The influence of lateral position when measuring road surface characteristic data : introductory study

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VTI notat 20A-2012 Published 2012

www.vti.se/publications

The influence of lateral position when measuring

road surface characteristic data

Introductory study

Thomas Lundberg Leif Sjögren

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VTI Notat 20A-2012

Dnr: 2009/0701-29

Preface

On commission of the Swedish Transport Administration (Per Andersson) within the MIRIAM project (www.miriam-co2.net/), VTI has made a study of road surface

properties and their relation to rolling resistance. As a part of this, an introductory study has been conducted by using road surface measurements with the VTI Laser RST (Road Surface Tester). Thomas Lundberg, VTI, has been responsible for the planning and execution of measurements, data collection and the main data analysis. Leif Sjögren has supported the planning of the study as well as the analysis, and has been the main editor of this report.

Linköping, February 2012

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VTI notat 20A-2012

Quality review

External peer review was performed on 20 February 2012 by Jesper Elsander, the Swedish Transport Administration. Leif Sjögren has made alterations to the final manuscript of the report. The research director of the project manager, Anita Ihs examined and approved the report for publication on 23 March 2012.

Kvalitetsgranskning

Extern peer review har genomförts 20 februari 2012 av Jesper Elsander, Trafikverket. Leif Sjögren har genomfört justeringar av slutligt rapportmanus 1 mars 2012.

Projektledarens närmaste chef, forskningschef Anita Ihs har därefter granskat och godkänt publikationen för publicering 23 mars 2012.

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VTI notat 20A-2012 3

Summary

The knowledge of the size of variation of road surface characteristics data across a road section is not well known. In conjunction with development of rolling resistance models this has been brought up to date. Therefore a first but limited study has been done. The parameters IRI (International Roughness Index), MPD (Mean Profile Index) and megatexture have been measured with the VTI Laser RST (Road Surface Tester) on a number of road sections of various quality and size.

The main objective of the study was to get knowledge of the influence of the lateral position when measuring road surface characteristics data to be used for development of rolling resistance models. To find out the influence the sections were measured repeated times with different lateral positions and the variation in data has been analysed.

Furthermore, analyses have been done based on data from an earlier study showing the influence of when in time the data has been measured. It is important to know whether data used for model development has to be collected at the same time.

The study has shown that the MPD level varies with lateral position, especially on smaller roads (which are often used as test sections in model development). Also other road surface data such as IRI and megatexture vary. Another conclusion is that data has to be collected at the same time when doing model development. One suggestion is to collect all data with the same vehicle.

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Påverkan av den laterala mätpositionen vid insamling av vägytedata – inledande studie

av Thomas Lundberg och Leif Sjögren VTI

581 95 Linköping

Sammanfattning

Kunskapen om hur vägytedata varierar tvärs vägen är begränsad. I samband med utveckling av rullmotståndsmodeller har detta blivit aktuellt. Därför har en första men begränsad studie genomförts för att undersöka detta. Parametrarna IRI (International Roughness Index), MPD (Mean Profile Index) samt megatextur har mätts med VTI Laser RST (Road Surface Tester) på ett antal sträckor av olika kvalitet och storlek. Det främsta syftet med studien var att få kunskap om påverkan av den laterala mätpositionen vid insamling av vägytedata vid utveckling av rullmotståndsmodeller. För att ta reda på detta mättes teststräckor upprepade gånger med olika laterala positioner där variationen i data sedan analyserades.

Vidare har analyser gjorts på data från en tidigare studie för att studera variationen hos mätdata beroende på när i tiden mätningarna har gjorts. Det är viktigt att veta om data som används för modellutveckling måste samlas in vid samma tillfälle i tiden.

Studien har visat att MPD-nivån varierar med sidoläge, speciellt på mindre vägar (vilka ofta används som teststräckor i modellutveckling). Även annan vägytedata såsom IRI och megatextur varierar. En annan slutsats är att data måste samlas in vid samma tillfälle när de ska användas för modellutveckling. Ett förslag är att samla in all data med samma fordon.

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1 Background

The surface condition of the paved road network in Sweden, as well as in many other countries, is measured with dedicated equipment, e.g. Laser RST (Road Surface Tester). The information gathered is part of the short term planning of maintenance and

operations as well as the long term decisions on maintenance strategies. The position of the sensors that measures most of the parameters are configured and optimized for detection of the load from private cars. For example two of the lasers are positioned to coincide with the wheel tracks of private cars. When doing studies on the effects from heavy vehicles this may cause problems and very little knowledge exist regarding the variation of the road surface characteristic data depending of the lateral position of the measurement device on the road.

