f1 and f2= the minimum and maximum of the frequency range which is to be integrated.

P=Sound pressure intensity.

It is proposed that the value of f1 in Eq. (2) should be fixed at 40 Hz for the reason of wind noise reduction. The upper limit of f2 should be dependent on the speed of the traveling vehicle and should use the following classical frequency equation (Eq. 4):

   … (4)


 = the distance between the pavement maximum aggregate (should be assumed);

 = the speed of the vehicle (m/s)

Figure 9. Energy vs. MTD (Sakhaeifar et. al. 2018)

4.2.         Frequency Range Selection

To find the frequency band in which acoustic energy is strongly related to the pavement’s macrotexture, correlation studies were performed. The frequency range with the highest repeated correlation coefficient (energy to MTD) was assumed to be related to the pavement macrotexture. A plot at 80 km/h for Configuration A is shown as an example of a correlation coefficient for many frequency ranges (Fig. 10). It shows that the correlation depends on the maximum frequency range and very little on the minimum frequency range. For most of the runs performed, the maximum correlation coefficient was found to be in the frequency range of 40–400 Hz.

Figure 10. Correlation between energy and MTD (Staiano, et al., 2018)

4.3.         Optimum Microphone Location

While trying to detect MTD of pavement through the sound generated by the tire-pavement interaction, it is important to know where to place the microphones. The height of the microphones above the surface of the road and the exact location of the microphone relative to the body of a vehicle will have a big effect on monitoring the MTD or macrotexture of the
pavement. It is obvious that the closer the microphones to the contact patch of the tire and pavement surface the better information about pavement surface can be extracted from the signal. Fig.11 shows different possible locations of a vehicle for the attachment of microphones.

Figure 11. Possible locations of microphones (Saykin, et al., 2013)

4.3.1.     Horizontal Location of Microphones

To test for optimal microphone location, microphones were placed at the same height above the ground but in different locations along the underbody of the vehicle to study the configuration. Different microphones resulted in different correlations with the MTD as given in Table 1. The best microphone location seemed to be Microphone 5 because of the highest value of correlation coefficient (i.e. nearly equals to 1)

Table 1. The correlation of microphone data to MTD








Correlation Coefficient








4.3.2.     Vertical Location of Microphones

Overall, it could be safely said that microphones 4 and 5 are all good choices for microphone
location in regards to the effect of the height on the correlation of the tire-pavement generated
sound to MTD, although microphone 5 is the best location as shown in Table 2.

 Table 2. The correlation of vertical microphone data to MTD





Correlation Coefficient



Microphones that are close and oriented toward the contact patch of the rear tire have the highest correlation to MTD. It was observed that raising the microphones by 33 cm decreased the correlation by about 13%. It was also observed that higher vehicle speed has higher correlation values.

4.3.3.     The Speed Effect

Speed of the vehicle affects the correlation of energy to MTD, i.e. the higher the speed of a traveling vehicle, the better is the correlation. This is valid for speeds of 32–80 km/h. These correlation coefficients were repeatable. Fig. 12 shows the variation of the correlation coefficient with speed.

Figure 12. Change in correlation coefficient with speed (Staiano, et. al., 2013)

The optimal frequency range to be used can be modified as given in Eq. 4 (Saykin, et. al., 2013):



l = distance between the pavement maximum aggregate assumed to be 0.02 m

v = speed of the vehicle (m/s), valid from 8.94 to 22.32 m/s (32–80 km/h) and

f2 = upper limit of energy integration (Hz). 


5.1. Conclusions

After reviewing relevant research papers, it may be concluded that it is possible to measure the MTD index of pavement surface through an acoustical signal. The optimal range in the Spectral Density was found to be about 40 to 400 Hz at 50 Kmph. Also, in noisy situations keeping the frequency band from 40 to 400 Hz while changing speeds gave stable results. The data seems to show that with the change of speed the frequency band should also change, and in situations, with less noise, this gave a much better correlation. It was also confirmed that the higher the speed of a traveling vehicle, the better is the correlation.

The microphones around the tire receive a lot of their signal from the tire-pavement interaction even at a fair distance above the ground It could be observed that raising the microphones about 33cm (13 inches) lowered the correlation of energy to MTD by about 13 percent on average. It was shown that microphones installed at 50cm (19.5 inches) and 53 cm (21 inches) from the center of the rear wheel in either direction and at a height of 38 cm (15 inches) above the road surface are the best choice for microphone location in monitoring macrotexture. However, by installing the microphone at a distance of 21 inches from the center of the rear wheel in the backside of the vehicle, it is possible to conduct a continuous, constant, and accurate monitoring of the pavement macrotexture through the sound generated by the tire-pavement interaction.

5.2. Scope for further research

Further, the effects of water being present on the pavement surface, on tire size on macrotexture monitoring through the sound generated by the tire-pavement interaction should be studied. In addition to monitoring the macrotexture, this technique could be used simultaneously to monitor the environmental impact of vehicle noise. 


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