PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Noise indicator evaluation of road and rail traffic noise for indooroutdoor differences in acoustic parameters
Manuel LIENHART1; Peter LERCHER2; Michael CIK3 and Martin FELLENDORF4
1, 2, 3, 4

Graz University of Technology, Austria

ABSTRACTThe two main objectives of the extensive project ”INTRANOISE” were to study the effect of road and rail
traffic sound on human sleep patterns and explore the relationship between the subjective perception of 100
test persons and objectively measured acoustic and physiological parameters. The free field measurement
arrangement includes binaural dummy head measurements at the façade of the test subjects’ home and within
the bedroom, thus providing a solid basis for evaluation. Indoor-outdoor differences were assessed for various
sound indicators and their relation with people’s subjective and objective responses. People are able todistinguish between the investigated noise sources. This ability contributes to their subjective traffic noise
annoyance and can be linked to acoustic parameters. Indoor noise is the dominant factor for physiologicalreactions during sleep, leading to observable cardiovascular reactions. The link between reactions andacoustic measurements varies depending on the health indicator. Further studies are needed to determine,
which noise indicator is more appropriate. This paper aims to describe people’s subjective and objective
response to road and rail traffic noise using the difference of indoor-outdoor sound pressure levels.
Keywords: Naturalistic traffic noise, annoyance, physiological parameters, noise indicators

1. INTRODUCTION
The public increasingly recognizes noise as a larger scale problem. More and more people becomeaware of the effects noise has on the human organism. These effects are well documented in scientific
literature and can be summarized as direct physiological effects and psychological effects.
Physiological effects can involve impairment of sleep, reflected by various sleep disorders, physicalstress reactions reflected by activation of the autonomic nervous system, arterial hypertension andassociated cardiovascular diseases reflected by ischemic myocardial dysfunctions. Psychologicaleffects often includes impairment of well-being and psychological stress through a reflection of thegrade of annoyance (1,2).
Especially urban soundscapes are increasingly affected by environmental noise from transport
sources (3,4). The direct effect of sound energy on the human hearing is scientifically well researched
and widely accepted (5,6). Generally, aural effects can be neglected in assessments, as the soundpressure level of the auditory traffic noise is typically below levels, which are known to caus e hearing
damage. This means, that non-auditory effects of noise on the human health is generally not the direct
result of sound energy, but noise as a more general stress inducing factor.
Non-auditory noise effects include both physiological and psychological effects. The result of
those both types of effects is in most cases annoyance, mental health issues, sleep disturbance andphysiological functions as well as effects on cognitive processes such as communication and cognitive
performance (7). These non-auditory effects of noise are less well researched and accepted, comparedto auditory effects.
It is estimated that about 28% of the total population living in the European Union, are constantly
affected in their quality of life, well-being or sleep patterns, which results in an increased health risk
for the individual and increased health costs for the public (8).
1manuel.lienhart@tugraz.at
peter.lercher@tugraz.at
3
michael.cik@tugraz.at
2