The influence of road surface characteristics, such as macrotexture and unevenness, on rolling resistance and fuel consumption is an important factor to consider when

planning and making maintenance strategies to meet the ever increasing demand for a greener environment. The ultimate goal should be to identify the level of road

macrotexture and unevenness which minimizes the total cost for the society and at a high level of traffic safety. MIRIAM is a project that was initiated by twelve partners from Europe and the USA. Each partner has contributed with internal/national funding for the project in order to provide a more sustainable and environmentally friendly road infrastructure. The objective is primarily to reduce rolling resistance – hence to lower CO2 emissions and increase energy efficiency. The investigation reported in this document is part of the MIRIAM project.

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2 Coastdown, a method to measure rolling resistance

By measuring the speed, the acceleration and the decrease of speed of a free rolling specified vehicle along a well-defined road section the rolling resistance can be assessed. It is assumed that the various resistive forces (e.g. effect from road surface characteristics) acting on the vehicle will make it slow down. By performing

measurements on sections with varying conditions it is possible to distinguish effects from the separate forces, (Karlsson et al., 2011).

Problem:

It has been questioned whether the lateral position on the road, when doing coastdown tests is important since the measurements of macrotexture and other road surface characteristics is measured in optimised positions related to cars. Macrotexture also changes with time and not always linearly. Is this effect important and of a size that it can influence the model development?

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3 Purpose of this study

The purpose of this study is to study and evaluate:

• The variation of road surface characteristics (influencing rolling resistance) when using different lateral position of the road surface characteristic measurements with the RST.

• Give information that can be used as input for sensitivity studies for rolling resistance models.

• Describing the variation of road surface characteristic data in the lateral position of different vehicle types e.g. heavy vehicles and cars.

Limitations

• The number of sections and pavement types is limited • The number of the test sites with different ages is limited

• Only MPD, IRI (International Roughness Index) and Megatexture are analysed • It is assumed that the normal lateral position of a heavy vehicle relative a

personal car is that the left wheel track is common (valid for countries with right hand traffic and on straight roads).

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4 Method

The test was carried out on six sections in two directions. A general description of the sections and the number of measurement runs can be seen in Table 1. The sections can be considered as typical Swedish roads. Each section has a length between 200 m and 500 m and the analysis have been done on 20 m average values. ABS XX represent flexible stone mastic asphalt with maximum aggregates of XX mm size, ABT XX represent dense mastic asphalt and Y surface treatment.

Figure 1 shows a typical section with the left wheel path as a common position for the different vehicle types. There are a narrow left wheel track and a wider right track. The lighter part of the road indicates the wear from the traffic. The pictures also illustrate different lateral positions used in the test.

Figure 1 Section E1 at E4.04 near Linköping with a five year old ABS16 pavement. The five pictures show the lateral position of the measurements used in the study.

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Table 1 Test sections used in the study.

Section measurements Number of Pavement Road cathegory Age (year)

E1 5 ABS16 High quality Highway 5

E2 4 ABS11 High quality Urban 5

J1 5 ABT11

High quality

Secondary 6 J2 4 ABT11 High quality Secondary 6 M1 5 ABT16 Low quality Secondary 8 M2 5 ABT16 Low quality Secondary 8

T1 5 ABS16

High quality

Secondary 0.5 T2 5 ABS16 High quality Secondary 0.5

V1 4 Y1 Low quality Secondary 6

V2 4 Y1 Low quality Secondary 6

Vi1 3 ABS11

Low quality

Secondary 10 Vi2 3 ABS11 Low quality Secondary 10

The measurements at the six road sections were done in both directions of the road which gives us 12 test sections. The measurements were at least done in three different lateral positions on each section. On wider roads measurements in five lateral positions was carried out. Figure 2 shows the average lateral position for all sections relative the “normal” measurement. Since the main purpose of this study is to see how road surface characteristic data varies with different lateral positions the normal position (N) and the positions to the right of normal (MH) are the most important to study. Earlier studies have shown (Sjögren and Lundberg, 2004) that the normal case is that the different vehicular types have the left wheels as a common track and the right track differs depending on the base width of the vehicle. The position N and MH in Figure 2 indicates that the measurements have been separated by approximately 350 mm in average. This is a rather good approximation for the difference in position of the right wheels for a car (N position) and for a heavy vehicle (MH position) when the left wheels are in the same track.

