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The effects of site selected variables on human responses to traffic noise, part III: Community size by socio-economic status by traffic noise level
Journal of Sound and Vibration (1979) 67(3), 409423
THE EFFECTS RESPONSES
OF SITE SELECTED
TO TRAFFIC
BY SOCIO-ECONOMIC
VARIABLES
ON HUMAN
NOISE, PART III: COMMUNITY STATUS BY TRAFFIC
SIZE
NOISE LEVEL
J.S. BRADLEY Faculty of Engineering AND
B.A. JONAH-~ Department of Psychology, The University of Western Ontario, London, Canada (Received 31 August 1978, and in revisedform 18 July 1979)
This paper reports the results of the third part of a field study of human responses to traffic noise. The influence of traffic noise level, community size, and socioeconomic status were investigated in a controlled manner determined by subject selection procedures. Human responses were obtained from interviewer administered questionnaires, and were as spatially and temporally coincident with the noise measurements as possible. Noise measurements were obtained from several days of rapidly sampled digital recordings. Tragic noise level was the major predictor of the intensity of elicited responses. A number of other signilicant effects were observed largely related to community size. These were explained as due to parallel variations in the perceived necessity of vehicles, the annoyance with aircraft noise, and the logarithm of the vehicle flow rate. The results did demonstrate effects related to community size, and thus it is unwise to extrapolate the results of large city noise studies to small communities and rural areas.
1. INTRODUCTION
The responses of subjects to traffic noise have been studied as a function of controlled site variables. The combined effects of traffic noise levels and housing type [l] (Part I) and the combined effects of traffic noise levels, road type, and socio-economic status [2] (Part II) have been previously reported. The present paper is concerned with the combined effects of traffic noise levels, community size and socio-economic status. Other site variables such as road type and housing type were kept constant. Thus, all 600 subjects in this study resided in single unit housing on regular roads. Twenty-four sites were measured consisting of all combinations of four traffic noise levels, two levels of SES (socioeconomic status), and three levels of community size including communities with populations from 7000 to 2 000 000. Most major studies of traffic noise have been carried out in very large urban areas. It was not at all clear whether similar results would be found in smaller communities, t Present address: Road and Motor Vehicle Traffic Safety, Transport Canada, Ottawa KiA ON5, Canada. 409 0022-460X/79/230409
+ 15 $02.00/O
0
1979 Academic Press Inc. (London)
Limited
410
J. S. BRADLEY AND B. A. JONAH
sometimes approaching rural conditions. It was thought that social differences might occur between community sizes as well as differences in the overall noise exposures. Previous research had not clearly determined the influence of status on human responses to traffic noise [3-53, and it was thus thought valuable to include this as a third controlled site variable. Both spontaneous and elicited responses were considered and composite response scales were constructed and examined in terms of the intensity of negative responses as in the previous two parts of the study [l, 21. These responses were investigated as a function of traffic noise level, SES (socio-economic status), community size and the various interactions among these variables.
2. METHOD
2.1. SITE SELECTION AND MEASUREMENT PROCEDURES Twenty-five subjects were interviewed from the fifty or more dwellings at each of twenty-four sites chosen according to the procedures described in Part I [l]. The twentyfour sites consisted of all possible combinations of four levels of traffic noise, two levels of SES, and three sizes of community. Traffic noise levels ranged from the quietest to the noisiest possible within the other constraints of site choice and where traffic noise was the only major source of environmental noise. Three sizes of communities were obtained by choosing sites in the following communities: (1) large, metropolitan Toronto (population 2 000 000); (2) medium, London, Ontario (population 240 000); and (3) small, Tillsonburg and Ingersoll, Ontario (populations 7000). Sites were selected so that high status sites were clearly higher in status than the low status sites. The large community sites were common to the study of road type reported in Part II [2]. A-weighted noise levels were digitally recorded once per second for four days in the medium and small sized communities and once per two seconds for six days in the large community. In both cases, the recordings included a Saturday and a Sunday. After the initial measurements in small and medium sized communities, the revised sampling schedule was found to be a slightly better optimization [6]. There was approximately one noise measurement position per ten subjects. A number of standard statistical noise measures were considered for each of the day (07:0&22:00 hours), the night (2290-0790 hours) and the twenty-four hour periods for each of: a weekday average (for either 2 or 4 weekdays), a Saturday, a Sunday, and an overall day type average. All noise measures were individually extrapolated to be representative of noise levels at the facade of each dwelling. Other noise and human response measurement procedures were described in Part I [l]. 2.2. FORMATION OF COMPOSITE RESPONSE SCALES A principal axis factor analysis was performed on eighty of the survey responsest and the live extracted factors were rotated by varimax. These five factors accounted for 71% of the variance in the eighty response items. The first factor included items that were measures of directly elicited annoyance to traffic noise. The remaining factors were labeled quality of sleep and health, unusual neighbourhood noise annoyance, interference with activities, and perceived necessity of vehicles respectively. Composite response scales were t A maximum of eighty items was a result of the limitations of available computer programmes.
HUMAN
RESPONSES
TO TRAFFIC
NOISE,
PART
411
III
constructed by averaging the responses on those items which the factor analysis indicated belonged together and were labelled as above. In addition, convenient sub-divisions of the interference scale were created by summing related items to measure interference with specific activities such as sleep and speech interference. For the annoyance scale item-total correlations ranged from 0.49 to 0.83 and the KR20 coefficient (Cronback coefficient Alpha) was O-95. For the interference scale, item-total correlations ranged from 0.48 to 0.92 and the KR20 coefficient was O-95.Thus, these two main response scales were quite internally consistent.
3. RESULTS 3.1.
NOISE AND VEHICLE
FLOW
MEASURES
AS PREDICTORS
OF RESPONSES
It was desired to investigate how responses depended on the various site variables and their interactions as well as to determine the relative merits of several noise measures as descriptors of the noise. These two aspects were considered separately by first investigating the prediction accuracy of a number of standard statistical noise measures. Noise measures were evaluated with respect to three main response scale scores: annoyance, overall interference, and elicited sleep interference. Subsequent, more detailed analyses of responses were carried out with respect to a single generally acceptable noise measure. Figure 1 shows plots of the correlation coefficients for all three responses versus noise I
I
I
I
I
I
I
I
x-x Interference
x-x Elicited sleep interference
Noise msasures
Figure
1. Correlations
of responses
with noise measures.
