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Highway Noise Criteria Study:

Relations Among Frequency

Rating Procedures
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OF STANDABDS,

.^LIBRARY

APft 8 1983

Highway Noise Criteria Study:

Relations Among Frequency Rating Procedures

trf«

Daniel R. Flynn and Simone L Yaniv

National Engineering Laboratory

National Bureau of Standards

Washington, DC 20234

Sponsored by:

Office of Research, Environmental Design and Control

Federal Highway Adnninistration

U.S. Department of Transportation

Washington, DC 20590

Kt**^ o^ C,

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

Issued February 1983
National Bureau of Standards Technical Note 1113-3

Natl. Bur. Stand. (U.S.), Tech. Note 1113-3, 57 pages (Feb. 1983)

CODEN: NBTNAE

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 1983

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402

Price $4.75

(Add 25 percent for other than U.S. mailing)
Abstract

A series of calculations was performed to ascertain how well one frequency-weighted rating, such as weighted sound level, loudness level, or perceived noise level, may be predicted from another such rating. A total of

103 average sound level spectra, measured at several distances from different

types of highways, were used in these calculations. It was found that knowing

a single noise rating, such as the A-weighted sound level, enables one to

predict other outdoor ratings in this set of 103 spectra with a standard

deviation of the order of 1 to 2 dB. If, in addition, traffic speed and mix

and the distance to the highway are taken into account, these standard

deviations can be reduced to 0.5 to 1 dB, depending upon the particular noise

rating of interest. Equations are given for predicting one rating from

another; the associated standard deviations are presented as a measure of how

well any given rating can be predicted from a single measured, or otherwise

known, noise rating. It is concluded that it is not very critical which

frequency-weighting procedure is used in conjunction with highway noise

criteria since one descriptor can be predicted from another with a rather

small statistical uncertainty. Thus, if human response criteria, or stimulus-response relationships, have been developed in terms of one frequency-weighting procedure, these may be translated into equivalent

criteria expressed in terms of a metric that is easier to measure or predict.

Key words: acoustics; environmental pollution; highway noise, motor

vehicle noise; noise; noise control; sound; traffic noise; transportation noise,

111
Table of Contents

Page

1

.

INTRODUCTION 1

2. TRAFFIC NOISE SPECTRA 2

3. NOISE DESCRIPTORS 8

4. ANALYSIS 9

4.1 Additive Constant 11

4.2 Functional Relationship Between Noise Ratings 14

5. REFERENCES 29

APPENDIX A. LITERATURE ON TRAFFIC NOISE SPECTRA 31

A.l Literature Data 31

A. 2 Data from Present Study 42

A. 3 References 46

APPENDIX B. EQUIVALENT LANE CONCEPT 47

List of Tables

Table 1. Description of sites used for recordings of actual- 3

traffic sounds.

Table 2. Sites, dates, times, and traffic speeds for actual- 4

traffic noise recordings.

Table 3. Traffic flow rates and mixes for actual-traffic 5

noise recordings.

Table 4. Summary of traffic conditions for actual-traffic 7 noise recordings.

Table 5. Number of 1/3-octave band average sound pressure 7

level spectra used for the analyses in this report.

Table 6. Values of M, the average difference between a pair 12

of ratings, and s, the standard deviation of these

differences around their mean, for the outdoor LEQ

free-flowing traffic noise spectra from the present

investigation.

iv
Table 7. Values of M, the average difference between a pair

of ratings, and s, the standard deviation of these

differences around their mean, for the outdoor LEQ

intersection-traffic noise spectra from the present

investigation.

Table 8. Regression coefficients (for Eq. (18)), and associated

standard deviations of the residuals, for predicting

different noise ratings from the A-weighted sound

level for free-flowing traffic.

Table 9. Regression coefficients (for first and third terms

of Eq. (18)), and associated standard deviations of

the residuals, for predicting different noise ratings

from the A-weighted sound level for intersection

traffic.

Table Al. Summary of conditions for the published traffic noise spectra.

Fage

13

26

28

36

List of Figures

Figure 1. Difference between the Mark VI Loudness Level and the

A-weighted sound level, as a function of effective

distance, for the three recording times at the COMSAT

site.

Figure 2. Difference between the Mark VI Loudness Level and the

A-weighted sound level, as a function of effective

distance, for the four recording times at the 195 site.

Figure 3. Difference between the Mark VI Loudness Level and the

A-weighted sound level, as a function of effective

distance, for the five recording times at the B-W PKWY

site.

Figure 4. Difference between the Mark VI Loudness Level and the

A-weighted sound level, as a function of effective

distance, for the four recording times at the GUDE DR.

site.

Figure 5. Difference between the Mark VI Loudness Level and the

A-weighted sound level, as a function of effective

distance, for the four recording times at the RT. 28

site.

20

20

21

21

22
Page

Figure 6. Average trends of the difference between the Mark VI 22

Loudness Level and the A-weighted sound level, as functions of effective distance, for the five sites

with free-flowing traffic.

Figure 7. The difference between the Mark VI Loudness Level and 24

the A-weighted sound level, each interpolated to an effective distance of 50 m, as a function of the

equivalent fraction of heavy trucks. The lower line

corresponds to the twelve recording times for

interstate highways. The upper line corresponds to

the eight recording sessions along the side of

secondary roads.

Figure 8. The slopes of the curves in Figs. 1-5 (i.e., the 25

slopes of plots of the difference between the Mark VI

Loudness Level and the A-weighted sound level as functions of the logarithm, to the base ten, of the

ratio of the effective distance) as a function of the

equivalent fraction of heavy trucks.

Figure 9. Difference between the Mark VI Loudness Level and the 27

A-weighted sound level, as a function of effective

distance, for the four recording times at the 355 and

S.G. intersection.

Figure 10. Difference between the Mark VI Loudness Level and the 27

A-weighted sound level, as a function of effective

distance, for the four recording times at the 355 and

Q.O. intersection.

Figure 11. Average trends of the difference between the Mark VI 28

Loudness Level and the A-weighted sound level, as functions of effective distance, for the two intersections.

Figure Al. Published "average" traffic noise spectra at 7.5 m. 32

Figure A2. Published LIO traffic noise spectra at 4 to 25 m. 33

Figure A3. Published "average" traffic noise spectra at 15 m. 34

Figure A4. Published "average" traffic noise spectra at 30 m. 35

Figure A5. Normalized "average" traffic noise spectra at 7.5 m. 37

Figure A6. Normalized LIO traffic noise spectra at 4 to 25 m. 38

Figure A7. Normalized "average" traffic noise spectra at 15 m. 39

Figure AS. Normalized "average" traffic noise spectra at 30 m. 40

VI
Page

Figure A9. Range defined by normalized traffic noise spectra. 41

Figure AlO. Normalized LEQ octave band spectra for each of the 43 seven actual traffic noise recording sites of

Section 2.1 at the 15-m microphone position.

Figure All. Range of normalized octave band levels for each of 44

the seven actual traffic noise recording sites of

Section 2.1 at the 15-m microphone position.

Figure A12. Comparison of ranges of normalized octave band levels 45

for the published data of Fig. A9 and for the data of

Fig. All from the current study.

Vll

1. Introduction

The analyses in this report were prepared in conjunction with a larger

research program designed to assess the ability of various environmental-noise

rating indices to predict accurately the adverse response associated with

exposures to time-varying highway noise[l-5]l. Since most of the noise

indices developed for rating time-varying noise are based upon the A-weighted

level, the psychoacoustic study conducted as part of this program focuses only

upon the effects of temporal factors upon human response; the effects of

frequency weighting were not addressed. This is not to say that frequency

weighting is not important from a human response viewpoint, but rather that

this question can be addressed, without any additional psychoacoustic studies,

through analysis of available spectral data on both free-flowing and

stop-and-go traffic.

Specifically, if it is shown, for highway noise spectra, that different

frequency weighting procedures are highly correlated and that one can predict,

with sufficient accuracy, one weighting function from another, then it may not

matter much which weighting procedure is utilized in a noise-rating index.

The extent to which it is important as to which frequency weighting

procedure is used can be addressed in the context of the following question:

Let Rl designate a rating procedure that incorporates a particular

temporal-weighting procedure and a particular frequency-weighting

procedure. Let R2 designate a rating procedure that incorporates

the same temporal-weighting procedure as is used for Rl but utilizes

a different frequency-weighting procedure. If a criterion level is

set at R1=X dB, what difference does it make, in terms of separating

"pass" situations from "fail" situations, if one elects instead a level R2=F(R1=X), where F( ) is an empirically determined

transformation from Rl to R2 for the particular spectrum shapes of

concern?

For example, the A-weighted noise level is approximately 11 dB less than

the perceived noise level for typical highway noise spectra. Based on this

simple relationship, how accurately could one predict perceived noise levels

from observed A-weighted levels? Could the prediction be improved by

incorporating adjustments to account for traffic conditions or distance from

the highway?

In the present report, such questions are addressed by examining the

relations among different frequency ratings for the average sound pressure

level spectra obtained in the course of the present investigation.

^Figures in square brackets indicate the literature references in Section 5 of

this report.
2. Traffic Noise Spectra

As part of the present Investigation, fifteen-minute recordings of actual

traffic noise were made at four microphone positions (7.5, 15, 30, and 60 m

from the centerline of the near lane) at several times of the day at each of seven sites, five representing nominally constant-speed traffic and two

representing stop-and-go intersection traffic. The recordings that resulted

were subjected to extensive analysis. In Ref. 1, the analysis procedures that were used are described and tables and graphs are included which document, for

each recording, the 1/3-octave band spectra and numerous noise descriptors

computed from the time-histories of the A-weighted sound level. For the

present analyses, the 1/3-octave band spectra of average sound levels (LEQ)

from Ref. 1 were used. These spectra are documented in Appendix B of Ref. 1,

where the 1/3-octave band levels are given for band center frequencies from 50

to 10,000 Hz. The sites and the traffic speeds and mixes for these recordings

are summarized below.

All recording sites were selected with the following general criteria in

mind:

o propagation over grass,

o essentially level terrain beside highway,

o essentially no hills or curves on highway, and

o no barriers between highway and microphone locations.