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Figure 2 Lateral position, in average, relative the normal measurement. H=Right (extreme), MH=Right, VM=Left, V=Left (extreme).

The lateral position was derived by doing cross-correlation calculations between the transverse profiles. The normal position was the reference. Before the cross correlations were done the transverse profiles were linearly interpolated in order to get a profile-value every mm. The lateral positions of the other profiles were calculated as the relative lateral distance to the normal position. The lateral distance was saved were the maximum fit (cross-correlation above 0.95) occurred when sliding the transverse profile on the interpolated reference profile. Approximately 72 % of the tested profiles had a correlation above 0.95 which indicates a god fit of the cross-correlation. For the remaining parts where the transverse profiles had a cross-correlation below 0.95 the lateral position was calculated using an average from the previous and the following cross-correlations which was “approved (above 0.95)” lateral distances (linear interpolation). We decided to accept this to be a good enough approximation for this study since the road surface characteristic data was gathered as a mean value every 20 m in traffic speed implying that the side position will not vary that much between two 20 m sections. In Figure 3 the three sensor positions used for measuring MPD and Megatexture are shown. The sensors measuring IRI are the two outer ones.

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Figure 3 Lateral positions of the sensors that measure the investigated indices in this study. The positions are indicated by dots. IRI is measured by the left and right sensor, Megatexture and MPD is measured by all three sensors.

In Figure 4 the lateral position of the different runs are indicated by a red cross in relation to the right sensor (the right orange dot).

Figure 4 The five different lateral positions (indicated by red crosses) used in the study in relation to the right sensor. The average lateral positions are shown here.

-15 -10 -5 0 5 10 0 500 1000 1500 2000 2500 3000 3500

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5 Results

In the following three figures the average value for IRI, Megatexture and MPD are shown for all 12 sections in three different lateral positions.

Figure 5 IRI, in average, for three different lateral positions.

The normal, previously observed, behaviour concerning IRI in the right wheel track, is that the closer the measurements are done to the edge of the road the higher the IRI right values get. This is confirmed by this test. The difference can be assumed to be larger for smaller roads with weaker shoulders compared to constructed high quality roads. This study includes both those road categories. Since IRI in left wheel track is measured nearer the middle of the road (where the road normally is strongest) the lateral position has minor influence on the left IRI value.

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Figure 6 Megatexture (RMS), in average, for the three different lateral positions.

Only small differences can be seen for the level of Megatexture in different lateral positions when looking at average values for all sections, see Figure 6.

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Figure 7 MPD, in average, for three different lateral positions.

The macrotexture expressed as MPD is influenced by wear from traffic load. The normal position MPD values can be seen in the N bars, in Figure 7. MPD in the wheel tracks are normally lower than in between. When varying the lateral position MPD in the right wheel track seems to be affected the most. This is not what was expected. Since the right wheel track normally is wider than the left the influence of the lateral position should not theoretically affect the MPD-level that much. On the sections used in this study there are approximately 10 % higher values for MPD right (from 1.12 mm to 1.24 mm) for the lateral position of heavy trucks than cars (N and MH). This is probably caused by the rather large amount of minor road categories used in the study. There are more details in the following tables where we can see the results for the individual sections.

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VTI notat 20A-2012 19 The following four tables (3, 4 and 5) show us a rather wide range of differences for IRI when looking on section level. Again there are much more differences in right wheel track than in the left. The only highway section, E1, shows that there are small

differences (2%) for IRI right when looking at the normal positions for heavy trucks and cars. The sections that are on roads with “better” quality are E1, E2, J1, J2, T1 and T2, the rest is on low quality secondary road network. The result varies a lot on the low quality secondary roads, up to 60 per cent on section V2 when comparing IRI right at position N and MH.

Table 2 Average of IRI right (mm/m) and differences in percent between the four lateral positions and the normal position.