O-0,
Day; o-0,
night;
x-x,
24 hour.
J. S. BRADLEY AND B. A. JONAH
412
TABLE 1
Correlations between response scores and day, night and 24 hour noise measures (N = 600) Response
Period
Annoyance
Day Night 24 Hours
0.435 0.455 0444
0.475 0.464 0.476
0.43 1 0.469
0.453 0.348 0.402
0.468 0.471 0.469
0.242 0.388 0.368
0.415 0.437 0.43 1
0.472
0.372 0.419 0.415
Overall interference
Day Night 24 Hours
0.337 0.340 0.340
0.344 0.342 0.344
0.350 0.328 0.341
0.341 0.294 0.286
0.341 0.337 0.341
0.173 0.273 0.271
0.308 0.316 0.322
0.341
0.254 0.292 0.303
Elicited sleep interference
Day Night 24 Hours
0.218 0.220 0.218
0.236 0.234 0.235
0.247 0.219 0.246
0.241 0.186 0.267
0.237 0.235 0.237
0.083 0.186 0.140
0.196 0.213 0.191
0.237
0.165 0.207 0.153
0.479
measures
for different time periods. All correlation coefficients for the three responses and noise measures for the day, night and 24 hour periods are given in Table 1. The vertical axis of Figure 1 is quite expanded and so many small differences between correlation coefficients are not statistically significant. Figure 1 and Table 1 indicate patterns of correlation coefficients roughly similar to those observed in Parts I and II of the study [l, 21. In particular, night time L,, values were the least satisfactory predictors of responses probably because L,, at night was least representative of local traffic’ noise and more representative of general background noises. One exception noted here was that L, values were also noticeably inferior predictors of annoyance, especially during the day time. As in the previous studies, LNp (the noise pollution level) produced correlations inferior to the corresponding L,, values, and LDN (the day-night sound level) was no more than marginally superior to Lzi. The three most commonly used noise measures, LIO, LSO and L,, were again seen to be approximately equally accurate predictors of responses. The 24 hour values of L,,, L,, and L,, explained 22-23x of the variance in annoyance responses for the 600 subjects considered.? This indicated more accurate and more reliable relationships were achieved than in previous traffic noise research where typically 10% or less of the individual response variance has been explained. Correlations between responses and noise measures were performed separately for each community size. Even though sites were chosen to be of equivalent traffic noise levels independent of community size, it was thought that other differences in the noise exposure might exist. For example, background noise levels might be more dominant in larger communities. The magnitude of the correlation coefficients varied with community size and results for small towns were a little irregular, but results were largely the same as shown in Figure 1 for all 600 subjects. Again, L,, at night was the least successful predictor. However, LgO at night became increasingly relatively less successful with increasing community size. This probably indicates that in larger communities L,, at night is less closely related to local traffic noise, and more a measure of more distant sources. Combinations of noise measures from day and night time periods were tested by multiple regression analysis to assess if more accurate compound noise predictors could be obtained. Scores on the three response scales were regressed onto combinations of noise measures from the day, night and 24 hour periods. The compound noise measures t The choice of N = 600 was dictated the factors influencing the large variance
by the obvious preference to consider individual subject responses in these responses. This approach was justified in Part I [l].
and
HUMAN RESPONSES TO TRAFFIC NOISE, PART III
413
formed by the multiple regression analyses were evaluated in terms of the variance explained in excess of that for L,24 . Practically none of the combinations increased the variance explained by 1% or more. For annoyance, only a combination of Lzi, Lf” and Lzq increased the variance explained by a little over lx, or approximately the same as the day time L,, value. The importance of vehicle flow measures as predictors of responses was investigated in combinations both with and without Lzi values. Multiple linear regression analyses were performed by regressing the response scores onto combinations of the predictors. First, regressions were performed with L$’ forced to be chosen first and the various vehicle flow measures then selected according to which explained the largest portion of the remaining unexplained variance at each step. The second set of regressions were similar but with Lgi values omitted, and with the order of the vehicle flow variables chosen according to the amount of the remaining variance that they explained. The results of these multiple regressions for all 600 subjects are given in Table 2 in terms of the multiple correlation coefficient after each step. It is seen from this table that essentially the same prediction accuracy could be achieved by using vehicle flow measures alone as with Lzi and vehicle flow measures. Adding vehicle flow measures to Lzt values increased the variance explained by up to 2.6% in the case of annoyance responses, such that the combination of physical predictors explained essentially 25% of the variance in annoyance responses. Similar multiple regression analyses were performed separately for each community size. It was found that the variance explained was increased most for the large community. Thus, in the large community vehicle flow parameters added most to the prediction accuracy of Lz: values. This may have been partially related to the higher vehicle flow rates at the higher noise level sites in the large community. The results were again interpreted as providing sufficient justification to use the one simple noise measure Lzi in further analyses of responses. It was seen that only the addition of vehicle flow measures provided clearly increased prediction accuracy above that for Lzi. The importance of vehicle flow measures appeared to vary with community size. The simplification of considering only one noise measure led to a beneficial concentration
on the influence
of the site variables
and their interactions
on the responses.
TABLE 2
Increasing
multiple
correlation
coeflcients
as succeeding
noise
and vehicle flow measures
were added (N = 600) Annoyance
Interference
L
O-469
L
lo;( WH)
0.492
lo&m)
VPH NT PTK
O-496 (ns) O-496 (ns) 0.496 (ns)
NT PTK VPH
log( VPH)
0.487 0.492 0.494 (ns)
1ogW) VPH NT
log( VPH) NT VPH PTK
Elicited sleep
0.341 0.348
of heavy p < 0.05.