michael.cik@tugraz.at

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One part of the problem is the still open question of a describing, clinically relevant threshold levelof noise caused by various traffic modes. To date, no clear consensus has been reached.
Additionally, it is unclear which parameters can and should be used, in order to scale for serious
health impairment. In most scientific noise studies, data was obtained either from epidemiologicalfield studies or from studies performed in sleeping laboratories. Such laboratory studies do offer theclear advantage of standardized conditions and usage of complex measuring equipment (e.g.
polysomnography - PSG) (9-11). This standardization level of test conditions cannot be achieved infield studies, which mostly are influenced by a broad range of external factors.
However, these controlled laboratory studies clearly allow a better simulation of actual in-situ
conditions and further epidemiological studies with representative sample sizes (12-15). It must be
noted, that the mentioned epidemiological studies, made use of questionnaires for reflecting the
subjective estimation of noise-induced discomfort in terms of data generation, and therefore lackobjective variables.
The prediction of subjective noise-induced discomfort is a difficult task (16). As the informational
quality of a certain sound like semantic or pragmatic aspects and the intentional attitude of a person
are highly situation-specific, they cannot be modelled in a reliable way. Therefore, fuzzy mathematicalsoft-computing methods describing the relationship between noise-induced discomfort and objective
noise parameters are important to consider (17) and should be further investigate.
1.1 Annoyance resulting from noise exposure
When used in combination with environmental influences the term ann oyance presents itself withsome ambiguities. In general the term annoyance is used to describe negative feelings, as for example
disturbance, displeasure, dissatisfaction, nuisance and irritation. Guski suggests to even include
somatic damage, loss of control and orientation, negative assessment of the noise source and highsound levels (18).
Since noise is described as unwanted sounds, the combined term n oise annoyance may beunderstood as an emotional process, as the reaction is closely tied to the affective experience of theindividual towards the noise source. Evidence of this assertion stems from investigations on aircraft
noise, where the existence of some correlation between the judgment of annoyance caused by aircraft
noise and the fear of aircraft accidents has been found (19,20). In relation to this, an attitudinal
dimension may extend noise annoyance, as the subjective rating of severity of annoyance often
depends on the acquired information about and the feelings towards the source of noise (21). This
relation of noise to the test subject may be expanded by considering the dependence of the subject tothe source of noise. When considering subjects, who for instance economically depend on the sourceof noise, might tend to feel less annoyed by it, as compared to others who are not.
Traffic noise continues to be a subject of continuous and increasing concern to people causing
annoyance and associated sleep disturbances, representing the direct and most relevant factors
affecting health.
1.2 Cardiovascular reactions
Considering physiological effects of noise, the basic reaction of the human body to noise events isthe prompt increase of the heart rate and change in systolic blood pressure. Carter et al. (22) were able
to show the immediate rise in heart rate following a noise event under laboratory conditions. They
were able to show, that intermittent or periodic noises during sleep induce a biphasic heart reaction
with a transient constriction of the peripheral blood vessels as well as clear changes in theelectrocardiography (ECG). The biphasic response of the heart first shows a rise of the heart rate
followed by a decompensation reaction with a marked drop in heart rate.
Griefahn et al. found a connection of the autonomic arousals during sleep and traffic noise in theirstudy, in which the response of the heart rate to traffic noise during sleep was analyzed. The extensive
study took place in a laboratory under standardized conditions (9,10).
However, these studied cardiac effects have been solely based on laboratory data. Largeepidemiologic studies, which examine the cardiac risk are solely based on question naires, yet thoseare essential for scaling the burden and identifying the meaningful limits for preventive actions agains t
noise emissions. To a certain extent, it might be obvious, that the normal surroundings and habits ofa test subject might describe a risk better, than the unfamiliar surroundings of a sleeping laboratory.
Presently, the Night Noise Guidelines for Europe of the World Health Organization (WHO) (8)demanding for a NOAEL (no observed adverse effect level) of NOAELAmax ≥ 42 dB. The heart rate
reacts very sensitive to external stimuli as it is regulated by the autonomic nervous system. The WHO

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recommends in the Night Noise Guidelines for Europe that field studies must be carried out, in order
to better describe the influence of traffic noise regarding its potential in causing chronic disorders ,e.g. sleep disturbances or cardiovascular diseases, more accurate.