Section V VM N MH H % Diff V-N % Diff VM-N % Diff MH-N % Diff H-N E1 Average 1.14 1.13 1.25 1.21 1.35 -8.4% -8.9% -2.0% 7.8% E2 Average 1.44 1.58 1.66 2.05 2.17 -10.9% -3.2% 21.6% 28.2% J1 Average 1.04 1.08 1.16 1.15 1.37 -12.1% -6.7% -1.1% 15.1% J2 Average 1.22 1.32 1.25 1.34 -4.6% 4.8% 6.4% M1 Average 1.64 1.86 2.22 2.37 3.65 -24.2% -15.6% 6.6% 66.7% M2 Average 1.64 2.11 2.62 2.82 4.55 -33.2% -15.6% 10.1% 79.4% T1 Average 2.03 2.00 2.10 2.25 2.41 0.4% -2.6% 7.2% 15.8% T2 Average 2.06 2.06 2.26 2.39 2.50 -8.0% -8.8% 5.3% 10.4% V1 Average 3.25 1.61 1.73 2.38 93.2% -2.9% 39.7% V2 Average 2.03 2.03 2.18 3.31 -5.5% -6.1% 60.3% Vi1 Average 1.35 1.89 2.23 -17.7% 16.6% Vi2 Average 1.07 1.20 1.40 -7.3% 19.7% Total average 1.75 1.57 1.76 2.01 2.29 0.1% -7.8% 14.1% 23.3%

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Table 3 Standard deviation of IRI right (mm/m) and the standard deviation of the differences between the four lateral positions and the normal position.

Section V VM N MH H Diff V-N Diff VM-N Diff MH-N Diff H-N E1 Std-dev 0.45 0.45 0.46 0.42 0.52 0.21 0.18 0.17 0.21 E2 Std-dev 0.49 0.63 0.76 1.07 1.15 0.28 0.17 0.33 0.43 J1 Std-dev 0.52 0.42 0.41 0.43 0.64 0.25 0.10 0.11 0.29 J2 Std-dev 0.41 0.38 0.28 0.41 0.22 0.15 0.22 M1 Std-dev 0.35 0.53 0.58 0.71 0.93 0.44 0.32 0.27 0.67 M2 Std-dev 0.54 0.44 0.72 0.83 1.46 0.77 0.59 0.57 1.21 T1 Std-dev 0.52 0.57 0.70 0.76 0.82 0.46 0.48 0.21 0.37 T2 Std-dev 0.54 0.58 0.61 0.69 0.72 0.33 0.25 0.15 0.30 V1 Std-dev 1.74 0.38 0.59 0.99 1.62 0.32 0.77 V2 Std-dev 0.80 0.74 0.71 1.01 0.49 0.36 0.97 Vi1 Std-dev 0.76 1.47 1.79 0.96 0.40 Vi2 Std-dev 0.19 0.27 0.31 0.28 0.30 Total Std-dev 0.95 0.65 0.82 1.05 1.28 0.83 0.43 0.52 0.76

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Table 4 Average of IRI left (mm/m) and differences in per cent between the four lateral positions and the normal position.

Section V VM N MH H % Diff V-N % Diff VM-N % Diff MH-N % Diff H-N E1 Average 1.17 1.06 1.21 1.14 1.11 2.0% -10.6% -2.8% -3.9% E2 Average 1.27 1.27 1.27 1.47 1.54 1.4% 0.3% 14.4% 19.9% J1 Average 1.20 1.26 1.16 1.11 1.03 5.1% 14.0% -6.3% -11.5% J2 Average 1.38 1.37 1.40 1.25 -1.1% -1.0% -10.7% M1 Average 1.95 1.68 1.54 1.58 1.84 31.4% 11.1% 4.4% 22.0% M2 Average 1.89 1.56 1.55 1.83 1.70 26.3% 0.2% 20.5% 12.2% T1 Average 2.39 2.36 2.23 2.06 2.05 11.6% 7.4% -5.0% -3.6% T2 Average 2.17 1.97 1.97 2.02 2.10 10.5% -0.2% 2.4% 6.0% V1 Average 3.53 2.34 2.31 1.95 63.1% 2.2% -13.0% V2 Average 2.61 2.67 2.44 2.11 9.2% 9.9% -8.7% Vi1 Average 1.20 1.51 1.68 -21.0% 9.8% Vi2 Average 0.87 1.00 1.34 -11.7% 38.3% Total average 1.96 1.63 1.65 1.62 1.60 14.6% -1.7% 2.5% 2.3%

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Table 5 Standard deviation of IRI left (mm/m) and the standard deviation of the differences between the four lateral positions and the normal position.

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VTI notat 20A-2012 23 The results for Megatexture have the same trend as the result of IRI. High quality roads give a much more stable Megatexture value in different lateral positions than low quality roads. And since some sections have very small levels of Megatexture the differences in percentage is very high.