log( VPH)
0.245 (ns) 0.253 (ns)
log(W
0~256(ns)
log( VPH)
0.231 0.241 (ns) O-243 (ns) 0.245 (ns) 0.245 (ns)
NT
0.336 0.339 (ns) 0.344 (ns) 0.351 (ns)
VPH = overall vehicle flow rate per hour, NT = number heavy trucks. (ns) indicates term did not add significantly to the prediction,
0.237
Le*
(ns) 0.350 (ns) 0.352 (ns) O-357 (ns) 0.357 (ns)
NT
log(NT) VPH PTK trucks
per hour,
PTK = percentage
of
414
J. S. BRADLEY AND B. A. JONAH
3.2. ELICITED RESPONSES TO TRWFIC NOISE The composite scale scores were considered by multiple regression analyses, with the variables entered into the equation in a stepwise manner according to the portion of the unexplained variance for which they accounted. The variables were traffic noise level (L$), SES (socio-economic status), community size, size by SES, size by noise level, SES by noise level, and SES by size by noise level. Annoyance scores increased with traflk noise level (F = 6.25, df = l/596, p c O-01),and also with increasing community size (F = 4.23, d?= l/596, p < O-04). In addition, a significant community size by traffic noise level interaction (F = 5.10, df = l/596, p -C0.03) indicated that at the higher noise levels subjects living in the small towns reported less annoyance than the subjects in the medium and large sized communities. This is illustrated in Figure 2 which shows separate regression lines for each of the six combinations of community size and SES. Subjects living at noisier sites were more often annoyed than subjects at quieter sites (F = 86.13, df = l/598, p c ONll). Table 3 summarizes the variance explained by each predictor variable for all the major elicited responses and also gives the associated significance levels. Overall interference scores increased with traffic noise (F = 55.88, df = l/596, p = OGOl). A significant size of community by traffic noise effect (F = 8.09, df = l/596, p < 0.01) revealed that the size of community effect occurred mostly at the higher levels of traflic noise, and a noise level by SES effect (F = 11.95, df = l/596, p < OGOl) revealed that low status subjects indicated increased interference at higher noise levels (see Figure 3). SES (F = 27.03, df = l/484, p < OXlOl) and size by traffic noise level (F = 5.91, df = l/484, p < O-02)influenced quality of sleep and health responses. Subjects at the higher noise sites reported sleeping less well (spontaneous sleep responses not mentioning trafftc noise) than subjects at the quieter sites (F = 9.28, df = l/597, p < O-01). Also (as indicated in Figure 4) the low SES subjects slept less well than high SES subjects (F = 14.74, df = l/597,
-,
Figure 2. Annoyance versus noise level. High SES: ---, large; -, medium; -.-.-, small.
large; ----,
medium; -----,
small. Low SES:
HUMAN
RESPONSES TO TRAFFIC
415
NOISE, PART III
TABLE 3 Summary of the variance explained Traffic noise level W::b4) Annoyance How often annoyed Interference Spontaneous sleep Elicited sleep Speech interference Recreational activities Perceived harm Psychological interference Perceived necessity
SES
(%) for significant eficts
Noise by Noise by SES size
Size 0.5*
22.0*** 12.6*** 11.6*** 2.4-4 14** 5.6*** 12*0*** 1.2** 10.7*** 6.9*** 1.3* 8.5*** 2.(-J***
SES by size
Noise by SES by Size
0.5*
o-7*
1.7***
1.1***
5.9***
().7* 1.5*** 1.0** 1.3**
2.1***
2.g***
*p < 005, ** p < 001, ***p < 0401, significance levels when variable was last variable added.
only tragic noise level significantly affected the subjects’ elicited p < O@Ol). However, reports that trafftc noise interferred with their sleep (F = 35.56, df = l/598, p < OGOl). Reported psychological interference (see Figure 5) increased with traffic noise level (F = 29.54, df = l/597, p < OJIOl). A significant size by t&tic noise effect (F = 8.42, df = l/597, p c 0.01) showed that the size of community effect occurred only at the higher traffic noise levels. Speech interference (see Figure 6) increased with noise level (F = 7.63, df = l/594, p
I 50
I 60
I 70
L24 q
Figure 3. Overall interference versus noise level. Key as Figure 2.
416
S. BRADLEY AND B. A. JONAH
Figure 4. Spontaneous sleep interference versus noise leGeel.Key as Figure 2.
among the high SES subjects and increased with community size (F = 5.41, df = l/594, p < 0.02). A significant SES by noise interaction effect indicated that the SES effect was greatest at the higher noise levels (F = 1l-62, df = l/594, p < O-001). A size by noise effect (F = 6-56, df = l/594, p < O-02) indicated that at the higher noise levels subjects living in the smaller towns reported less speech disturbance than the subjects living in the medium or large communities. Interference with recreational activities increased with traffic noise level (F = 23.02, df = l/596, p < O-001). The significant size by noise effect on this variable (F = 25.18, I
I
I
I
50
60
70
60
Figure 5. Psychological interference versus noise level. Key as Figure 2.
RESPONSES TO TRAFFIC NOISE, PART III
417
Figure 6. Speech interference versus noise level. Key as Figure 2.
CZ!! = l/596, p c OJIOl) which is shown in Figure 7 revealed that the size of community effect was greatest at the higher noise levels. A size by SES effect (F = 14.65, df = l/596, p < O@Ol)revealed that status differences varied with community size. Perceived physical and mental harm from t&k noise increased with traffic noise level (F = 21.65, df = l/596, p < O.OOl),and decreased as SES increased (F = 8.65, df- l/596, p < O-01). Once again, a significant size by noise interaction (see Figure 8) revealed that the size effect occurred mainly at the higher noise levels (F = 6.80, df = l/596, p < 0.01).
Figure 7. Recreational activity interference versus noise level. Key as Figure 2.
418
J. S. BRADLEY AND B. A. JONAH
40
50
60
70
6c
Figure 8. Perceived harm versus noise level. Key as Figure 2.