2. METHODOLOGY
The two main goals of the study’s concept were to investigate the influence of road and rail traffic
noise on sleep of individuals and additionally to explore the relationship of subjective perception oftest subjects with objective measured psychoacoustic and physiological parameters. The crucial pointof the project was, that all measurements were done in the free field. Since the project itself was
already previously presented, only the most relevant aspects will be presented in the following
subchapters 2.1 and 2.2 (23,24).
2.1 Study design, measurement area and choice of test subjects
In the first step test subjects were selected from a database consisting of test subjects having
participated in our previous studies (510 persons were tested in their general health, well -being andconnectivity to traffic noise). For this study 100 representative test subjects were intended to beinvestigated. Two different areas for measurements were provided:• Areas dominated by road traffic noise• Areas dominated by rail traffic noise
All measurements were done at the home of the test subjects and were performed for 5 consecutive
days (4 nights) per each test subject. For the study 3 relevant time periods were identified:• Evening: pre-sleep phase• Night: sleep phase• Morning: post-sleep phase
2.2 Free field study
For the field study different relevant parameters were investigated and analyzed. The most relevant
parameters for this paper will be described in the following sub-chapters.
2.2.1 Subjective and socio-demographic data and health status
Collection of socio-demographic data and health status was done by means of a basic questionnaireat the beginning of the investigation including the most important factors for test subjects inconnection with environmental influences, especially traffic n oise: Sex, Age, Education, Housing
conditions regarding traffic noise exposure and further residential surroundings.
In addition, the individual health status of each test person was assessed at the beginning of themeasurement week in a conversation, during which the most important health-relevant parameterssuch as regular consumption of certain drug groups, use of a heart pacemaker or hearing aid, generalhearing problems, therapy of cardiac arrhythmias or obstructive sleepapnoe were asked by a physician .
2.2.2 Annoyance questionnaire
In the “evening” and “morning” measurement periods a continuous questioning of the current
traffic noise annoyance on basis of the ICBEN 11-graded interval scale (14) using the so called„experience sampling method” (time near seizing of experiences, feelings and behavior) (25,26) wasdone. In individual investigation areas the test subjects evaluated their half hourly annoyance by rail
and/or road traffic noise and also responded to the questions of the experience sampling m ethod in
the measurement periods on the days of investigation.
2.2.3 Objective acoustic measurements
Goal of the acoustic measurements was to achieve a time-synchronicity between the result of theannoyance questionnaire (traffic noise annoyance-rating, experiences, feelings and behavior) andacoustic parameters including sound pressure level and psychoacoustics reflecting the total quantityof the test subject’s acoustic load. Measurements (recordings) of current existing sound emissions
with 2 binaural dummy heads HSU III.2 were done in combination with a SQuadriga II mobile
recording system (HEAD acoustics GmbH) to get a realistic illustration of the traffic noise exposureof each test subject in the investigated area. For each test subject one measuring point was inside thehouse in the sleeping room and the second one was outside in front of the house or apartment.
Measurements were done during all three time periods and afterwards statistically correlated with thecollected subjective and physiological data. The recordings were analyzed using the software package

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“ArtemiS SUITE V7.3” by HEAD acoustics GmbH by means of the latest acoustic und psychoacoustic
standards.
Data collection of traffic volume in the investigated time period was also done for com parison.
2.2.4 Measuring physiological parameters
Sleep disturbances, defined both as awakenings or changes in depth of sleep, are frequently
associated with traffic noise and are an important criterion in defining limits for noise pollution. The
physiological measurements were done during the 4 night periods for each test subject in theinvestigated area.
For this project it was necessary to assess sleep parameters in a reliable way, which needed to beeasy to use for non-professionals. A suitable tool to monitor changes in the depth of sleep is an
actimeter. This is a simple method that can be used on several subjects simultaneously. The results
obtained are in good comparison of those obtained using the PSG allowing the assessment of changes
in depth of sleep at home (27).
In this project, heart rate and also body movements were measured by using a wrist-actigraph,
specifically type “wActiSleep-BT Advanced Activity Monitoring Solution with Heart Rate” from
ActiGraph, LLC. The measurement and interpretation of heart rate variability (HRV) al lows
conclusions concerning the adaptability of the heart to internal and external stimuli. The actigraph isequipped with an acceleration sensor that translates arm movements into a numeric representation,
which is stored in memory. Afterwards, the acquired raw data was analyzed using the proprietary
software package “ActiLife V6” by ActiGraph, LLC. For the in-depth analysis, two algorithms fordifferent age groups were used:• Sadeh algorithm: Is considered for younger test subjects, because it was developed using
subjects of an age span ranging from 10 to 25 years (28).
• Cole-Kripke algorithm: Is considered for adult test subjects, because it was developed using
subjects of an age span ranging from 35 to 65 years (3229
Therefore, two different age groups were distinguished when analyzing the obtained sleep data.
Sadeh’s algorithm is applied to sleep data from test subjects who were 18 to 25 years old and Cole Kripke’s algorithm is used for sleep data from test subjects who were 26 years and up at the time ofdata acquisition.
2.3 Aim of this paper
Since it is still an unsolved question, which noise indicator is to be used as means to properly
assess noise caused by various traffic modes for consideration in regards of both physiological andpsychological research questions, further studies are needed.
This paper aims to describe people’s subjective and objective response to road and rail traffic noiseusing the difference of indoor and outdoor sound pressure levels. The difference of the indoor to the
outdoor sound pressure levels is used as assessment of the sound dynamics of traffic noise in thesurrounding of test subjects homes and tested for applicability regarding the description ofphysiological and psychological effects.