In selecting the particular sites, these general criteria were interpreted as follows. There were no hills that would require trucks to downshift or to

lose speed while going uphill. There were no curves that would result in tire

squeal at normal highway speeds and, specifically, no curves of less than

300 m radius. Sites were selected where the ground elevation at the 60-m

microphone location was within plus 3 m or minus 1 m of the highway elevation

at the center of the nearest lane and, further, where a length of highway of

at least 300 m was visible from a position 0.6 m above the ground at all four

microphone locations.

The sites representing constant-speed traffic conditions were selected in

order to cover a range of traffic densities (from quite light to

near-capacity), a range of traffic speed limits (48-88 km/hr), a range of

highway sizes (two- to eight-lane), and a range of values for the proportion of

truck traffic. The sites near intersections were -selected to represent a range of traffic densities and a range of values for the proportion of truck

traffic. The seven sites that were used are listed in Table 1.

All recordings of actual traffic noise were started between the hours of

1300 and 1700 on weekdays during the period 13-24 June 1977. At the times of

recording, the air temperature was between 21 and 29 "C, there was no precipitation, and the road surfaces were dry. Wind speeds were low, less

than 4 m/s, except for occasional gusts to 6-8 m/s on 17 and 21 June.
Table 1. Description of sites used for recordings of actual-traffic sounds.

Site Type of Highway Truck Traffic Speed Limit,

km/hr

COMSAT

195

B-W PKWY

RT. 28

GUDE DR.

355 & SHADY GR,

355 & Q.O. RD.

Four-Lane Interstate

Eight-Lane Interstate

Four-Lane Parkway

Dual-Lane Road

Dual-Lane Road

Intersection of FourLane & Dual-Lane Roads

Intersection of Two

Four-Lane Roads

Light 88

Fairly Heavy 88

None 88

Light 48-64 (see text)

Heavy 64

Moderate Controlled by

traffic light

Light Controlled by

traffic light

The times at which recordings were initiated at the various sites are shown in Table 2. Also shown in this table, for the five sites where there was essentially constant-speed traffic, are the average traffic speeds, and

the standard deviations and ranges of these speeds, in each direction during

each recording session. No traffic speed measurements were made at the two

sites where there was stop-and-go traffic.

Continuous video recordings of traffic flow were made during each

recording session. Each video tape was analyzed, by visual inspection, to

determine the number of automobiles, medium trucks, and heavy trucks traveling

in each direction over the duration of the corresponding traffic noise

recording. These data are presented in Table 3. For purpose of

classification, the three vehicle categories were defined as follows:^

Automobiles - all 2-axle, 4-tire vehicles

Medium trucks - all 2-axle, 6-tire vehicles plus all buses and

motorcycles

Heavy trucks - all vehicles with three or more axles.

For the two sites at which there was stop-and-go intersection traffic, the

"near-side" data in Table 3 correspond to the sum of traffic flows in the near

lanes of both highways while the "far-side" data are for the far lanes of both

highways.

•"^An "automobile" pulling a trailer was classified as an "automobile."

A "bob-tailed" tractor (i.e., a tractor that was not pulling a trailer) was classified as a "medium truck" if it had two axles and as a "heavy

truck" if it had three axles.
Table 2. Sites, dates, times, and traffic speeds

for actual-traffic noise recordings.

Time of

Vehicle Sp eed, km/hr

Near-Side Far-Side

Standard Standard

Site DateCa) Initiation Avg. Deviation Range Avg. Deviation Range

COMSAT 15 1510 92 5 74-103 92 8 74-117

15 1600 92 6 77-106 93 8 74-111

15 1700 85 6 68-101 92 8 72-109

195 23 1400 89 6 56-105 93 8 76-108

23 1500 92 6 72-113 92 8 74-114

23 1600 93 6 79-114 93 8 69-113

23 1700 92 6 69-105 93 6 72-114

B-W PKWY 20(c) 1420 90 6 79-111 85 8 71-114

20(c) 1500 89 6 72-106 85 8 69-113

21(b,c) 1515 90 8 71-121 87 6 71-101

21(b,c) 1600 90 6 69-108 87 8 69-105

21(b,c) 1700 89 6 72-111 87 6 71-106

RT. 28 17(b) 1300 69 6 60-89 69 8 55-88

17(b) 1415 71 8 55-89 69 10 47-85

17(b) 1500 71 10 42-84 69 8 47-85

17(b) 1600 69 6 50-84 66 6 56-85

GUDE DR. 16 1400 64 6 40-80 63 8 39-79

16 1500 66 6 48-80 63 6 51-80

16 1600 66 8 48-92 61 10 40-93

16 1700 66 6 50-85 63 8 40-92

355 & 22 1400 - - - - - SHADY GR. 22 1500 - - ~ - - -

22 1600 - - - - - -

22 1700 - - — — —

355 & 24 1445 _ - - - - Q.O. RD. 24 1515 - - - - - - 24 1600 - - - - - - 24 1700 — — — — "

All dates correspond to a calendar day in June 1977'

On these dates, there were occasional wind gusts up. to 6-8 m/s; on all other

dates, wind speeds were less than 4 m/s.

'For these runs, no recordings were made with a microphone at 60 m since the

site was heavily wooded beyond about 40 m.
Table 3. Traffic flow rates and mixes for actual-traffic noise recordings,

Vehic.Le Mix

Near-Side Far-S Lde

Tota]. % % % Total^ % % %

Time of Traffic Auto- Medium Heavy Traffic Auto- Medium Heavy

Site Date^ Initiation Rate mobiles Trucks Trucks Rate mobiles Trucks Trucks

COMSAT 15 1510 1040 87.2 2.9 9.9 950 89.2 2.7 8.1

15 1600 2010 93.2 1.2 5.6 880 92.8 1.1 6.1

15 1700 3340 96.0 1.7 2.3 820 91.2 3.4 5.4

195 23 1400 1280 77.1 6.6 16.3 1580 77.5 7.6 14.9

23 1500 1420 85.8 4.8 9.3 1700 78.9 7.6 13.5

23 1600 1490 88.3 4.5 7.3 2110 86.4 4.0 9.7

23 1700 1710 88.4 3.1 8.5 2620 88.6 4.0 7.4

B-W PKWY 20 1420 970 98.2 1.4 0.5 1220 98.2 1.1 0.7

20 1500 1140 98.6 1.1 0.4 1400 97.7 1.7 0.6

21 1515 1340 97.0 3.0 0.0 1490 99.1 0.9 0.0

21 1600 1860 98.0 1.5 0.4 1880 98.1 1.5 0.4

21 1700 2200 98.9 0.7 0.4 1730 98.1 1.4 0.5

RT. 28 17 1300 350 96.0 1.3 2.7 310 88.2 10.3 1.5

17 1415 390 90.7 7.2 2.1 370 93.3 5.6 1.1

17 1500 350 95.6 2.2 2.2 360 93.6 4.3 2.1

17 1600 620 96.2 1.9 1.9 510 89.1 8.5 2.3

GUDE DR. 16 1400 550 86.0 5.0 9.1 480 89.6 3.8 6.6

16 1500 510 84.0 9.6 6.4 570 84.4 2.8 12.8

16 1600 600 89.2 2.7 8.1 710 88.1 8.5 3.4

16 1700 590 92.4 1.4 6.2 840 94.6 4.4 1.0

355 & 22 1400 1180 87.6 7.8 4.5 1580 88.5 5.6 5.8

SHADY GR.'^ 22 1500 1070 88.5 4.9 6.7 1750 89.3 6.4 4.3

22 1600 1320 92.1 4.3 3.6 2360 91.6 5.6 2.7

22 1700 1270 94.7 3.8 1.6 2990 97.6 1.5 0.9

355 & 24 1445 1900 93.7 4.6 1.6 1190 94.1 4.0 1.8

Q.O. RD.'^ 24 1515 1950 93.4 4.9 1.7 1370 90.6 6.1 3.3

24 1600 2510 95.8 3.1 1.1 1580 94.6 3.3 2.0

24 1700 3650 98.8 1.2 0.0 1730 96.7 2.8 0.5

All dates correspond to a calendar day in June 197 7.

Total vehicles per hour, computed from the observed traffic rates over the duration of

each noise recording.

See Appendix A of Ref. 1 for more detail on traffic counts at these intersections
The data of Tables 2 and 3 are summarized in Table 4.

Recordings were made at distances of 7.5, 15, 30, and 60 m (from the

centerline of the near lane) for each of the 28 recording times listed in

Tables 2 and 3 except at the B-W PKWY site where no recordings were made with

a microphone at 60 m since the site was heavily wooded beyond about 40 m. Thus

a total of 107 recordings were made, each nominally 15 min in duration.

Examination of the 1/3-octave band average sound pressure level spectra, as well as audition, revealed excessive background noise (probably due to air

conditioning equipment at a nearby building) at the 60-m microphone location

at the RT. 28 site; accordingly, spectra from these four recordings were not

used for the study described in the present report. Thus a total of 103

spectra were used, as summarized in Table 5. There were 43 spectra

corresponding to recordings made along interstate highways and 28 spectra for

noise from secondary roads, making a total of 71 spectra for free-flowing

traffic. The number of spectra for noise from intersection traffic was 32.

A comparison of traffic noise spectra from the literature with spectra

from the present investigation is given in Appendix A. It is concluded there

that the shapes of traffic noise spectra from the literature are consistent

with the spectra described above and used for the analyses in the present

report

.
Table 4. Summary of traffic conditions for actual-traffic noise recordings.

Type of Highway Interstate Se condary Intersection

Sites (No. Lanes) C0MSAT(4) RT. 28(2) 355 & SHADY GR.(-)

195(8) GUDE Dr. (2) 355 & Q. 0. RD.(-)

B-W PKWY(4)

Traffic Parameter Min Mean Max Min Mean Max Min Mean Max

Average Traffic Speed, km/hr 85 90 93 61 67 71 - -

Total Traffic Volume, veh./hr 1990 3020 4330 660 1010 1430 2760 3680 5380

Percent Automobiles 77.4 91.9 98.6 84.2 90.8 94.6 88.3 93.2 98.1

Percent Medium Trucks 1.0 2.8 7.1 3.2 4.9 6.4 1.7 4.3 6.5

Percent Heavy Trucks 0.0 5.3 15.5 1.6 4.3 9.8 0.2 2.5 5.2

Table 5. Number of 1/3-octave band average sound pressure level

spectra used for the analyses in this report.