Table 6 Average of Megatexture middle (mm) and differences in per cent between the four lateral positions and the normal position.

Section V VM N MH H % Diff V-N % Diff VM-N % Diff MH-N % Diff H-N E1 Average 0.39 0.42 0.47 0.46 0.44 -16.1% -8.7% -0.8% -5.3% E2 Average 0.26 0.23 0.31 0.30 0.26 -12.2% -23.9% -0.3% -13.6% J1 Average 0.21 0.14 0.15 0.18 0.30 43.3% -4.2% 19.6% 106.5% J2 Average 0.19 0.14 0.15 0.30 27.9% -1.4% 103.3% M1 Average 0.13 0.23 0.29 0.28 0.12 -54.9% -21.0% -4.6% -59.0% M2 Average 0.09 0.20 0.24 0.19 0.13 -63.8% -17.0% -20.6% -47.9% T1 Average 1.00 0.93 1.01 1.10 1.02 0.4% -6.9% 9.4% 3.0% T2 Average 1.12 1.06 1.09 1.04 1.00 3.5% -2.9% -4.4% -7.3% V1 Average 0.27 0.24 0.24 0.19 15.6% 0.0% -21.3% V2 Average 0.20 0.22 0.24 0.19 -11.5% -4.2% -20.9% Vi1 Average 0.53 0.54 0.49 -1.7% -8.3% Vi2 Average 0.59 0.57 0.49 3.4% -14.5% Total average 0.47 0.48 0.51 0.50 0.57 -6.4% -6.1% 0.5% -3.2%

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Table 7 Standard deviation of Megatexture middle (mm) and the standard deviation of the differences between the four lateral positions and the normal position.

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Table 8 Average of Megatexture right (mm) and differences in per cent between the four lateral positions and the normal position.

Section V VM N MH H % Diff V-N % Diff VM-N % Diff MH-N % Diff H-N E1 Average 0.47 0.46 0.45 0.48 0.53 6.6% 2.9% 8.3% 19.3% E2 Average 0.32 0.37 0.32 0.30 0.39 1.7% 19.3% -2.9% 27.4% J1 Average 0.22 0.29 0.28 0.21 0.13 -20.7% 3.7% -23.0% -54.3% J2 Average 0.22 0.33 0.31 0.20 -28.8% 7.6% -34.3% M1 Average 0.26 0.16 0.11 0.10 0.26 139.1% 48.4% -7.6% 137.8% M2 Average 0.25 0.15 0.11 0.10 0.35 122.2% 33.9% -10.5% 212.2% T1 Average 0.99 1.08 1.02 0.99 1.08 -2.2% 6.7% -2.4% 6.5% T2 Average 1.05 1.02 1.00 1.00 1.08 5.6% 2.5% 0.8% 8.2% V1 Average 0.33 0.19 0.20 0.49 69.3% -5.9% 146.6% V2 Average 0.24 0.19 0.21 0.44 13.6% -8.9% 112.2% Vi1 Average 0.48 0.48 0.50 1.0% 5.2% Vi2 Average 0.54 0.54 0.58 0.6% 8.9% Total average 0.51 0.50 0.48 0.53 0.65 24.3% 6.3% 19.1% 37.0%

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Table 9 Standard deviation of Megatexture right (mm) and the standard deviation of the differences between the four lateral positions and the normal position.

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Table 10 Average of Megatexture left (mm) and differences in percent between the four lateral positions and the normal position.

Section V VM N MH H % Diff V-N % Diff VM-N % Diff MH-N % Diff H-N E1 Average 0.58 0.50 0.39 0.42 0.48 50.6% 28.8% 9.7% 23.8% E2 Average 0.29 0.32 0.27 0.30 0.35 15.2% 28.7% 19.7% 44.5% J1 Average 0.23 0.32 0.30 0.23 0.14 -23.4% 8.1% -22.4% -52.7% J2 Average 0.28 0.34 0.34 0.16 -17.0% 1.2% -51.8% M1 Average 0.18 0.11 0.10 0.12 0.29 81.8% 7.9% 17.9% 195.9% M2 Average 0.23 0.08 0.08 0.12 0.23 212.1% 9.5% 55.5% 205.6% T1 Average 1.02 1.03 1.02 0.95 0.99 -0.3% 1.6% -7.4% -3.1% T2 Average 1.12 1.14 1.12 1.08 1.09 0.1% 1.5% -3.7% -2.7% V1 Average 0.26 0.16 0.22 0.26 25.0% -25.9% 22.8% V2 Average 0.21 0.15 0.21 0.24 2.9% -25.2% 22.3% Vi1 Average 0.40 0.40 0.51 0.7% 29.9% Vi2 Average 0.47 0.47 0.60 1.4% 30.1% Total average 0.54 0.49 0.47 0.49 0.60 32.0% 4.3% 10.0% 40.3%

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Table 11 Standard deviation of Megatexture left (mm) and the standard deviation of the differences between the four lateral positions and the normal position.