Finally, perceived devaluation of property by traffic noise (i.e., economic interference) increased with traffic noise level (F = 55-71, df = l/484, e < OXKll).There was a significant size by traflic noise level interaction effect (F = 7-94, df = l/484, p < O-01)which indicated that at higher traffic noise levels there were differences between community size groups such that small communities perceived the least property devaluation. 3.3. DISCUSSION OF ELICITED RESPONSES As in the previous studies, traffic noise accounted for the major part of the explained variance in the elicited human responses. However, size of community, SES and several interaction terms significantly influenced responses. In particular, there was a quite consistent community size by traffic noise level effect. On many responses, this interaction effect appeared to be created by lower response scores at higher noise levels for small town subjects. In further examining these effects, the perceived necessity of vehicles was investigated. It was found that the perceived necessity of vehicles varied with traffic noise level (F = 25.79, df = l/595, p < OGOl), and with the community size by traffic noise level interaction variable (F = 17.94, df = l/595, p < 0X)01). It was thought that differences in the perceived necessity of vehicles could have caused a number of the observed effects as residents in more isolated small towns, with little public transport, perceived vehicles to be more necessary. When regression analyses were performed, with first tratlk noise level and second perceived necessity entered before the other predictors, a number of effects were no longer significant. In particular, the community size by traffic noise level effects were no longer significant on speech interference and annoyance. Annoyance with aircraft noise produced the same effects as perceived necessity of vehicles as well as reducing the SES effect on perceived harm so that it was no longer significant. When both perceived necessity of vehicles and annoyance with aircraft noise were entered before the other predictors, several additional effects were no longer significant. These were the size by
HUMAN
RESPONSES TO TRAFFIC NOISE, PART III
419
traffic noise level effects on psychological interference, perceived harm, and on overall interference. These results thus suggested that the perceived necessity of vehicles and the annoyance with aircraft noise could have acted as mediators and contributed to a number of significant effects, particularly those related to community size. It is quite clear that both the perceived necessity of vehicles and the annoyance with aircraft noise could be expected to depend on community size. (Toronto had a major international airport, London an intermediate sized airport, and the small towns had no airports.) An alternative explanation was found for the significant effects on annoyance response scores. It was observed that the logarithm of the vehicle flow rate correlated quite highly with the size by noise level interaction variable. When the logarithm of the vehicle flow rate was entered into the regression after traffic noise level but before the other predictors the size and size by traffic noise level effects were no longer significant on annoyance responses. This result would support the suggestion that these effects on annoyance were related to differences in the logarithm of the vehicle flow rate. Status effects on spontaneous sleep interference and on speech interference responses remained after these further analyses. It may be that differences of activities between the two status groups caused more interference with the activities of the low SES subjects. 3.4.
SPONTANEOUS
RESPONSES TO TRAFFIC NOISE
The initial items of the questionnaire concerning neighbourhood likes and dislikes allowed the determination of spontaneously mentioned responses to traffic noise. Some spontaneous responses were considered in aggregate form by plotting the percentage of subjects giving a particular response versus the site average traffic noise level (Lzi). When considered in aggregate form, the separate regression lines for each of the six combinations of status and community size were derived from only four data points. Significant results were thus difficult to obtain. A regression analysis showed that reported satisfaction with the neighbourhood decreased with increasing traffic noise level (F = 2244, df = l/484, p < OJXU). There was also a positive size by SES effect (F = 11.80, df = l/484, p < O@Ol) indicating a status related effect varying with community size. Although subjects in the large community had expressed greater negative responses to traflic noise, they were unexpectedly more satisfied with their neighbourhood. Apparently satisfaction with one’s neighbourhood was influenced by factors other than traffic noise. Traffic noise level was not related to the number of hours spent outside engaged in leisure activities, but this response increased with community size (F = 105.95, df = l/485, p < 0~001). It appears that subjects in the large community may have expressed stronger negative responses to traffic noise partly because they were outside more’ often. The percentage of subjects at each site who reported that traffic noise was the reason that they were not outside more often was generally quite small but increased with traffic noise level. At 65 dB(A) (J_$), up to 8% of the population gave traffic noise as the reason they were not outside more often with a tendency for the highest responses to occur for the large community residents. Those subjects who went outside found it increasingly less pleasant outside as traffic noise level increased (F = 4.92, df = l/498, p < 0.03). In addition, high SES subjects rated being outside as more pleasant than low SES subjects (F = 10.10, df = l/484, p < 0.01). The percentage of subjects who liked the quietness of their neighbourhood decreased with increasing traflic noise level as shown in Figure 9. At lower noise levels, there is a tendency for the percentage of respondents to increase with community size. As shown in Figure 10, the percentage of people who gave traffic noise as their major dislike about
420
J. S. BRADLEY AND B. A. JONAH
60 2 E
50
a. 40
30
0 40
50
60
70
80
Lz4 w (dE(A)) Figure 9. Percentage of subjects reporting they liked the quietness of the neighbourhood versus noise level. Small: 0, A, low SES; 0, B, high SES. Medium: A., C, low SES; A, D, high SES. Large: x, E, low SES; +, D, high SES.
60
-
E g!
50-
$ 40
-
Lz4 ?? o (dt3tA))
Figure 10. Percentage of subjects reporting traffic noise was the major neighbourhood level. Key as Figure 9.
dislike versus noise
HUMAN
RESPONSES
01
TO TRAFFIC
I 50
40
I 60 Lz4 w
Figure 11. Percentage level. Key as Figure 9.
of subjects
reporting
NOISE,
421
PART III
I
I 70
80
(dB(A) 1
traffic noise was an annoying
neighbourhood
noise versus noise
their neighbourhood increased as a function of traffic noise level, and decreasing community size. The percentage of subjects indicating that traffic noise was an annoying neighbourhood noise increased as a function of traffic noise level, but was not simply related to SES or community size as shown in Figure 11. As traffic noise level increased, the percentage of subjects who wanted to move also increased, and tended to be greatest for low SES subjects. The percentage of people wanting to move because of traffic noise also increased with traffic noise level as shown in Figure 12. Contrary to expectation, as traffic noise level increased, windows were left open for a greater percentage of the time (F = 5.91, df = l/484, p < 0.02) and there was a significant
“(I30
E zk
20
a
IO
0 40
50
60
L,,
Figure 12. Percentage of subjects versus noise level. Key as Figure 9.
reporting
60
70
(dB(A ))
traffic noise was the reason
windows
were not open more often
422
J. S. BRADLEY
AND B. A. JONAH
SES by community size effect (F = 16.87, df = l/484, p < O@Ol)indicating a community size effect dependent on status. Decreased status and community size increased the likelihood that windows were left open. As traffic noise level increased, the percentage of subjects giving traffic noise as the reason for not opening windows more frequently increased as shown in Figure 12. The spontaneous responses clearly supported the earlier conclusion from the elicited responses that traffic noise level was the major predictor of adverse responses. The effects of status and community size observed on many elicited responses were not so clearly evident in the spontaneous responses. For elicited responses, a consistent size by noise level effect was found that indicated disturbance was greatest in the larger communities at the higher noise levels. Some spontaneous responses conflicted with this result. For example, subjects in small towns were less likely to like the quietness of their neighbourhood and more likely to give traffic noise as their major neighbourhood dislike. The effect of SES was not as clearly defined as with the elicited response data. However, SES effects were found on particular forms of activity interference but not on overall annoyance responses. Perhaps one should expect SES effects only on particular responses that concern a status related activity. Where SES effects were found, they did parallel those for the elicited responses and suggested low SES subjects were more disturbed. Thus, low SES subjects were less satisfied with their neighbourhood, found it less pleasant outside and more frequently wanted to move.