Type of

Highway Site Number of Spectra

7.5 m 15 m 30 m 60 m

Interstate COMSAT 3 3 3 3

1-95 4 4 4 4

B-W PKWY 5 5 5 —

Secondary RT. 28 4 4 4 - GUDE DR. 4 4 4 4

Intersection 355 &

SHADY GR.

4 4 4 4

355 & Q. 0. 4 4 4 4

ROAD
3. Noise Descriptors

Using the 1/3-octave band spectra of average sound levels for the 103

15-minute recordings of traffic noise, as described above, computations were made of the following noise ratings (the abbreviation given in parentheses

after each rating is used in this report)

:

Weighted Sound Levels

o A-weighted sound level, (A) [6]

o B-weighted sound level, (B) [6]

o C-weighted sound level, (C) [6]

o D-weighted sound level, (D) [7]

o E-weighted sound level, (E) [8,9]

Loudness Levels

o Stevens Mark VI loudness level, (MK6) [10, 11]

o Stevens Mark VII loudness level, (MK7) [8]

o Zwicker loudness level (free-field), (ZWK) [11]

Noisiness Levels

o Perceived Noise Level, (PNL) [12]

o Tone-Corrected Perceived Noise Level, (PNT) [13]

Speech Interference Level

o Preferred Speech Interference Level, (PSIL) [14]

These ratings (rounded to the nearest 0.1 dB) , along with data on highway

configurations, microphone locations, and traffic speed and mix, were used for

the analyses described below.
4. Analysis

The analyses given in the remainder of this report are linear regression

analyses in which a functional relationship between two variables (in this

case, two different noise ratings) is assumed and the regression coefficients

are obtained by least-square fitting. It is important to stress that the

problem that is being addressed in this report is quite different in one respect from regression problems such as are usually encountered in physics,

engineering, or experimental psychology. We are not dealing with random

variations in the variables of concern. We start with a precisely-defined

one-third octave band spectrum, say, Lj , L2 , L3, ... 124. We then utilize

precisely-defined algorithms to compute, for example, the A-weighted sound

level, A(L|, L2, L3, ... L24), and the D-weighted sound level, D(L]^, L2, L3

... L24). The quantities which are calculated are known exactly for the given

spectrum. The type of question that is asked in the present report can be

phrased as follows: If we throw away our knowledge of the individual

one-third octave band levels, how well can we predict, for example, the

D-weighted sound level from the A-weighted sound level, provided that we take

advantage of the fact that the shape of traffic noise spectra is reasonably

fixed and, in the analyses in Section 4.2, we use traffic parameters and

microphone locations as surrogate independent variables?

Let us first consider the very simple analysis in Section 4.1, where it

is assumed that one descriptor can be predicted from another simply by adding

a fixed constant to the known descriptor. In this extremely simple model, it

is assumed that we know nothing about traffic parameters or microphone

location. To gain insight into the functional form that is consistent with

this assumption, consider a single highway with one microphone at a fixed

location beside the highway. For a given traffic speed and mix (i.e.,

percentage of automobiles, medium trucks, and heavy trucks), the spectrum

shape will not change significantly as the total volume of traffic changes.

Rather, if the traffic volume increases by a factor N, each one-third octave

band level will increase by an additive increment, AL, that is proportional to

log N. The algorithms for computing the weighted levels and the Preferred

Speech Interference Levels are such that if the spectrum is shifted by a fixed

amount, say AL, with no relative changes in individual one-third octave band

levels, then each of these descriptors also will change by exactly AL. Thus,

for these descriptors, the difference between, say, the D-weighted level and

the A-weighted level is not changed by an increase in traffic flow.

The value of any given descriptor is generally controlled by the

one-third octave band levels covering a narrow range of frequencies. If

traffic mix, traffic speed, pavement surface, or microphone location is

changed, the first order effect will be a uniform shift in the important part

of the spectrum, leading to an equivalent uniform shift in each of the

weighted levels and in the Preferred Speech Interference Levels; there will be some changes in the spectrum shape but these will be second order effects.

Thus, for a given and fixed spectrum shape, any two weighted sound levels

(e.g., the A-weighted and D-weighted levels) will differ from one another by a constant that is independent of the magnitude of the sound pressure level. In

the range of sound levels of interest in the present study, the equal-loudness

(or equal-noisiness) contours used in a given rating scheme are very nearly

parallel with one another (e.g., the 1 sone contour and the 2 sone contour
differ only slightly in shape). If the contours were exactly parallel, the

additive constant relating one rating to another, for a fixed spectrum shape,

would be independent of the sound level. Since the loudness, or noisiness,

contours are not exactly parallel and since the traffic noise spectrum shapes

vary somewhat, the differences between any two ratings will vary around some average value. The analysis of Section 4.2 attempts to "correct" for slight

variations in spectrum shape by including terms involving distance from the

highway as well as traffic speed and mix.

It is also important to stress that the present analysis does not utilize

Inferential statistics to attempt to predict the confidence with which one could predict one noise rating from another for a spectrum selected at random.

Such a use of inferential statistics would be meaningful only if the entire

universe of traffic noise spectra of interest had been defined and if a random

sample from that universe were available for analysis. The sample population

used in the present report clearly was not drawn randomly from any such

well-defined universe.

The fitting, in a least-squares sense, of a regression curve is a well-defined operation that can be carried out for any arbitrary sample

population of data. However, if one is to attempt to draw the usual

statistical inferences about some universe from which the sample population

was taken, then a number of assumptions must be met. These include, in

addition to the requirements that the independent variables be known without

significant error and that the functional relationship, or model, chosen be

appropriate, the following:

a) the sample population must bear a known relation to the universe

about which it is desired to make inferences (preferably, the

sample population should have been randomly selected from the

target population),

b) the errors (observed minus predicted values) of the dependent

variable are random,

c) the errors are mutually independent,

d) the errors are homos cedastic (i.e., have a common variance),

e) the errors have zero mean, and

f) the errors are normally distributed

Briefly stated, none of these conditions is met. Probably the most serious

problem, which was mentioned before, is that the sample population was not

randomly selected from any well-defined target population. As a result of

this, it is believed that assumptions b), c), and d) also are violated.

Any two descriptors which are to be related were computed, using

different algorithms, from the same one-third octave band spectra; the nature

of the algorithms involved is such that assumptions e) and f) are probably

violated as well.

10
For these reasons, all statistical descriptors presented below should be

thought of as descriptive of the population of traffic noise spectra that was used in these analyses and should not be used to infer the uncertainty with

which one noise rating can be predicted from another for an arbitrary

spectrum. Nonetheless, in a qualitative sense it seems probable that the

functional relationships given below should yield good predictions for site

configurations and traffic conditions similar to those used in the present

analyses.

Consistent with the use of regression analyses based on functional models

that are in keeping with physical reality (see Refs. 15-16), the goodness of

fit between the data and a given model is described in terms of the standard

deviation of the residuals and not in terms of correlation coefficients (see

Refs. 15, 17-19).

4.1 Additive Constant

As pointed out above, if all traffic noise spectra had the same shape,

the difference between two weighted levels, say the A-weighted sound level and

the D-weighted sound level, would be a constant. Similarly, the difference

between the Preferred Speech Interference Level and a weighted level would be

constant for a fixed spectrum shape. For a family of spectra of slightly

differing shapes, the differences between two noise ratings would be expected

to vary somewhat around an average value. Thus the simplest regression

equation that is reasonable to try is

Y = X + M, (1)

where X is a known noise rating, M is an additive constant (for the given pair

of rating procedures), and Y is the noise rating to be predicted. Given a set

of N spectra, the additive constant is given by

N

M

N Z-^

(Y. - X.) = Y - X, (2)

i=l

where X^ and Yj|^ are the noise ratings corresponding to the i-th spectrum. A

measure of how well Eq. (1) can be used to predict Y from X is the standard

deviation of the (Yjl - X^^) values around their mean value:

N 1/2

s(X,Y) = ^E [Y. - X. - M]

1 1 (3)

i=l

Table 6 shows the values of M and s, computed from Eqs. (2) and (3) for

the 71 spectra corresponding to free-flowing traffic (the first five sites in

Tables 1-3), for all possible pairs of the noise descriptors listed in Sec. 3.

For each descriptor listed in the first column, the first row gives the values

of M and the second row gives the values of s. Thus, for the noise spectra

from free-flowing traffic, M » 6.1 dB is the additive constant relating the

D-weighted sound level to the A-weighted sound level and s = 1.2 dB is the

11
corresponding standard deviation, around their mean, of the differences

between the D-weighted and A-weighted levels. The diagonal of this matrix

contains only zeroes. Since the matrix of M values is antisymmetrical [i.e.,

M(X,Y) = -M(Y,X)] and the matrix of s values is symmetrical [s(X,Y) = s(Y,X)],

the lower half has been suppressed.

Table 6. Values of M, the average difference between a pair of ratings,

and s, the standard deviation of these differences around their

mean, for the outdoor LEQ free-flowing traffic noise spectra from

the present investigation.

B C D E MK6 MK7 ZWK PNL PNT PSIL { A M 4.6 7.9 6.1 4.1 12.9 5.4 18.9 12.7 13.2 -7.9

s 1.9 2.9 1.2 .9 1.4 1.4 1.3 .8 1.4 1.0 i

t

B M 3.3 1.5 -.5 8.4 .8 14.4 8.1 8.6

1 -12.5 ! s 1.1 1.4 1.1 1.0 1.1 1.2 1.9 1.7 2.6

C M — — -1.8 -3.8 5.0 -2.5 11.0 4.8 5.3 -15.8

s — — 2.1 2.0 1.7 1.8 1.9 2.7 2.3 3.5

D M — — — -2.0 6.9 -.7 12.9 6.6 7.2 -14.0

s — — — .6 .9 1.0 .7 .8 .6 1.5

E M — — — — 8.8 1.3 14.8 8.6 9.1 -12.0

s — — — — .8 .8 .7 .9 1,0 1.5

MK6 M — — — -7.6 6.0 -.2 .3 -20.8

s — — — .4 .4 1.3 1.4 2.0

MK7 M

s — — — —

13.6

.4

7.3

1.3

7.9

1.5

-13.3

1.9

ZWK M

s — — — — — —

-6.2

1.1

-5.7

1.2

-26.9

1.8

PNL M

s — — — — — —

— .5

1.0

-20.6

.9

PNT M

s — —

— — — — —

— -21.1 1 1.7

j

12
Table 7 shows the values of M and s for the 32 spectra of noise from

intersection traffic.