3. RESULTS
During the four years of conducting the study 97 test subjects were analyzed over 97 measuring
weeks on different investigation sites. In general, the methodology was under stood well by the test
subjects. The technical part consisting of physiological and acoustic measurements worked well onsite. The rating of subjective annoyance and completing of questionnaires was done without major
problems. During the course of the project small inconsistencies occurred while gathering data of 3
test subjects. Therefore, only the data of 94 test subjects will be included in the analysis of the acoustic
parameters.
Generally, people were able to distinguish between the investigated noise sources. This ability
contributes to their subjective traffic noise annoyance and can be linked to acoustic par ameters. Indoor
noise is the dominant factor for physiological reactions during sleep, leading to observablecardiovascular reactions. The link between reactions and acoustic measurements varies depending on
the health indicator.
The factor of indoor-outdoor difference will be described as delta of outdoor subtracted by indoor
sound pressure levels regarding a specific time domain.

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3.1 Results of the indoor-outdoor difference regarding annoyance ratings
The participating test subjects were asked for a rating (based on the 11-graded ICBEN interval
scale)) of their subjective traffic noise annoyance for time period s of 30 minutes. In a first step, theseratings were tested using a linear regression model against different acoustic parameters, including
the indoor-outdoor difference, describing the 30 minute rated timeframe. It quickly became apparent,
that although most parameters produced statistically significant results (p < 0.05), the explained
variance of the best model was rather low with about 5%, which is a result of the amount of “notfeeling disturbed” ratings of about 43.7%. This could not be improved by excluding those ratings, as
the explained variance decreased to about 4.9, while still producing significant results. Previous
evaluations of the raw data showed a broad variability of the concerned sound pressure level parameterfor each rating from 0 to 10 (23,24). Therefore, it was necessary to include socio demographic
parameters of the test subjects in a multivariate linear regression model to put the ratings inperspective. The best results were produced by including the factors gender, age, general annoyance,noise sensitivity, health condition and perception of sleep disturbance due to traffic noise. These
results are presented in Table 1, which shows the 5 best “conventional” sound pressure levelparameters, as well as all of the delta-values.
Table 1 – Multivariate linear regression of acoustic parameters, gender, age, general annoyance, noise
sensitivity, health condition and perception of sleep disturbance due to traffic noise against annoyance
ratings
Parameter

p-ValueSignificanceVariable(p < 0.05)

adjusted R²

SPL [dB(C)] outside 75th percentile

8.29578E-19

Yes

0.2113873

SPL [dB(C)] outside arithmetic mean

1.06468E-16

Yes

0.209128

SPL [dB(C)] outside maximum value

1.18102E-16

Yes

0.2090797

LCeq outside

1.57983E-16

Yes

0.2089442

SPL [dB(C)] outside median

2.91478E-16

Yes

0.2086589

delta maximum SPL [dB(A)]