4. CONCLUSIONS
It was found that LlO, L,, and L,, values were essentially equally accurate predictors of responses. All three of these measures for the 24 hour period explained 22% or more of the variance in individual annoyance responses. This is more than double the variance explained in previous traffic noise studies. Vehicle flow measures were found to be successful predictors both independently and in combination with L$’ values. The importance of vehicle flow measures as predictors of responses in addition to L.2: values appeared to vary with community size. Traffic noise levels accounted for the largest portion of the explained variance in the major elicited responses. There were also significant SES, community size, size by noise level, SES by noise level and SES by size effects. Both the perceived necessity of vehicles and annoyance with aircraft noise were thought to contribute to several of these size related effects. Thus, the overall lower negative responses of small town residents may have been due to their greater dependence on vehicles and their very small exposure to aircraft noise. In addition, differences in the logarithm of the total vehicle flow rate were also capable of explaining the observed effects on annoyance. This relates to the results in Part I [l] showing that increased awareness of the vehicle flow rate appeared to increase annoyance for apartment dwellers. Spontaneously mentioned responses to traffic noise also indicated that traflic noise level was the major determinant of the measured responses and hence validated the specifically elicited response scores. In some cases, spontaneous responses indicated some conflicts with respect to the effect of community size. While elicited negative response scores increased with increasing community size, in some cases spontaneous negative responses were greatest in the smaller communities. The results confirm that responses to traffic noise do vary with community size and that it is unwise to extrapolate the results of studies in large cities to small towns and semirural areas. These measured differences were found to relate to variables that would be
HUMAN RESPONSESTO TRAFFIC NOISE,PART III
423
expected to be inherently related to community size. For example, people in small towns can generally be expected to perceive vehicles as being more necessary, and also to encounter less aircraft noise and lower vehicle flow rates. In many cases, it seemed that the largest differences occurred between the small communities and the other sizes of communities which were more similar. Further studies could be contemplated in which one would consider other sizes of small communities including residents of rural areas. In further analyses one might also consider the minor conflicts between the effects of community sjze on elicited responses and some spontaneous responses. It may be that small town residents make strong negative spontaneous responses, but that their realization of the importance of vehicles tempers their more considered elicited responses. The importance of vehicle flow rate somewhat confirms the results of Part I [l]. In further research one might more fully investigate this area in more detail with the view 01 considering various vehicle flow measures as predictors of responses.
ACKNOWLEDGMENT This work was sponsored by the Canadian Ministry of Transport. The helpful comments and advice of Dr R. C. Gardner, throughout gratefully acknowledged.
this work
are
REFERENCES 1. J. S. BRADLEY and B. A. JONAH1979 Journal of Sound and Vibration 66,589-604. The effects of site selected variables on human responses to traffic noise, Part I : Type of housing by traffic noise level. 2. J.S.BRADLEY and B. A. JONAH1979 Journal of Sound and Vibration 67,395407. The effects of site selected variables on human responses to traffic noise, Part II: Road type by socio-economic status by traffic noise level. 3. A. JENKINS,J. PAHL, F. CARROLL,N. ALYASSINIand S. HELLER1974 Research Center Report, Institute of Safety and Systems Management, University of Southern California. Community response to freeway noise in Los Angeles County. 4. ANON 1971 Automobile Manufacturers Association Report 2112. A survey of annoyance from motor vehicle noise. 5. E. RELSTER1975 Trafic Noise Annoyance. University of Copenhagen. 6. J. G. VASKOR,S. M. DICKINSONand J. S. BRADLEY1979 Applied Acoustics 12,11 l-124. The effect of sampling on the statistical descriptors of traffic noise.
THE EFFECTS RESPONSES
OF SITE SELECTED
TO TRAFFIC
BY SOCIO-ECONOMIC
VARIABLES
ON HUMAN
NOISE, PART III: COMMUNITY STATUS BY TRAFFIC
SIZE
NOISE LEVEL
J.S. BRADLEY Faculty of Engineering AND
B.A. JONAH-~ Department of Psychology, The University of Western Ontario, London, Canada (Received 31 August 1978, and in revisedform 18 July 1979)
This paper reports the results of the third part of a field study of human responses to traffic noise. The influence of traffic noise level, community size, and socioeconomic status were investigated in a controlled manner determined by subject selection procedures. Human responses were obtained from interviewer administered questionnaires, and were as spatially and temporally coincident with the noise measurements as possible. Noise measurements were obtained from several days of rapidly sampled digital recordings. Tragic noise level was the major predictor of the intensity of elicited responses. A number of other signilicant effects were observed largely related to community size. These were explained as due to parallel variations in the perceived necessity of vehicles, the annoyance with aircraft noise, and the logarithm of the vehicle flow rate. The results did demonstrate effects related to community size, and thus it is unwise to extrapolate the results of large city noise studies to small communities and rural areas.
1. INTRODUCTION
The responses of subjects to traffic noise have been studied as a function of controlled site variables. The combined effects of traffic noise levels and housing type [l] (Part I) and the combined effects of traffic noise levels, road type, and socio-economic status [2] (Part II) have been previously reported. The present paper is concerned with the combined effects of traffic noise levels, community size and socio-economic status. Other site variables such as road type and housing type were kept constant. Thus, all 600 subjects in this study resided in single unit housing on regular roads. Twenty-four sites were measured consisting of all combinations of four traffic noise levels, two levels of SES (socioeconomic status), and three levels of community size including communities with populations from 7000 to 2 000 000. Most major studies of traffic noise have been carried out in very large urban areas. It was not at all clear whether similar results would be found in smaller communities, t Present address: Road and Motor Vehicle Traffic Safety, Transport Canada, Ottawa KiA ON5, Canada. 409 0022-460X/79/230409
+ 15 $02.00/O
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1979 Academic Press Inc. (London)
Limited
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J. S. BRADLEY AND B. A. JONAH
sometimes approaching rural conditions. It was thought that social differences might occur between community sizes as well as differences in the overall noise exposures. Previous research had not clearly determined the influence of status on human responses to traffic noise [3-53, and it was thus thought valuable to include this as a third controlled site variable. Both spontaneous and elicited responses were considered and composite response scales were constructed and examined in terms of the intensity of negative responses as in the previous two parts of the study [l, 21. These responses were investigated as a function of traffic noise level, SES (socio-economic status), community size and the various interactions among these variables.