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VTI notat 20A-2012 29 On section level the MPD values give a rather stable result on sections with high quality roads and a more dispersed result on the low quality roads, as expected. The road we would expect to have most variation, sections V1 and V2, has in average the most divergently results. This road section has the pavement type surface dressing and is also rather old. The traffic load on this combination has a big effect on the MPD level depending on the lateral position.

Table 12 Average of MPD left (mm) and differences in per cent between the four lateral positions and the normal position.

Section V VM N MH H % Diff V-N % Diff VM-N % Diff MH-N % Diff H-N E1 Average 1.30 1.15 0.90 0.94 1.05 45.2% 28.3% 4.5% 16.9% E2 Average 0.73 0.93 0.83 0.78 0.87 -10.2% 15.0% -3.4% 8.8% J1 Average 0.77 0.84 0.82 0.74 0.54 -5.5% 2.0% -9.4% -33.5% J2 Average 0.87 0.90 0.87 0.65 -1.0% 2.7% -25.1% M1 Average 0.55 0.48 0.41 0.42 0.70 34.3% 17.3% 2.4% 71.5% M2 Average 0.65 0.41 0.37 0.40 0.67 74.1% 9.5% 7.2% 80.8% T1 Average 2.10 2.01 1.92 1.75 1.97 9.7% 5.5% -8.7% 2.2% T2 Average 2.43 2.43 2.28 2.17 2.36 6.7% 7.2% -4.9% 4.0% V1 Average 0.91 0.58 0.76 0.97 21.0% -22.3% 30.3% V2 Average 0.74 0.54 0.81 0.93 -7.4% -32.4% 16.3% Vi1 Average 1.07 1.08 1.24 -0.5% 14.9% Vi2 Average 1.17 1.19 1.32 -2.0% 11.6% Total average 1.29 1.17 1.12 1.14 1.33 20.4% 4.4% 3.9% 17.7%

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Table 13 Standard deviation of MPD left (mm) and the standard deviation of the differences between the four lateral positions and the normal position.

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Table 14 Average of MPD middle (mm) and differences in per cent between the four lateral positions and the normal position.

Section V VM N MH H % Diff V-N % Diff VM-N % Diff MH-N % Diff H-N E1 Average 0.92 0.96 1.04 1.02 0.95 -11.7% -7.3% -1.3% -8.3% E2 Average 0.81 0.68 0.80 0.84 0.79 4.1% -13.0% 6.7% -0.2% J1 Average 0.71 0.57 0.56 0.66 0.82 26.2% 1.6% 17.0% 45.5% J2 Average 0.65 0.55 0.56 0.86 16.8% -2.2% 53.1% M1 Average 0.44 0.61 0.69 0.69 0.46 -36.1% -10.4% 0.6% -33.4% M2 Average 0.38 0.49 0.67 0.62 0.50 -43.5% -26.6% -7.7% -25.0% T1 Average 1.89 1.70 1.95 2.10 1.91 -0.9% -11.2% 8.9% 0.5% T2 Average 2.27 2.15 2.33 2.20 2.04 -1.6% -7.5% -5.1% -11.6% V1 Average 0.93 0.88 0.95 0.79 -1.4% -6.9% -17.2% V2 Average 0.81 0.86 0.92 0.74 -10.1% -5.1% -18.8% Vi1 Average 1.21 1.21 1.25 0.3% 3.6% Vi2 Average 1.38 1.20 1.21 15.8% 0.8% Total average 1.12 1.12 1.19 1.19 1.22 -6.5% -5.5% 1.4% -5.8%

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Table 15 Standard deviation of MPD middle (mm) and the standard deviation of the differences between the four lateral positions and the normal position.

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Table 16 Average of MPD right (mm) and differences in per cent between the four lateral positions and the normal position.