3.85321E-05

Yes

0.1967772

delta LAeq

6.43069E-05

Yes

0.1965444

delta 95th percentile SPL [dB(A)]

0.000120647

Yes

0.1962591

delta 75th percentile SPL [dB(A)]

0.000144185

Yes

0.1961785

delta arithmetic mean SPL [dB(A)]

0.000215739

Yes

0.1959966

delta 25th percentile SPL [dB(A)]

0.00153317

Yes

0.1951199

delta median SPL [dB(A)]

0.00222493

Yes

0.1949555

delta 5th percentile SPL [dB(A)]

0.002964853

Yes

0.1948293

delta maximum SPL [dB(C)]0.008318639

Yes

0.1943807

delta LCeq

0.03098191

Yes

0.193825

delta 95th percentile SPL [dB(C)]0.08214834

No

0.1934321

delta 75th percentile SPL [dB(C)]0.1088335

No

0.1933236

delta minimum SPL [dB(A)]

0.1356018

No

0.1932407

delta arithmetic mean SPL [dB(C)]0.1883469

No

0.1931211

delta median SPL [dB(C)]0.2630704

No

0.1930059

delta 25th percentile SPL [dB(C)]0.3157415

No

0.1929467

delta minimum SPL [dB(C)]0.6530683

No

0.1927528

delta 5th percentile SPL [dB(C)]0.6718815

No0.1927473

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3.2 Results of the indoor-outdoor difference regarding physiological measurements
In addition to the subjective half hourly annoyance ratings, the test subjects’ sleep was assessed
during the week, in order to test for physiological effects of traffic noise. Similar to the annoyance
ratings, the sleep parameters were first tested using a linear regression model against different acoustic
parameters, including the indoor-outdoor difference, describing the normal night timeframe of 10 pm
to 6 am. Although most parameters produced statistically significant results (p < 0.05), again the
explained variance of the best model was rather low with about 6%. This is plausible, as physiological
data needs to be evaluated and corrected for various parameters regarding the test subjects. Several
sleep describing parameters have been evaluated for explanatory socio demographic variables of thetest subjects. Based on the acquired data, those vary for each sleep parameter. In th is paper, we will
focus on the parameters sleep efficiency and total sleep time. As for the sleep efficiency models, acombination of the acoustic parameters and health condition and age proved to be the best match (cf.
Table 2, showing the 5 best “conventional” sound pressure level parameters, as well as all of the deltavalues). The best results concerning the total sleep time were achieved by including gender, age andperception of sleep disturbance due to traffic noise (cf. Table 3, showing the 5 best “conventional”
sound pressure level parameters, as well as all of the delta-values).
However, apart from the LCeq indoor-outdoor difference, no significant result (p < 0.05) could beachieved for the other parameters. This proved to be different for the total sleep time, which produced
significant results for all of the indoor-outdoor difference models, though the explained variance was
low for both sleep efficiency and the total sleep time.
Table 2 – Multivariate linear regression of acoustic parameters, health condition and age against
sleep efficiency
SignificanceVariable(p < 0.05)

adjusted R²

2.11E-05

Yes

0.06522817

SPL [dB(C)] inside maximum value

0.000528259

Yes

0.04956241

SPL [dB(A)] inside maximum value

0.001722714

Yes

0.04385209

SPL [dB(C)] inside 95th percentile

0.002925597

Yes

0.04131

LAeq inside

0.006099754

Yes

0.03780801

delta LCeq

0.03314535

Yes

0.02992482

delta maximum SPL [dB(C)]0.116618

No0.02439899

delta 95th percentile SPL [dB(C)]0.1179285

No0.02435216

delta maximum SPL [dB(A)]

0.1579803

No0.0231471

delta median SPL [dB(A)]

0.2066695

No0.02208054

delta minimum SPL [dB(A)]