Inspection of the various values of s in Tables 6 and 7 shows that the

more-frequently-proposed descriptors, such as A-, D-, and E-weighted levels,

the three loudness levels, and the perceived noise level, are highly

interrelated and a simple additive constant can be used to predict one rating

from another with rather small uncertainty.

Table 7. Values of M, the average difference between a pair of ratings, and s, the standard deviation of these differences around their mean, for

the outdoor LEQ intersection-traffic noise spectra from the present

investigation.

B C D E MK6 MK7 ZWK PNL PNT PSIL

A M 7.6 12.5 7.7 5.7 15.1 7.1 20.5 14.3 14.4 -8.7

s 1.1 1.5 .7 .6 .9 .7 .7 .6 .7 .8

B M 4.9 .1 -2.0 7.4 -.5 12.9 6.7 6.8 -16.4

s .4 .4 .5 .5 .8 .7 .9 1.0 1.9

C M — — -4.8 -6.9 2.5 -5.4 8.0 1.8 1.9 -21.3

s — — .8 .9 .7 1.0 1.0 1.2 1.3 2.2

D M — — — -2.0 7.4 -.6 12.8 6.6 6.7 -16.4

s — — — .1 .4 .5 .5 .6 .7 1.5

E M — — — — 9.4 1.4 14.9 8.7 8.7 -14.4

s — — — — .5 .5 .5 .5 .6 1.4

MK6 M — — — — — -7.9 5.5 -.7 -.6 -23.8

s — — — — — .4 .4 .7 .8 1.6

MK7 M

s — — — — —

13.4

.1

7.2

.7

7.3

.8

-15.8

1.3

ZWK M

s — — —

—

— — -6.2

.7

-6.1

.8

-29.3

1.3

PNL M

s —

—

— — —

— — .1

.2

-23.1

1.2

PNT M

s —

— — —

— — — -23.1

1.2

13
There is considerable redundancy in the additive constants listed in

Tables 6 and 7 in that the additive constants relating all other frequency

ratings to any given single frequency rating contain all the information

necessary to relate other pairs of descriptors. Thus, from Table 6, it is seen that E = A + 4.1 and PNL = A + 12.7; therefore PNL = E + (12.7 - 4.1) =

E + 8.6, as indicated further on in the table.

On the other hand, one cannot, in any simple way, combine standard

deviations to predict the standard deviation of a particular pair of frequency

ratings.

4.2 Functional Relationship Between Noise Ratings

We will now examine the feasibility of using other data, such as distance

from the highway and traffic conditions, to reduce the uncertainty with which

one noise rating can be predicted from another. Rather than empirically

examine the residuals, ^± ~ ^i ~ M> we will develop a functional relationship,

between two noise ratings, that explicitly includes consideration of the

influence of distance, traffic speed, and traffic mix.

The mean-square sound pressure due to an omni-directional, incoherent

point source moving in a straight line, at uniform speed, past an observation

point is

p2 = P

2

—^ (*)

where

D is the perpendicular distance from the

observation point to the line of travel,

S is the speed at which the source is

moving,

Pq'^ is the mean-square pressure at a reference distance, Dq,

and t=0 at the time when the source is closest to the observation point. It

is assumed that there is spherical spreading in a non-absorptive medium and

that the speed of travel is much less that the speed of sound in the medium.

The sound exposure due to the passage of this source is

/

2 2 p2dt=-^ ^- (^)

^ SD

14
The mean-square sound pressure due to a succession of such sources, moving

past the observation point at arbitrary spacing, is

2 2 -- ttD VP

2 o o P =

~ SD

(6)

where V is the average flow rate (vehicles per unit time). The moving stream

of traffic can be represented by an equivalent line source, the mean-square

sound pressure falling off inversely with distance (i.e., the sound pressure

level decreases 3 dB per doubling of distance away from the source).

In practice, the sound level from a real highway falls off more rapidly

with distance that is indicated by Eq. (6). This occurs because of excess attenuation due to absorption by air, vegetation, etc. Equation (6) can be

generalized to the form

ttD VP

o o f(D^/D) (7)

where fiD^/T)) represents not only the cylindrical spreading exhibited by

Eq. (6) but also the effects of excess attenuation. Empirically, it has been

observed that this function can be represented as a simple exponential

function of the argument so that

(8)

where v=l corresponds to 3 dB per doubling of distance, v=1.5 to 4.5 dB per

doubling of distance, etc.

A real highway may consist of a number of lanes carrying several classes

of vehicles each traveling at various speeds. The average sound level at an observation point to the side of an infinitely-long, straight highway may be

estimated by summing over a number of terms of the form of Eq. (8), yielding

the following expression for the average sound level:

LEQ = 10 log

L i-l j=l

^i

TTD V. . Q. . /d \

o 1-1 iJ _o

1 ij \ 3 /

(9)

15
where

is the average volume flow rate (vehicles per unit time)

of the i-th class of vehicles in the j-th lane,

S^-; is the average speed of the i-th class of vehicles in the j-th lane,

D -! is the perpendicular distance from the observation point to the

acoustic center of the j-th lane (hereafter, and in all calculations

in this report, the acoustic center of each lane is taken as the

physical center of that lane)

,

Dq is a reference distance at which the source strength is defined,

Qi 1 ~ Qi^^ii^ ^^ ^^^ average source strength of the i-th class of

vehicles and is a function of the speed, S^a, at which this class of

vehicles is traveling in the j-th lane,

v-j^ is the exponent which describes the rate of attenuation with distance

for the i-th class of vehicles (it is a function of the class of

vehicle because the characteristic sound spectra may be different for

different classes of vehicles and because the differences in

effective source heights may lead to different attenuations by ground

cover)

,

I is the number of vehicle classes, and

J is the number of lanes.

Equation (9), for an infinitely-long straight highway, is consistent, for

example, with the FHWA Highway Traffic Noise Prediction Model [20-23], that is

used to predict the A-weighted average sound level along a highway.

Let us assume that Eq. (9) is valid for a particular frequency weighting

and that measured values of LEQ and the relevant traffic parameters are available. Let us further assume that a similar equation can be written

corresponding to some other frequency-weighted average sound level but with

LEQ, Qf-j, and v^^ replaced by primed quantities, the primes indicating that the

level, source strengths, and rates of attenuation are those corresponding to

the other frequency weighting.

It is desired to predict LEQ from measured values of LEQ and traffic

parameters. In principle, this could be done by combining two equations of

the form of Eq. (9) and then attempting to find values of Qj^'(S-^j), Qi(Sj^j),

V-j^, and v^ that would make this expression fit the empirically-observed values

of A = LEQ - LEQ. While this could be done, using a non-linear fitting

routine, the resulting expression would be complicated and difficult to use. We therefore seek means to develop a simpler empirical equation for predicting

LEQ*.

16
The difference between the two ratings is obtained by combining two

equations of the form of Eq. (9) to obtain

^ I

A = LEQ' - LEQ = 10 log

i=l -1

J:!

S

.q!. /D \^i.

I J^ f. .Q. . /D \^± (10)

where fj^-; is the fraction of the vehicles in the j-th lane that are of the

i-th class. We will now make a number of assumptions to bring Eq. (10) into a more tractable form:

o the equivalent lane concept of Appendix B is used to enable

replacement of -j(DQ/D-j)^i by (DQ/D)^i, where D is the effective

distance from a microphone location to the center of the fictitious

equivalent highway and v^ is an exponent that defines the rate of

change of sound intensity with distance; similarly ^(Dq/DO^I goes

to (DQ/Dj)^i, where the prime indicates that v^^ may differ from v^,

o all vehicles are assumed to be traveling at the same speed, S,

regardless of lane or vehicle class,

o all lanes are assumed to carry the same mix of traffic,

o the source strengths for the i-th class of vehicles vary with speed in

accordance with Q^ = <X|^(S/Sq) ^i, where Sq is a reference speed and a^

and 3i are constants.

With these assumptions, Eq. (10) reduces to

,- I

A = 10 loi

•- i=l

» _ Z f.a' (S/S ) ^ (D /D)

"- TT IX o o x=l

2- f.a. (S/S ) " (D /D) IX o

(11)

17
We now make the assumption that there are only two classes of vehicles:

automobiles (i = 1) and trucks (i = 2). Since fi + f2 = 1> we take

f2 = f and f]^ = 1 - f and write

A = 10 loj

a,'(S/S )

-" (D /D) -" (1 - f) + f(a:/a:)(S/S ) ""(D /D) ^\

1 o o L 2 1 o o J ^1 "^1 r ^2"^i '^2"'^r

a^(S/S )

-" (D /D) ^ (1 - f) + f(a„/aJ(S/S ) ^

-"(D /D) looL zio ^ ^ o

(12)

where the term for automobiles has been factored out in both the numerator

and the denominator. This expression can be expanded to yield

A = M + M, log (S/S ) + M_ log (D/D ) o i o z o

+ 10 los

(1 - f) + f(a:/a:)(S/S ) ^ ^(D /D)

2 1 o o

B -6 V -V

(1 - f) + f(a„/a^)(S/S ) -"(D /D)

z 1 o o

(13)

where

and

Mq = 10 log (ai/ai).

Ml = 10 (3i-Bl),

M2 = -10 (vi - vi).

(14)

(15)

(16)

Inspection of the last term in Eq. (13) reveals that both the numerator

and the denominator should be fairly close to unity since a2/cti " 1, 32 " 3l,

V2 " vj, and similar approximations hold for the primed quantities.