2. METHOD
2.1. SITE SELECTION AND MEASUREMENT PROCEDURES Twenty-five subjects were interviewed from the fifty or more dwellings at each of twenty-four sites chosen according to the procedures described in Part I [l]. The twentyfour sites consisted of all possible combinations of four levels of traffic noise, two levels of SES, and three sizes of community. Traffic noise levels ranged from the quietest to the noisiest possible within the other constraints of site choice and where traffic noise was the only major source of environmental noise. Three sizes of communities were obtained by choosing sites in the following communities: (1) large, metropolitan Toronto (population 2 000 000); (2) medium, London, Ontario (population 240 000); and (3) small, Tillsonburg and Ingersoll, Ontario (populations 7000). Sites were selected so that high status sites were clearly higher in status than the low status sites. The large community sites were common to the study of road type reported in Part II [2]. A-weighted noise levels were digitally recorded once per second for four days in the medium and small sized communities and once per two seconds for six days in the large community. In both cases, the recordings included a Saturday and a Sunday. After the initial measurements in small and medium sized communities, the revised sampling schedule was found to be a slightly better optimization [6]. There was approximately one noise measurement position per ten subjects. A number of standard statistical noise measures were considered for each of the day (07:0&22:00 hours), the night (2290-0790 hours) and the twenty-four hour periods for each of: a weekday average (for either 2 or 4 weekdays), a Saturday, a Sunday, and an overall day type average. All noise measures were individually extrapolated to be representative of noise levels at the facade of each dwelling. Other noise and human response measurement procedures were described in Part I [l]. 2.2. FORMATION OF COMPOSITE RESPONSE SCALES A principal axis factor analysis was performed on eighty of the survey responsest and the live extracted factors were rotated by varimax. These five factors accounted for 71% of the variance in the eighty response items. The first factor included items that were measures of directly elicited annoyance to traffic noise. The remaining factors were labeled quality of sleep and health, unusual neighbourhood noise annoyance, interference with activities, and perceived necessity of vehicles respectively. Composite response scales were t A maximum of eighty items was a result of the limitations of available computer programmes.
HUMAN
RESPONSES
TO TRAFFIC
NOISE,
PART
411
III
constructed by averaging the responses on those items which the factor analysis indicated belonged together and were labelled as above. In addition, convenient sub-divisions of the interference scale were created by summing related items to measure interference with specific activities such as sleep and speech interference. For the annoyance scale item-total correlations ranged from 0.49 to 0.83 and the KR20 coefficient (Cronback coefficient Alpha) was O-95. For the interference scale, item-total correlations ranged from 0.48 to 0.92 and the KR20 coefficient was O-95.Thus, these two main response scales were quite internally consistent.
3. RESULTS 3.1.
NOISE AND VEHICLE
FLOW
MEASURES
AS PREDICTORS
OF RESPONSES
It was desired to investigate how responses depended on the various site variables and their interactions as well as to determine the relative merits of several noise measures as descriptors of the noise. These two aspects were considered separately by first investigating the prediction accuracy of a number of standard statistical noise measures. Noise measures were evaluated with respect to three main response scale scores: annoyance, overall interference, and elicited sleep interference. Subsequent, more detailed analyses of responses were carried out with respect to a single generally acceptable noise measure. Figure 1 shows plots of the correlation coefficients for all three responses versus noise I
I
I
I
I
I
I
I
x-x Interference
x-x Elicited sleep interference
Noise msasures
Figure
1. Correlations
of responses
with noise measures.
O-0,
Day; o-0,
night;
x-x,
24 hour.
J. S. BRADLEY AND B. A. JONAH
412
TABLE 1
Correlations between response scores and day, night and 24 hour noise measures (N = 600) Response
Period
Annoyance
Day Night 24 Hours
0.435 0.455 0444
0.475 0.464 0.476
0.43 1 0.469
0.453 0.348 0.402
0.468 0.471 0.469
0.242 0.388 0.368
0.415 0.437 0.43 1
0.472
0.372 0.419 0.415
Overall interference
Day Night 24 Hours
0.337 0.340 0.340
0.344 0.342 0.344
0.350 0.328 0.341
0.341 0.294 0.286
0.341 0.337 0.341
0.173 0.273 0.271
0.308 0.316 0.322
0.341
0.254 0.292 0.303
Elicited sleep interference
Day Night 24 Hours
0.218 0.220 0.218
0.236 0.234 0.235
0.247 0.219 0.246
0.241 0.186 0.267
0.237 0.235 0.237
0.083 0.186 0.140
0.196 0.213 0.191
0.237
0.165 0.207 0.153
0.479
measures
for different time periods. All correlation coefficients for the three responses and noise measures for the day, night and 24 hour periods are given in Table 1. The vertical axis of Figure 1 is quite expanded and so many small differences between correlation coefficients are not statistically significant. Figure 1 and Table 1 indicate patterns of correlation coefficients roughly similar to those observed in Parts I and II of the study [l, 21. In particular, night time L,, values were the least satisfactory predictors of responses probably because L,, at night was least representative of local traffic’ noise and more representative of general background noises. One exception noted here was that L, values were also noticeably inferior predictors of annoyance, especially during the day time. As in the previous studies, LNp (the noise pollution level) produced correlations inferior to the corresponding L,, values, and LDN (the day-night sound level) was no more than marginally superior to Lzi. The three most commonly used noise measures, LIO, LSO and L,, were again seen to be approximately equally accurate predictors of responses. The 24 hour values of L,,, L,, and L,, explained 22-23x of the variance in annoyance responses for the 600 subjects considered.? This indicated more accurate and more reliable relationships were achieved than in previous traffic noise research where typically 10% or less of the individual response variance has been explained. Correlations between responses and noise measures were performed separately for each community size. Even though sites were chosen to be of equivalent traffic noise levels independent of community size, it was thought that other differences in the noise exposure might exist. For example, background noise levels might be more dominant in larger communities. The magnitude of the correlation coefficients varied with community size and results for small towns were a little irregular, but results were largely the same as shown in Figure 1 for all 600 subjects. Again, L,, at night was the least successful predictor. However, LgO at night became increasingly relatively less successful with increasing community size. This probably indicates that in larger communities L,, at night is less closely related to local traffic noise, and more a measure of more distant sources. Combinations of noise measures from day and night time periods were tested by multiple regression analysis to assess if more accurate compound noise predictors could be obtained. Scores on the three response scales were regressed onto combinations of noise measures from the day, night and 24 hour periods. The compound noise measures t The choice of N = 600 was dictated the factors influencing the large variance
by the obvious preference to consider individual subject responses in these responses. This approach was justified in Part I [l].