0.2950832

No0.02074634

delta minimum SPL [dB(C)]0.3406099

No0.02024212

delta 75th percentile SPL [dB(C)]0.3756299

No0.01991187

delta LAeq

0.4098199

No0.01962858

delta 25th percentile SPL [dB(A)]

0.4547769

No0.01930503

delta 5th percentile SPL [dB(A)]

0.7332986

No0.01811671

delta 25th percentile SPL [dB(C)]0.8080164

No

0.0179634

delta median SPL [dB(C)]0.8457079

No0.01790651

delta 75th percentile SPL [dB(A)]

0.8564483

No0.01789269

delta 95th percentile SPL [dB(A)]

0.877693

No0.01786839

delta 5th percentile SPL [dB(C)]0.9488414

No0.01781582

Parameter
LCeq inside7992

p-ValueTable 3 – Multivariate linear regression of acoustic parameters, gender, age and perception of
sleep disturbance due to traffic noise against the total sleep time

Parameter

p-ValueSignificanceVariable(p < 0.05)

adjusted R²

SPL [dB(A)] inside maximum value

3.77E-07

Yes

0.1113088

delta 75th percentile SPL [dB(A)]1.04E-05

Yes

0.09565355

delta 95th percentile SPL [dB(A)]

1.54E-05

Yes

0.0937927

delta median SPL [dB(C)]0.000111062

Yes

0.08441378

delta maximum SPL [dB(A)]

0.000161302

Yes

0.08264567

delta 75th percentile SPL [dB(C)]0.000205198

Yes

0.08150593

delta 25th percentile SPL [dB(C)]0.000592569

Yes

0.07649366

delta median SPL [dB(A)]

0.000594685

Yes

0.07647685

delta 95th percentile SPL [dB(C)]0.000677861

Yes

0.07585957

delta minimum SPL [dB(C)]0.000920045

Yes

0.07442076

delta 25th percentile SPL [dB(A)]

0.002174228

Yes

0.07038563

delta 5th percentile SPL [dB(C)]0.002541042

Yes

0.06965721

delta LAeq

0.002691289

Yes

0.06938908

delta maximum SPL [dB(C)]0.004669397

Yes

0.06682598

delta 5th percentile SPL [dB(A)]

0.005624777

Yes

0.06596414

delta LCeq

0.006706187

Yes

0.06515218

delta minimum SPL [dB(A)]

0.01341816

Yes

0.06197449

4. DISCUSSION AND CONCLUSION
The aim of this paper has been to describe people’s subjective and objective response to road andrail traffic noise, using the difference of indoor and outdoor sound pressure levels. The difference of
the indoor to the outdoor sound pressure levels was intended to be used as assessment of the sounddynamics of traffic noise in the surrounding of test subjects homes. It was tested for applicability
regarding the description of physiological and psychological effects of traffic noise. Although thefactor didn’t prove to be useful regarding the explanation of every aspect of physiological andpsychological effects of traffic noise, it seems to be one way to explain a subset of them.
The experience, gathered while conducting the study, shows, that people are able to distinguishbetween the investigated noise sources and focus on them. This ability contributes to their subjective
traffic noise annoyance and can be linked to acoustic parameters for the outside of their homes, whichis indicated as well in the evaluation, as inside models are not conclusive. In regards of sleep, indoor
noise is the dominant factor for physiological reactions, leading to observable cardiovascular reactions.
It is important to mention, that annoyance ratings and sleep efficiency were best explained by models
using the C-weighting.
As a conclusion, it can be stated, that the data acquisition methodology works well and the methods
can be applied within other projects. The amount of acquired data is extensive and p rovides a goodbasis for further detailed research projects, combining different fields of expertise. However, theacquired data seems to feature a few unknown relations, which need to be determined in order to beable to make the correct interpretations. Therefore, it needs to be further evaluated, especiallyconcerning the connection of the acquired objective with the subjective questionnaire data.

ACKNOWLEDGEMENTS
This project has been funded by the Austrian Science Fund (FWF). Thanks a lot.

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