Accordingly, little error is introduced by the approximation 10 log x = 4.34 £n X « 4.34(x-l). With this substitution, Eq . (13) becomes

A = M^ + M^ log (S/S ) + M log (D/D )

+ 4.34f(a:/a')(S/S ) ^

"^(D /D) ^ ^

Z 1 o o (17)

- 4.34f(a„/a,)(S/S ) ^ "^ (D /D) "^ Zio ^ o

18
Using the approximation, a^ « 1 + xJlna, which is valid for small x,

Eq. (17) can be further reduced to

A = M + M log (S/S ) + M log (D/D ) o 1

^ ' ' o' 2 o

where

+ M f + M f log (S/S ) + M f log (D/D ), (18)

M„ = 4.34(a2/a| - a^/a^) , ,^^.

M^ = 10[(a^/a|)(B^ - B|) - {oi^/a^)(B^ - g^)] (20)

M3 = -10[(a^/a|)(v^ - v|) - (oi^/a^)(iv^ - v^) ] , (21)

and a higher order term in f log (S/Sq) log (D/Dq) has been dropped.

Equation (18) was used for regression analysis of the differences in

noise ratings as functions of traffic speed, effective distance from the

highway, and equivalent fraction of heavy trucks. The effective distances

from the highway were computed as indicated in Appendix B. The equivalent

fraction of heavy trucks was computed from f = f3 + (Q2/Q3)f2» where t^ ^^^ ^3 are the observed fractions of medium and heavy trucks, respectively, and Q2

and Q3 are the corresponding source strengths as computed from the equations

defining the FHWA Reference Noise Emission Levels [16-19]. The reference

distance used was D^ = 15 m, while the reference speed was Sq = 88.5 km/hr

(55 mph) . For the remainder of the analyses in this report, the A-weighted

sound level was taken as the known noise rating and regression equations, of

the form of Eq. (18), were derived to predict other ratings from the

A-weighted level. The equations given could be combined to yield any rating

in terms of any other rating (e.g., predict Mark VI Loudness Level from the

D-weighted sound level) although the reliability of the prediction would not

be well known.

In order to give the reader a better feel for the efficacy of regression

equations of the form of Eq. (18), a series of figures have been included for

the case of predicting the Mark VI Loudness Level [6,7] from the A-weighted

level [2], based upon the data from the 71 recordings of noise from

free-flowing traffic. Figures 1-5 show the difference A = MK6 - A, plotted

versus log (D/Dq) for the COMSAT, 1-95, B-W PKWY, GUDE DR., and RT. 28 sites,

respectively. In any one of these plots, the points represented by a given

symbol correspond to the (three or four) recordings made at a given session.

The straight lines are regression lines, of the form A = Mq + M2 log (D/Dq),

fitted to the data from a given recording session. It is seen that the lines on any given figure group rather closely. In Figure 6 a mean line is plotted

for each of the five sites; these lines clearly illustrate marked differences

among the five sets of data. As will become apparent below, these differences

are mainly due to differences in traffic speed and mix, rather than to any

particular site characteristics.

19
17.00

16.50

16.00

15.50

15.00

I 14.50 h <

^ u.oo

^13.50

lij S 13.00 -

12.50 h £12.00 m

° 11.50 -

11.00 -

10.50

10.00-.300

MK6 LOUDNESS LEVEL MINUS REFERENCE A-WEIGHTED LEVEL

~i I

1 1 1 T I I 1 r

1 1 1 1 r

COMSAT

-.200 -.100 .100 .200 .300 LOG(DE/D0)

.400 .500 .600 .700 .800

Figure 1. Difference between the Mark VI Loudness Level and the A-weighted

sound level, as a function of effective distance, for the three

recording times at the COMSAT site.

17.00

16.50

16.00 -

15.50 - ^ 15.00 i 14.50 <

^ 14.00 ^ 13.50

_]^13.00

g 12.50 ^ 12.00 m

m

o 11.50

11.00

10.50

10.00

MK6 LOUDNESS LEVEL MINUS REFERENCE A-WEIGHTED LEVEL

i 1 1 1 rI L

-.300 -.200 -.100

~i r

195

100 .200 .300 LOG(DE/D0).400 .500 .600 .700 .800

Figure 2. Difference between the Mark VI Loudness Level and the A-weighted

sound level, as a function of effective distance, for the four

recording times at the 195 site.

20
11.00

10.50

10.00

17.00

16.50

16.00 h

15.50 d 15.00

Yl4-.50 ^U.OO ^ 13.50

_ikJg 13.00 R 12.50

uj 12.00

to

COo 11.50

MK6 LOUDNESS LEVEL MINUS REFERENCE A-WEIGHTED LEVEL

~i I 1

I

1

I I I I I \

1 1 1 1 1 1 1 1 1 r

B-W PKWY

J 1 1 1 II I I -I 1 1 1 1 1 I I I I I I -.300 -.200 -.100 .100 .200 ,300 .400 .500 .600 700 800

LOG(DE/D0)

Figure 3. Difference between the Mark VI Loudness Level and the A-weighted

sound level, as a function of effective distance, for the five

recording times at the B-W PKWY site.

17.00

16.50

16.00

15.50 - y 15.00 - 1 14.50 ^U.OO

^13.50

_i^13.00 h R 12.50

uj 12.00 m

CDO 1 1 .50 -

11.00 -

10,50 -

10.00-.300

MK6 LOUDNESS LEVEL MINUS REFERENCE A-WEIGHTED LEVEL

"I I 1 I 1 1 1 1 1

_L. _L J_ -.200 -.100 .100 .200 .300 L0G(DE/D0)

.400 .500 .600 .700 .800

Figure 4 . Difference between the Mark VI Loudness Level and the A-weighted

sound level, as a function of effective distance, for the four

recording times at the GUDE DR. site.

21
MK5 LOUDNESS LEVEL MINUS REFERENCE A-WEIGHTED LEVEL

17.00

16.50

16.00

15.50 ^ 15.00 i 14.50 ^ 14.00 ^ 13.50

_jUJ^13.00

R 12.50

UJ 12.00

00m

o 11.50

11.00

10.50

10,00

"T ~r

RT. 28

1 I I I 1 I I 1 1 i I I I I I I I 1 I

I I 300 -.200 -.100 .100 .200 .300 .400 .500 .600 .700 .800 LOG(DE/D0)

Figure 5. Difference between the Mark VI Loudness Level and the A-weighted

sound level, as a function of effective distance, for the four

recording times at the RT. 28 site.

10.00-.300

Figure 6.

MK5 LOUDNESS LEVEL MINUS REFERENCE A-WEIGHTED LEVEL

-.200 .100 .100 .200 .300 LOG(DE/D0).400 .500 .600 .700 .800

Average trends of the difference between the Mark VI Loudness

Level and the A-weighted sound level, as functions of effective

distance, for the five sites with free-flowing traffic.

22
Using the 20 lines from Fig. 1-5, the value of A was computed for D = Dq

for each recording session. These interpolated values are plotted in Fig. 7 versus the equivalent fraction of heavy trucks. It is seen that the twelve

values corresponding to interstate traffic (S « 88.5 km/hr) lie rather closely

along the regression line shown. The eight values corresponding to the slower

traffic on secondary roads lie rather close to a different line, having a larger ordinate intercept and a slightly more negative slope.

In Fig. 8 the slopes of the 20 lines from Figs. 1-5 are plotted versus

the equivalent fraction of heavy trucks. These slopes decrease with

increasing truck traffic but show little evidence of dependence on traffic

speed.

Equations of the form of Eq. (18) were fitted to all of the other noise

ratings as functions of the A-weighted sound level. The resulting regression

coefficients are shown in Table 8, along with the standard deviation of the

residuals about the fitted curve. It is seen that the regression equations

enable prediction of the noise ratings that would be likely to be used for

traffic noise with standard deviations of 0.6 -0.8 dB, except for B-weighted

and C-weighted noise levels, which are rarely used for predicting human

response, and the Tone-Corrected Perceived Noise Level, PNT, which treats

"spikes" in spectra in a very different way from that used by any of the other

noise ratings. With the exception of these three ratings (B, C, PNT), the

observed values for all 71 free-flowing traffic noise spectra were within

i 2.1 dB of the values computed using Eq, (18) and the regression coefficients

shown in Table 8. (It should be noted that some of the regression

coefficients obtained, especially for the cross-product terms (i.e., M4 and

M5) do not lend themselves to ready interpretation from a physical point of

view; these may be particularly influenced by the specific set of spectra used

in this study.)

Figures 9 and 10 show the differences between the Mark VI Loudness Level

and the A-weighted sound level plotted versus effective distance for the four

recording sessions at the 355 & S. G. and the 355 & Q. 0. sites, respectively.

The mean curves for the two sites are compared in Fig. 11. The interpolated

values at 15 m and the slopes of the regression lines show little, if any,

systematic dependence upon traffic mix and hence plots corresponding to Figs. 7 and 8 are not included for intersection traffic. Since there is no relatively

constant speed associated with stop-and-go intersection traffic, no terms

involving speed were included in the regression analysis.

Table 9 gives the regression coefficients, and the associated standard

deviation of the residuals, for the simple regression equation

A = Mq + M2 log (D/Dq). The standard deviations of the residuals are in the

range of 0.3 -0.6 dB for the noise ratings of most likely applicability to

prediction of human response to traffic noise. For the D-weighted and

E-weighted sound levels, the three loudness levels, and the two noisiness of

levels, the observed values for all 32 spectra were within better than - 2 dB

the values computed using the Mq and M2 coefficients. Inclusion of terms (M3

and M5) involving traffic mix did not significantly improve prediction

accuracies.

23
17.00

16.50

16.00

15,50

15.00

MK6 LOUDNESS LEVEL MINUS REFERENCE A-WEIGHTED LEVEL

"T T" T" "T I 1 1

SECONDARIES

+

INTERS TATE

S

.0200 .0400 .0600 .0800 .1000 .1200 .1400 EQUIVALENT FRACTION OF HEAVY TRUCKS

.1600 .1800 .2000

Figure 7. The difference between the Mark VI Loudness Level and the

A-weighted sound level, each interpolated to an effective

distance of 50 m, as a function of the equivalent fraction

of heavy trucks. The lower line corresponds to the twelve

recording times for interstate highways. The upper line

corresponds to the eight recording sessions along the side

of secondary roads.