and
HUMAN RESPONSES TO TRAFFIC NOISE, PART III
413
formed by the multiple regression analyses were evaluated in terms of the variance explained in excess of that for L,24 . Practically none of the combinations increased the variance explained by 1% or more. For annoyance, only a combination of Lzi, Lf” and Lzq increased the variance explained by a little over lx, or approximately the same as the day time L,, value. The importance of vehicle flow measures as predictors of responses was investigated in combinations both with and without Lzi values. Multiple linear regression analyses were performed by regressing the response scores onto combinations of the predictors. First, regressions were performed with L$’ forced to be chosen first and the various vehicle flow measures then selected according to which explained the largest portion of the remaining unexplained variance at each step. The second set of regressions were similar but with Lgi values omitted, and with the order of the vehicle flow variables chosen according to the amount of the remaining variance that they explained. The results of these multiple regressions for all 600 subjects are given in Table 2 in terms of the multiple correlation coefficient after each step. It is seen from this table that essentially the same prediction accuracy could be achieved by using vehicle flow measures alone as with Lzi and vehicle flow measures. Adding vehicle flow measures to Lzt values increased the variance explained by up to 2.6% in the case of annoyance responses, such that the combination of physical predictors explained essentially 25% of the variance in annoyance responses. Similar multiple regression analyses were performed separately for each community size. It was found that the variance explained was increased most for the large community. Thus, in the large community vehicle flow parameters added most to the prediction accuracy of Lz: values. This may have been partially related to the higher vehicle flow rates at the higher noise level sites in the large community. The results were again interpreted as providing sufficient justification to use the one simple noise measure Lzi in further analyses of responses. It was seen that only the addition of vehicle flow measures provided clearly increased prediction accuracy above that for Lzi. The importance of vehicle flow measures appeared to vary with community size. The simplification of considering only one noise measure led to a beneficial concentration
on the influence
of the site variables
and their interactions
on the responses.
TABLE 2
Increasing
multiple
correlation
coeflcients
as succeeding
noise
and vehicle flow measures
were added (N = 600) Annoyance
Interference
L
O-469
L
lo;( WH)
0.492
lo&m)
VPH NT PTK
O-496 (ns) O-496 (ns) 0.496 (ns)
NT PTK VPH
log( VPH)
0.487 0.492 0.494 (ns)
1ogW) VPH NT
log( VPH) NT VPH PTK
Elicited sleep
0.341 0.348
of heavy p < 0.05.
log( VPH)
0.245 (ns) 0.253 (ns)
log(W
0~256(ns)
log( VPH)
0.231 0.241 (ns) O-243 (ns) 0.245 (ns) 0.245 (ns)
NT
0.336 0.339 (ns) 0.344 (ns) 0.351 (ns)
VPH = overall vehicle flow rate per hour, NT = number heavy trucks. (ns) indicates term did not add significantly to the prediction,
0.237
Le*
(ns) 0.350 (ns) 0.352 (ns) O-357 (ns) 0.357 (ns)
NT
log(NT) VPH PTK trucks
per hour,
PTK = percentage
of
414
J. S. BRADLEY AND B. A. JONAH
3.2. ELICITED RESPONSES TO TRWFIC NOISE The composite scale scores were considered by multiple regression analyses, with the variables entered into the equation in a stepwise manner according to the portion of the unexplained variance for which they accounted. The variables were traffic noise level (L$), SES (socio-economic status), community size, size by SES, size by noise level, SES by noise level, and SES by size by noise level. Annoyance scores increased with traflk noise level (F = 6.25, df = l/596, p c O-01),and also with increasing community size (F = 4.23, d?= l/596, p < O-04). In addition, a significant community size by traffic noise level interaction (F = 5.10, df = l/596, p -C0.03) indicated that at the higher noise levels subjects living in the small towns reported less annoyance than the subjects in the medium and large sized communities. This is illustrated in Figure 2 which shows separate regression lines for each of the six combinations of community size and SES. Subjects living at noisier sites were more often annoyed than subjects at quieter sites (F = 86.13, df = l/598, p c ONll). Table 3 summarizes the variance explained by each predictor variable for all the major elicited responses and also gives the associated significance levels. Overall interference scores increased with traffic noise (F = 55.88, df = l/596, p = OGOl). A significant size of community by traffic noise effect (F = 8.09, df = l/596, p < 0.01) revealed that the size of community effect occurred mostly at the higher levels of traflic noise, and a noise level by SES effect (F = 11.95, df = l/596, p < OGOl) revealed that low status subjects indicated increased interference at higher noise levels (see Figure 3). SES (F = 27.03, df = l/484, p < OXlOl) and size by traffic noise level (F = 5.91, df = l/484, p < O-02)influenced quality of sleep and health responses. Subjects at the higher noise sites reported sleeping less well (spontaneous sleep responses not mentioning trafftc noise) than subjects at the quieter sites (F = 9.28, df = l/597, p < O-01). Also (as indicated in Figure 4) the low SES subjects slept less well than high SES subjects (F = 14.74, df = l/597,
-,
Figure 2. Annoyance versus noise level. High SES: ---, large; -, medium; -.-.-, small.
large; ----,
medium; -----,
small. Low SES:
HUMAN
RESPONSES TO TRAFFIC
415
NOISE, PART III
TABLE 3 Summary of the variance explained Traffic noise level W::b4) Annoyance How often annoyed Interference Spontaneous sleep Elicited sleep Speech interference Recreational activities Perceived harm Psychological interference Perceived necessity
SES
(%) for significant eficts
Noise by Noise by SES size
Size 0.5*
22.0*** 12.6*** 11.6*** 2.4-4 14** 5.6*** 12*0*** 1.2** 10.7*** 6.9*** 1.3* 8.5*** 2.(-J***
SES by size
Noise by SES by Size
0.5*
o-7*
1.7***
1.1***
5.9***
().7* 1.5*** 1.0** 1.3**
2.1***
2.g***
*p < 005, ** p < 001, ***p < 0401, significance levels when variable was last variable added.
only tragic noise level significantly affected the subjects’ elicited p < O@Ol). However, reports that trafftc noise interferred with their sleep (F = 35.56, df = l/598, p < OGOl). Reported psychological interference (see Figure 5) increased with traffic noise level (F = 29.54, df = l/597, p < OJIOl). A significant size by t&tic noise effect (F = 8.42, df = l/597, p c 0.01) showed that the size of community effect occurred only at the higher traffic noise levels. Speech interference (see Figure 6) increased with noise level (F = 7.63, df = l/594, p
I 50
I 60
I 70
L24 q
Figure 3. Overall interference versus noise level. Key as Figure 2.