Section V VM N MH H % Diff V-N % Diff VM-N % Diff MH-N % Diff H-N E1 Average 1.04 1.00 0.95 1.03 1.17 10.1% 6.4% 9.1% 23.1% E2 Average 0.80 0.92 0.86 0.87 1.01 -6.5% 7.5% 2.1% 19.1% J1 Average 0.73 0.82 0.79 0.71 0.51 -7.7% 4.0% -10.6% -35.2% J2 Average 0.76 0.88 0.87 0.75 -12.8% 1.2% -13.4% M1 Average 0.65 0.52 0.42 0.37 0.64 60.2% 24.9% -12.4% 54.9% M2 Average 0.69 0.53 0.41 0.37 0.63 66.0% 27.3% -11.6% 50.5% T1 Average 1.92 2.05 1.87 1.85 2.05 2.4% 9.3% -1.1% 9.4% T2 Average 2.25 2.09 2.04 2.09 2.35 10.2% 2.2% 3.3% 15.0% V1 Average 1.09 0.78 0.84 1.60 31.1% -6.4% 91.5% V2 Average 0.91 0.74 0.83 1.40 9.2% -11.4% 68.4% Vi1 Average 1.21 1.17 1.26 4.3% 8.8% Vi2 Average 1.26 1.31 1.32 -4.1% 0.2% Total average 1.21 1.17 1.13 1.25 1.38 14.8% 4.5% 13.0% 19.0%

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Table 17 Standard deviation of MPD middle (mm) and the standard deviation of the differences between the four lateral positions and the normal position.

5.1 Influence of when in time measurements are done

The level of most road surface characteristic data, such as IRI and rut depth, increases with time and traffic load. This is not the case for MPD and is especially crucial to consider when deriving the relationship between MPD and rolling resistance. To show the importance of when in time the measurements of surface characteristic data are done an example of a new IMT layer (close to a surface dressing but sealed with smaller stone materials mixed with binder) is presented in this chapter. The first measurement was done within two months after treatment. After one winter three measurements were done at different dates, see Figure 8. On long parts of the road the MPD-values were decreased up to 50% and other parts showed normal changes (a decrease of 20%), see Figure 8. This object is not of a common type but it shows the importance of making the coastdown and MPD measurements at the same time or at least within a couple of weeks. A normal change for an object like this would be about a 20 per cent decrease of the MPD level along the entire section.

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Figure 8 MPD measurements on E691 at Bestorp, Sweden. The first measurement was done within two months after treatment.

IRI is also an important parameter when establishing correlation between rolling resistance and road surface characteristic data but it does not change nearly as much as MPD. Of course there are some objects with problems that can be affected more than normally. A normal yearly change of IRI is within 0-5 % increase depending on road category.

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6 Conclusions

and

discussion

Even if this is a limited test we can draw the conclusion that it is very important to align measurements. But since the study was rather limited the result cannot be generalised. Preferably the measurements of all road surface characteristics and the investigated effects should be done with the same vehicle. Since it is very expensive to equip a test vehicle with all needed equipment it is not practically implementable.

The most important advice when planning a study is to choose test sections in order to get a good variety of the combinations of surface characteristics used and a minimum influence of the lateral position. It is also very important to give careful instructions to different drivers of test vehicles in order to achieve the same lateral position.

Concerning the selection of different test sections one can’t exclude the secondary road network because this is where you can find a broad range of roads with a broad range of different levels of for example IRI and MPD.

A second conclusion that is as important as the lateral position is to do the

measurements in a close range of time. For example it is not recommended to use the same road characteristics data in more than one coastdown study if there is too long time in between the studies, and especially if a winter has past. The latter may be more important for the Nordic countries since the winter maintenance and studded tyres affects the surface in the MPD-range rather much. Also warm weather affects the pavement macrotexture and its MPD value.

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References

Karlsson, R., Hammarström, U., Sörensen, H. och Eriksson, O.: Road surface

measurement on rolling resistance, coast down measurements for a car and a HGV

VTI Notat 24A-2011. VTI. Linköping

Sjögren, L.; Lundberg, T.: Designing an up to date rut depth monitoring profilometer,

requirements and limitations , Conference paper (2004), 12 s, 2nd European pavement

and asset management conference,. 21st to 23rd March, 2004, Estrel Convention Center, Berlin

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The influence of lateral position when measuring road surface characteristic data : introductory study