24
MK6 LOUDNESS LEVEL MINUS REFERENCE A-WEIGHTED LEVEL T

.0200 .0400 .0600 .0800 .1000 .1200 .1400 EQUIVALENT FRACTION OF HEAVY TRUCKS.1600 .1800 .2000

Figure 8. The slopes of the curves in Figs. 1-5 (i.e., the slopes of

plots of the difference between the Mark VI Loudness Level

and the A-weighted sound level as functions of the logarithm,

to the base ten, of the ratio of the effective distance) as a function of the equivalent fraction of heavy trucks.

25
Table 8. Regression coefficients (for Eq. (18)), and associated standard

deviations of the residuals, for predicting different noise ratings

from the A-weighted sound level for free-flowing traffic.

Coefficient

Rat ing

B C D E MK6 MK7 ZWK PNL PNT PSIL

Mo 3.2 5.3 4.5 3.2 11.9 4.4 17.6 11.6 11.3 -8.6

Ml -19.6 -33.5 -15.0 -10.7 -16.0 -16.8 -18.2 -10.6 -16.1 -5.2

M2 4.71 8.27 4.26 2.99 4.65 4.53 4.67 2.10 4.12 0.03

M3 -8.34 -7.76 10.0 2.28 -8.97 -6.35 -0.83 15.1 16.9 20.6

M4 -26.8 27.9 115. 32.1 64.8 83.4 121. 153. 148. 189.

M5 -11.9 -18.5 -22.8 -16.1 -10.3 -16.8 -15.9 -20.6 -22.9 -20.5

s 1.1 1.4 0.7 0.6 0.7 0.8 0.7 0.6 1.0 0.6

In conclusion, the results presented above indicate that, in the case of

both free-flowing and stop-and-go highway traffic noise, one frequency rating

can be predicted from another rating, using simple regression equations, with

standard deviations on the order of 1 to 2 dB. In addition, if the regression

equations incorporate terms for traffic speed, traffic mix, and distance from the

highway, these standard deviations may be reduced to 0.5 to 1 dB, depending on the

particular frequency rating of interest. These uncertainties are less than those

typically associated with noise measurements. Thus, for traffic noise the choice

of which frequency weighting is utilized does not appear to be critical since

dose-response relationships based upon a particular frequency weighting can easily

be translated into equivalent dose-response relationships incorporating another

frequency weighting without any significant loss of uncertainty.

26
19.00

18.50

18.00

17.50 ^ 17.00 i 16.50 <

^ 16.00 2

^ 15.50

_)S 15.00

P, 1 4.50 >w 14.00

in

DO

C> 13.50

13.00

12.50

1 1 1 1 1 r MK6 LOUDNESS LEVEL MINUS REFERENCE A-WEIGHTED LEVEL

1 1 1 1 1 1 1

\ I I 1 I r 355 & S.G.

12.00-.300 -.200 .100 .100 .200 .300 LOG(DE/DO)

.400 ,500 .600 .700 .800

Figure 9. Difference between the Mark VI Loudness Level and the A-weighted

sound level, as a function of effective distance, for the four

recording times at the 355 & S.G. intersection.

MK6 LOUDNESS LEVEL MINUS REFERENCE A-WEIGHTED LEVEL

19.00

18.50

18.00

17.50 y 17.00 - Y 16.50 <

^16.00 - z

^ 15.50

_iS 15.00

£14.50

Si 4.00 h m

o 13.50 -

13.00 -

12.50

12.00-.300

_L

355 & Q.O.

-.200 -.100 .100 .200 .300 LOG(DE/DO)

,400 .500 ,600 ,700 .800

Figure 10. Difference between the Mark VI Loudness Level and the A-weighted

sound level, as a function of effective distance, for the four

recording times at the 355 & Q.O. intersection.

27
17.00

16.50

16.00

15.50

^15.00

Y 14.50 <

^14.00 ^ 13.50 f

2i 13.00

_lR 12.50

Lu 12.00

tom

o 11.50

11.00

10.50

10.00

MK6 LOUDNESS LEVEL MINUS REFERENCE A-WEIGH"^ED LEVEL

n 1 1 1 1

i ~i 1 1 i I I I

1 1 1 1 : r

J I

I I J I I I

I I J iJ I I

I I -.300 -.200 -.100 .100 .200 ,300 .400 .500 .600 .700 .800 LOG(DE/D0)

Figure 11. Average trends of the difference between the Mark VI Loudness

Level and the A-weighted sound level, as functions of effective

distance, for the two intersections.

Table 9. Regression coefficients (for first and third terms of Eq. (18)), and associated

standard deviations of the residuals, for predicting different noise ratings from

the A-weighted sound level for intersection traffic.

Rating

Coefficient

B C D E MK6 MK7 zuT<: PNL PNT PSIL

"o 6.2 10.6 6.8 5.0 13.7 6.1 19.5 14.2 14.3 -7.8

M2 4.2 6.0 2.7 2.2 4.0 2.9 3.1 0.5 0.4 -2.6

s 0.7 0.9 0.5 0.4 0.5 0.3 0.3 0.6 0.7 0.6

28
5. References

1. Flynn, D.R. , Voorhees, C.R., and Yaniv, S.L., Highway Noise Criteria

Study: Traffic Noise Data Base, NBS Technical Note 1113-1, (National

Bureau of Standards, Washington, D.C., 1980).

2. Donovan, P.R., Flynn, D.R., and Yaniv, S.L., Highway Noise Criteria Study:

Outdoor/Indoor Noise Isolation, NBS Technical Note 1113-2 (National Bureau

of Standards, Washington, D.C., 1980).

3. Yaniv, S.L., Bauer, J.W., Flynn, D.R. , and Danner, W.F., Effects of

Time-Varying Noise on Annoyance: A Review, NBSIR 81-2377 (National Bureau

of Standards, Washington, D.C., 1981).

4. Yaniv, S.L., Danner, W.F., and Bauer, J.W., Measurement and Prediction of

Annoyance Caused by Time-Varying Highway Noise, Journal of the Acoustical

Society of America 7_2, 200-207 (1982).

5. Yaniv, S.L., and Flynn, D.R., Highway Noise Criteria Study: Technical

Summary, NBSIR 82-2610 (National Bureau of Standards, Washington, D.C, . 1982).

6. American National Standard Specification for Sound Level Meters, ANSI

SI. 4-1971 (American National Standards Institute, New York, 1971).

7. Standard on Frequency Weighting for Measurement of Aircraft Noise

(D-weighting) , lEC Publication 537 (International Electrotechnical

Commission, Geneva, Switzerland, 1976).

8. Stevens, S.S., Perceived level of noise by Mark VII and decibels (E) , J. Acoust. Soc. Am. _51_, 575-601 (1972).

9. Draft American National Standard E-Weighting Network for Noise

Measurements, Draft ANSI SI. 27 (Acoustical Society of America, New York,

1978).

10. American National Standard Procedure for the Computation of Loudness of

Noise, ANSI S3. 4-1980 (American National Standards Institute, New York,

1980).

11. Acoustics — Method for Calculating Loudness Level, ISO 532-1975

(International Organization for Standardization, Geneva, Switzerland,

1975).

12. Procedure for Describing Aircraft Noise Around an Airport, ISO R507-1970

(International Organization for Standardization, Geneva, Switzerland,

1970).

13. Federal Aviation Administration Noise Standards, Title 14, Code of Federal

Regulations, Chapter I, Part 36, 34 FR 1864, November 18, 1969, as amended

at 38 FR 29574, October 26, 1973.

14. American National Standard for Rating Noise with Respect to Speech

Interference, S3. 14-1977 (Acoustical Society of America, New York,

1977).

29
15. Natrella, M.G., Experimental Statistics, NBS Handbook 91 (National Bureau

of Standards, Washington, D.C., 1963) Chapts. 5 and 6.

16. Johnson, N.L., and Leone, F.C. , Statistics and Experimental Design, in

Engineering and the Physical Sciences (John Wiley and Sons, New York,

1964) p. 378.

17. Mandel, J., The Statistical Analysis of Experimental Data (Interscience,

New York, 1964) p. 58.

18. Warren, W.G., Correlation or regression: bias or precision, Appl.

Statistics ^, 148-164 (1971).

19. Seber, G.A.F., Applied Regression Analysis (John Wiley and Sons, New

York, 1977) p. 107.

20. Reagan, J. A., The FHWA highway traffic noise prediction model - manual

method, in Proceedings of NOISE-CON 77, NASA Langley Research Center,

Hampton, Virginia (Noise Control Foundation, Poughkeepsie, New York,

1977). pp. 181-192.

21. Hand-Held Calculator Listings for the FHWA Highway Traffic Noise

Prediction Model, FHWA Technical Advisory T5040.5 (Federal Highway

Administration, Washington, D.C., September 5, 1978).

22. Reagan, J. A., Determination of Reference Energy Mean Emission Levels,

Report No. FHWA-OEP/HEV-78-1 (Federal Highway Administration, Washington,

D.C., July 1978).

23. Kessler, F.M. , and Alexander, M. , Sound Procedures for Measuring Highway

Noise: Final Report, W. Bowlby (ed.), Report No. FHWA-DP-45-1R (Federal

Highway Administration, Washington, D.C., August 1981).

30
Appendix A. Literature on Traffic Noise Spectra

This appendix contains a brief comparison of selected traffic noise

spectra from the literature with some of the traffic noise spectra obtained in

conjunction with the present investigation.

A.l Literature Data

Many spectra have been reported for motor vehicle noise throughout the

literature. For the purposes of this report, attention was given only to

those studies which provide traffic noise spectra, rather than spectra for

discrete, individual vehicle passbys. Included are typical traffic noise

spectra corresponding to a variety of traffic conditions, including low- and

high-speed cruise, stop-and-go traffic, arterial streets, freeways, passenger

cars only, and mixed truck, bus, and automobile traffic. The traffic noise

data reported in the literature were obtained from measurements performed at

various distances from the roadways, ranging from 4 m to 30 m, although most

reported spectra are for measurements performed at either 7.5, 15, or 30 m. Several references present spectra corresponding to Ljq, the level exceeded 10

percent of the time; however, in most studies "average" spectra are reported

where the averaging is of maximum levels, or some sort of average over time.

In several of the reported studies, "average" is left undefined. With one exception, all the reported spectra are octave band levels.

The traffic noise spectra found in the literature are presented

graphically in Figs. Al through A4. The spectra have been grouped into Ljq

spectra (Fig. A2) and average spectra at distances of 7.5, 15, and 30 m

(Figs. Al, A3, and A4, respectively). The literature reference from which

each of these spectra was taken is indicated in the figure; a summary of the

conditions under which each spectrum was measured is provided in Table Al . It

will be noted that in some of the studies substantial variations in the level

of the spectra occur. These differences in sound pressure level are due to

the differences in vehicle speed, traffic volume, traffic mix, and averaging

technique used in the various studies.

In order to allow easier comparison of the shapes of the individual

spectra found in the literature, the reported band levels were normalized

relative to the A-weighted sound level corresponding to each spectrum. This was done by computing the A-weighted level for each spectrum and then

subtracting the A-weighted level from the level in each octave band. These

normalized spectra are presented in Figs. A5 through A8. Comparison of these

figures indicates that the shapes of individual spectra are generally similar

regardless of the details of the traffic situation or measurement method.

However, a comparison of individual spectra suggests that there will be source spectrum shape differences due to vehicle speed and traffic mix. The number

of samples included in various studies is too small and in many cases the

detail provided by individual references is not sufficient to allow for any

general conclusions to be made.

The envelope defining the normalized spectra of Figs. A5 through A8 is

presented in Fig. A9. Due to the emphasis placed on the middle frequencies by

the normalization relative to A-weighted levels, the envelope of Fig. A9

displays its greatest range in the lowest and the highest octave bands. In

31
OQ

CO

UJ

CO

OQ

CJ

100

90

80

70

60

50

40

30

1 r

• SPECTRUM #1 [A1]

SPECTRUM #2 [A2]

J L

I \ L 1

31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY |Hz

•Figure Al . Published "average" traffic noise spectra at 7.5 m.

32
OQ

CO

CO

CO

100

90

80

70

60

50

40

30

1 r

A SPECTRUM #3 [A3] • SPECTRUM #4[A4]

SPECTRUM #5[A5]

J L J L 31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY (Hzj

Figure A2. Published LlO traffic noise spectra at 4 to 25 m.

33
00

CO

LU

cc

Q.

a

CO

a

100

90

80

70

60

50

40

30

A SPECTRUM #6 [A6]

D SPECTRUM #7[A6]

O SPECTRUM #8 [A6] • SPECTRUM #9 [A6]

SPECTRUM #10 [A7]

1 1 1

31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY (Hzj

Figure A3. Published "average" traffic noise spectra at 15 m.

34
CQ

CO

00

LU

a.

CO

flO

100

90 —

80 —

70 —

60 —

50

40 —

30

1 1 1 1 1 1

1

1

1

O SPECTRUM #11 [AS — A SPECTRUM #12[A9]

D SPECTRUM #13[A10]

- SPECTRUM #14 [Ali; _ • SPECTRUM #15 [All]

1 1 1 i 1 1 1 1 31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY |Hz|

Figure A4 . Published "average" traffic noise spectra at 30 m.

35
0)

CJa

OJ • M o

0) 3

M-lCU (^

CO

J-i 4JO

<Um

0)

CO •Hodo

•Hm

cd

4-)

X)

(U

,i:2 CO •HiH

,£3P^

0)

4-> o

enCo

•H

+J •HX)

fiooH

4-1 co

CO^ OJ T3 ex

CO >. O H

Pi

•H 0) ---

0) CO ^

CO H

CD >^ U CO c e 5 —^

to

O T3 S U >-i cC --^

Cfl M-i

O

•H Oi o

•H

CU>

1-1o

en e

S-i • U O O IS

(Uex

CO

.—1 CNJ

m <r in o ^ VD ^ r^ oo ON o rH rH

<d < <C < <: < << < < < <; < < <

rH

<;

CO X)c

CO t—r

-o

S-I c CO CO CO CO >^ OJ CO TJ T3 -o xi

-H ^ rH CO CO CO CO <; < CO 4-1 W) C C C C -< < < < < < en ai +J (U c CO CO CO CO en en en en en en

tD & 1—1 3 w CLJ

O CJ CJ P ;3 :=> :3 :3 t3

rH

>. >^ >. CO

CO CO CO •H

a ^ S ^ Sh

CO X. Q) QJ QJ < <:

5-1 <; < -< <: <: <c < &0

•H

QJ

S-I <i QJ

5-1

4-)

5-4

a :2 P 2 s 2: 13 ^ "Z s 3: ^ z Ph <

TO

Q)

OJ ro CO a <r O r^ rH iH \o

<j- in cn < < \D CXD ON rH < rH < <; r-- in \C o

"""^-^ ""*-* 1 1 1 1'***>. 1 ""'-^ "-^

C3^ 1 iH o Z •z CO o

00 s o00 S IS CO

?-. >.

rH rH

>% ?»^ CO >^ ?^ >. >. >. 4-1 4-1 tH rH dJ rH rH r-l rH a rH CO CO c C CO C G c C •H C O O o o :3 O o o o 14H MH CO o S 2:

CO CO CO CO CO CO CO ^ CO CO CO

i-l !-i r. CJ S-I S-J S-I S-I S-I CJ S-I 5-1

u

CO CO CO •H CO CO CO CO

H

D CO CO CO o o M-l 4-1 CJ o CJ o

>^

S-I H o CJ CJ

uM 3 CO < u S-I S-I S-1 > < S-Iu 5-4 0) 0) !-i U QJ QJ QJ QJ CO Xl QJ QJ QJ M WD H H z M bO to M QJ C z bO bO bO C C C c c C K CO C C C

0) »• Xl 0) QJ QJ QJ ^—^ QJ QJ QJ CO CO CO 0) CO CO CO CO CO CO CO CD

CO CO S-I

X CO CO CO CO <! S-I CO CO CO

CO CO CO •H CO CO CO CO CO CO CO CO

PLI p-i o S Ph CL, Pm (X, iz: CJ Ph Ph PL,

u-1 in in CNl • • 1 O in in in in CN o o O

O

r^ r-^ <r 1-1 •<r rH rH rH rH rH

m C^ ro ro

n

^-N CO

•H

CO

^-^ __^ Id 'O x)

1) CO CO CO QJ QJ •H (U o E •H •H

<+-! 4-1 4-1 3 l+H UH

•H •H •r-i 6 •H •H O CO CO •H tJ a

CJ

X QJ 0) Ci LO 00 CO QJbOC

CO 'A

p. CIi to S CO CO C (U 01 o d d < 13 00 bO rH rH rH rH rH <: <4-l <: :=) :=) *-v^ ^w^ CO CO ^J QJ 0) QJ QJ o ^-^ ^—-' Z S-I S-I > > > > S IS

<y Q) QJ QJ QJ QJ QJ QJ

M-lo

QJ 0) bO > > kJ J hJ hJ OJ} bO bO

CO < < CO CO CO

S-i 1 1 c C c C !-i QJbO

CO

S-I

i-l ^-1 0) o O CO CO CO CO QJ QJ QJ > rH rH 0) QJ QJ QJ > > >

<; i-J hJ s :s la ;si < < <

QJ ^

rH C>4 ro <]- in vX> r^ 00 CJN

O rH

r-l CNrH

m

rH rH

m

rH

36
20

10 —

—

10 —

Kl

20 —

-30

1

'

1 1 1 1

^^""^^

>^=-A

—

~ SPECTRUM #1 [Al]

>\ii - A SPECTRUM #2[A2] \b

1 1 1 1 I 1 1

1 31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY (Hz]

Figure A5 . Normalized "average" traffic noise spectra at 7.5 m.

37
20

M 10 —

ISI

—

10 —

•20 —

•30

1 1 1 1 1 1 1 1

D SPECTRUM #3 [A3]

^

V

-

- O SPECTRUM #4[A4] \

s^ A SPECTRUM #5 [A5] \ _

i 1 1 1

i 1 1 1 31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY |Hz)

Figure A6. Normalized LIO traffic noise spectra at 4 to 25 m.

38
20

CO 10

CO

o

M

10

20

30

T

1 r

O SPECTRUM #6 [A6]

A SPECTRUM #7 [A6]

D SPECTRUM #8[A6]

V SPECTRUM #9 [A6]

A SPECTRUM #10 [A7]

J \ I L

31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY |Hz)

Figure A7. Normalized "average" traffic noise spectra at 15 m.

39
20

SS 10

fiQ

-10

ISI

-20

30

I I [

O SPECTRUM #11 [A8]

A SPECTRUM #12 [A9]

D SPECTRUM #13 [A10]

V SPECTRUM #14 [All]

SPECTRUM #15 [All]

T r

31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY (Hz)

Figure A8 . Normalized "average" traffic noise spectra at 30 m.

40
20

10 —

—

GO

CS

ISI

10 —

20 —

30

1 1 1 1 1 1 1 1

•OT

- "7\

—

^ ^ ^^V ^^

—

A SPECTRUM #10 [A7] \x. O SPECTRUM #2 [A2;

\:

1

1 1 1 1

la

1 1

i

31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY (Hz

Figure A9. Range defined by normalized traffic noise spectra.

41
these bands, the envelope width is about 12 dB. In the middle frequency

range, the width is as small as 5 dB. Within the envelope, individual spectra

may vary in overall shape by an amount allowed by the limits of the envelope.

As an example of this, two extreme spectrum shapes also are presented in Fig.

A9.

A.2 Data from Present Study

As part of the present study, a library of 107 15-minute analog

recordings of actual highway noise was obtained by the staff of the National

Bureau of Standards at various times of day at seven sites. These recordings

are fully documented in Ref. A12.

For the analyses in the main body of this report, the measured 1/3-octave

band spectra were used. To facilitate a comparison between the traffic noise

spectra obtained in the present study and those reported in the literature,

octave band levels were computed for one recording, corresponding to the 15-m

microphone position, at each of the seven sites used in the present

investigation. These octave-band data, computed from the energy-averaged

(LEQ) 1/3-octave band spectra and normalized relative to the corresponding

A-weighted sound levels, are plotted in Fig. AlO. Comparison with the

published data (Figs. A5 through A8) indicates that these seven selected

spectra are similar to those reported in the literature. For further

comparison, the range defined by the individual normalized octave band levels

of Fig. AlO is shown in Fig. All along with two individual spectra. As was observed with the corresponding plot (Fig. A9) for the published data,

individual spectra may vary in overall shape within the envelope shown. To

allow for a more direct comparison, the range of normalized octave-band levels

for published data (Fig. A9) and the range from the current study (Fig. All) are both shown in Fig. A12. It can be seen that the normalized octave-band

level ranges established from the published data and from the selected data of

the current study are very similar. The ranges are within 2 dB of each other

over the entire frequency range, except in the 63, 125, and 8,000 Hz octave

bands where the limits of the ranges differ by no more than 4 dB. To within a few dB, none of the published octave-band spectral shapes is excluded by the

range of individual normalized octave-band levels from the data set collected

in this study.

42
20

10 —

-10

ISI

-20

30

O

A

D

COMSAT SITE, 1700 HRS.

GUDE DR. SITE, 1700 HRS.

RT. 28 SITE,1600 HRS.

B-W PKWY. SITE, 1700 HRS.

1-95 SITE, 1700 HRS.

355 & SHADY GROVE SITE, 1700 HRS.

355 & Q.O. RD. SITE, 1700 HRS.

1 1 1

31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY (Hz|

Figure AlO. Normalized LEQ octave band spectra for each of the seven actual traffic noise recording sites of Section 2.1 at

the 15-m microphone position.

43
ISI

20

tt 10

-10

20

-30

O COMSAT SITE, 1700 HRS.,

ISm MICROPHONE

A 355 & Q.O. RD. SITE, 1700 HRS.,

15m MICROPHONE

J L 1 J L J

31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY (Hzj

Figure All. Range of normalized octave band levels for each of the seven actual traffic noise recording sites of Section 2.1

at the 15-m microphone position.

44
20

10

-10

ISI

-20

-30

RANGE OF OCTAVE BAND

LEVELS FROM CURRENT STUDY

RANGE OF OCTAVE BAND

LEVELS FROM PUBLISHED DATA

1 I 1

31.5 63 125 250 500 1000 2000 4000 8000 16,000

BAND CENTER FREQUENCY (Hz)

Figure A12. Comparison of ranges of normalized octave band levels for

the published data of Fig. A9 and for the data of Fig. All

from the current study.

45
A.3 References

Al. Cook, D.I., and Van Haverbeke, D.F., Trees and Shrubs for Noise

Abatement (U.S. Forest Service, Department of the Interior,

Washington, D.C. , July 1971).

A2. Close, W.H., and Wesler, J.E,, Vehicle noise sources and

noise-suppression potential, pp. 14-33 in Motor Vehicle Noise Control , Mildred Clark (editor), Special Report 152 (Transportation Research

Board, National Research Council, Washington, D.C, 1975).

A3. Cannelli, G.B., Traffic noise pollution in Rome, Appl. Acous. _7, 103-115

(1974).

A4. de Lange, P.A. , Sound insulation of glazing with respect to traffic

noise, Appl. Acous. 2^, 215-236 (1969).

A5. A Review of Road Traffic Noise, RRL Report LR 357 (Road Research

Laboratory, Ministry of Transport, Crowthorne, Berkshire, England, 1970).

A6. Olson, N., Statistical Study of Traffic Noise, Report No. APS-476

(Division of Physics, National Research Council of Canada, Ottawa,

Canada, 1970).

A7. Cohen, A., Location-design control of transportation noise, J. Urban

Planning and Development Division, Proc. Am. Soc. Civil Eng. ^3^, 63-86

(December 1967).

A8. Brasch, J.K. , Vehicular traffic noise near high-speed highways, S)V Sound

Vib. J.(12), 10-24 (December 1967).

A9. Community Noise, Report No. NTID 300.3 (U.S. Environmental Protection

Agency, Washington, D.C, December 31, 1971).

AlO. Goodfriend, L.S., Control of highway noise, S)V Sound Vib. U6), 15-20

(June 1967).

All. Noise Exposure and Control in the City of Inglewood, California (Paul S.

Veneklasen and Associates, Santa Monica, California, November 1968).

A12. Flynn, D.R., Voorhees, C.R. , and Yaniv, S.L., Highway Noise Criteria

Study: Traffic Noise Data Base, NBS Technical Note 1113-1 (National

Bureau of Standards, Washington, D.C, 1980).

46
Appendix B. Equivalent Lane Concept

In order to establish the "effective location" of a highway such that a plot of sound level versus the logarithm of distance (from the "effective

location" to the point of observation) will, ideally, be a straight line, one hypothesizes a single virtual lane into which all the traffic is placed. The

resultant average sound level is given by

LEQ = 10 log

1=1 3=1

:ii (Bl)

where D is an effective distance from the point of observation to the center

of the virtual lane, v is an effective average exponent, and all other

variables are as defined in Sec. 4.2. In order to obtain the values for D and

V, one requires that Eqs. (9) and (Bl) yield the same average sound level,

thus obtaining

L ^ .

i=l -1 = 1

f.. Q..

S. . ZE

i=i j=i

f.. Q..

S, . ij

D (B2)

where f-j^j = ^-^ j/(i:lVj^ j) is the fraction of total traffic corresponding to the

i-th class of vehicles and the j-th lane. By selecting a reasonable value for

V, Eq. (B2) yields the effective distance, D. The value obtained for D is

very insensitive to the value selected for v.

In computing effective locations of virtual lanes in the present study,

the values of fj^A were obtained from traffic counts and classifications made

from the video recordings obtained simultaneously with the audio recordings.

The values of S^ -s were obtained from radar observations of traffic speed

obtained simultaneously with the audio recordings. The A-weighted "source

strengths" for three classes of vehicles (automobiles, medium trucks, and

heavy trucks) were taken from the FHWA Highway Traffic Noise Model (Refs.

20-23 in Sec. 5).

47
If all of the data required for Eq. (B2) are not available, simplified

expressions can be used, sometimes with little loss of accuracy. The most

obvious first step is to assume that all vehicles are traveling at the same

speed, regardless of lane or vehicle class. If all values of S-j^-; are the

same, Eq. (B2) reduces to

1 . J _ i^ -1 = 1

f.. Q.

13 1

i=l j = l

(B3)

where Q-j^ -; has been replaced by Qj^ since it is assumed that all lanes of

traffic are running at the same speed. If it is further assumed that all

lanes are carrying the same mix of traffic, Eq. (B3) can be simplified to

I.J . (B4)

where

j=i

f. =

11 (B5)

is the fraction of vehicles in a given class, regardless of the lane.

Equation (B4) should be reasonably accurate when all lanes, in both

directions, are carrying roughly the same volume and mix of traffic. If it can further be assumed that v-j^ = v = a constant, independent of the class of

vehicle, Eq. (B4) reduces to

= J Z (D^/D.)

-1

(B6)

48
independent of the mix of traffic or the source strength, Q^ ? , of the various

classes of vehicles.

In the present report, values of Dg were computed using Eq. (B2) but with

the simplification that all lanes in a given direction were assumed to carry

the same mix and speed of traffic. The values for v^ and v were all taken as

1.5, as in the FHWA Highway Traffic Noise Model.

iX U3. GOVERNMENT PRINTING OFFICE : 1983 O—380-997 (2443)

49

NBS-n4A iREv. 2-8C) U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

SHEET (See instructions)

1. PUBLICATION OR

REPORT NO.

NBS TN 1113-3

2. Performing Organ. Report No. 3. Publication Date

February 1983

4. TITLE AND SUBTITLE

Highway Noise Criteria Study: Relations Among Frequency Rating Procedures

5. AUTHOR(S)

Daniel R. Flynn and Simone L. Yaniv

6. PERFORMING ORGANIZATION (If joint or other thon NBS. see instructions)

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

7. Contract/Grant No. 6-3-0154

8. Type of Report & Period Covered

Final

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City. State. ZIP)

Office of Research, Environmental Design and Control

Federal Highway Administration

U.S. Department of Transportation

Washington, DC 20590

10. SUPPLEMENTARY NOTES

I I Document describes a computer program; SF-185, FlPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual sumnrtary of most significant information. If document includes a significant

bibliography or literature survey, mention it here) A series of calculations was performed to ascertain how well one frequency-weighted

rating, such as weighted sound level, loudness level, or perceived noise level, may be

predicted from another such rating. A total of 103 average sound level spectra,

measured at several distances from different types of highways, were used in these

calculations. It was found that knowing a single noise rating, such as the A-weighted

sound level, enables one to predict other outdoor ratings in this set of 103 spectra

with a standard deviation of the order of 1 to 2 dB. If, in addition, traffic speed

and mix and the distance to the highway are taken into account, these standard

deviations can be reduced to 0.5 to 1 dB, depending upon the particular noise rating

of interest. Equations are given for predicting one rating from another; the associated

standard deviations are presented as a measure of how well any given rating can be

predicted from a single measured, or otherwise known, noise rating. It is concluded

that it is not very critical which frequency-weighting procedure is used in conjunction

with highway noise criteria since one descriptor can be predicted from another with a rather small statistical uncertainty. Thus, if human response criteria, or stimulusresponse relationships, have been developed in terms of one frequency-weighting procedure, these may be translated into equivalent criteria expressed in terms of a metric

that is easier to measure or predict.

12. KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolons)

acoustics; environmental pollution; highway noise; motor vehicle noise; noise;

noise control; sound; traffic noise; transportation noise.

13. AVAILABILITY

|y I Unlimited

I I For Official Distribution. Do Not Release to NTIS

[y~| Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402.

I I Order From National Technical Information Service (NTIS), Springfield, VA. 22161

14. NO. OF

PRINTED PAGES

57

15. Price

$4.75

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