416
S. BRADLEY AND B. A. JONAH
Figure 4. Spontaneous sleep interference versus noise leGeel.Key as Figure 2.
among the high SES subjects and increased with community size (F = 5.41, df = l/594, p < 0.02). A significant SES by noise interaction effect indicated that the SES effect was greatest at the higher noise levels (F = 1l-62, df = l/594, p < O-001). A size by noise effect (F = 6-56, df = l/594, p < O-02) indicated that at the higher noise levels subjects living in the smaller towns reported less speech disturbance than the subjects living in the medium or large communities. Interference with recreational activities increased with traffic noise level (F = 23.02, df = l/596, p < O-001). The significant size by noise effect on this variable (F = 25.18, I
I
I
I
50
60
70
60
Figure 5. Psychological interference versus noise level. Key as Figure 2.
RESPONSES TO TRAFFIC NOISE, PART III
417
Figure 6. Speech interference versus noise level. Key as Figure 2.
CZ!! = l/596, p c OJIOl) which is shown in Figure 7 revealed that the size of community effect was greatest at the higher noise levels. A size by SES effect (F = 14.65, df = l/596, p < O@Ol)revealed that status differences varied with community size. Perceived physical and mental harm from t&k noise increased with traffic noise level (F = 21.65, df = l/596, p < O.OOl),and decreased as SES increased (F = 8.65, df- l/596, p < O-01). Once again, a significant size by noise interaction (see Figure 8) revealed that the size effect occurred mainly at the higher noise levels (F = 6.80, df = l/596, p < 0.01).
Figure 7. Recreational activity interference versus noise level. Key as Figure 2.
418
J. S. BRADLEY AND B. A. JONAH
40
50
60
70
6c
Figure 8. Perceived harm versus noise level. Key as Figure 2.
Finally, perceived devaluation of property by traffic noise (i.e., economic interference) increased with traffic noise level (F = 55-71, df = l/484, e < OXKll).There was a significant size by traflic noise level interaction effect (F = 7-94, df = l/484, p < O-01)which indicated that at higher traffic noise levels there were differences between community size groups such that small communities perceived the least property devaluation. 3.3. DISCUSSION OF ELICITED RESPONSES As in the previous studies, traffic noise accounted for the major part of the explained variance in the elicited human responses. However, size of community, SES and several interaction terms significantly influenced responses. In particular, there was a quite consistent community size by traffic noise level effect. On many responses, this interaction effect appeared to be created by lower response scores at higher noise levels for small town subjects. In further examining these effects, the perceived necessity of vehicles was investigated. It was found that the perceived necessity of vehicles varied with traffic noise level (F = 25.79, df = l/595, p < OGOl), and with the community size by traffic noise level interaction variable (F = 17.94, df = l/595, p < 0X)01). It was thought that differences in the perceived necessity of vehicles could have caused a number of the observed effects as residents in more isolated small towns, with little public transport, perceived vehicles to be more necessary. When regression analyses were performed, with first tratlk noise level and second perceived necessity entered before the other predictors, a number of effects were no longer significant. In particular, the community size by traffic noise level effects were no longer significant on speech interference and annoyance. Annoyance with aircraft noise produced the same effects as perceived necessity of vehicles as well as reducing the SES effect on perceived harm so that it was no longer significant. When both perceived necessity of vehicles and annoyance with aircraft noise were entered before the other predictors, several additional effects were no longer significant. These were the size by
HUMAN
RESPONSES TO TRAFFIC NOISE, PART III
419
traffic noise level effects on psychological interference, perceived harm, and on overall interference. These results thus suggested that the perceived necessity of vehicles and the annoyance with aircraft noise could have acted as mediators and contributed to a number of significant effects, particularly those related to community size. It is quite clear that both the perceived necessity of vehicles and the annoyance with aircraft noise could be expected to depend on community size. (Toronto had a major international airport, London an intermediate sized airport, and the small towns had no airports.) An alternative explanation was found for the significant effects on annoyance response scores. It was observed that the logarithm of the vehicle flow rate correlated quite highly with the size by noise level interaction variable. When the logarithm of the vehicle flow rate was entered into the regression after traffic noise level but before the other predictors the size and size by traffic noise level effects were no longer significant on annoyance responses. This result would support the suggestion that these effects on annoyance were related to differences in the logarithm of the vehicle flow rate. Status effects on spontaneous sleep interference and on speech interference responses remained after these further analyses. It may be that differences of activities between the two status groups caused more interference with the activities of the low SES subjects. 3.4.
SPONTANEOUS
RESPONSES TO TRAFFIC NOISE
The initial items of the questionnaire concerning neighbourhood likes and dislikes allowed the determination of spontaneously mentioned responses to traffic noise. Some spontaneous responses were considered in aggregate form by plotting the percentage of subjects giving a particular response versus the site average traffic noise level (Lzi). When considered in aggregate form, the separate regression lines for each of the six combinations of status and community size were derived from only four data points. Significant results were thus difficult to obtain. A regression analysis showed that reported satisfaction with the neighbourhood decreased with increasing traffic noise level (F = 2244, df = l/484, p < OJXU). There was also a positive size by SES effect (F = 11.80, df = l/484, p < O@Ol) indicating a status related effect varying with community size. Although subjects in the large community had expressed greater negative responses to traflic noise, they were unexpectedly more satisfied with their neighbourhood. Apparently satisfaction with one’s neighbourhood was influenced by factors other than traffic noise. Traffic noise level was not related to the number of hours spent outside engaged in leisure activities, but this response increased with community size (F = 105.95, df = l/485, p < 0~001). It appears that subjects in the large community may have expressed stronger negative responses to traffic noise partly because they were outside more’ often. The percentage of subjects at each site who reported that traffic noise was the reason that they were not outside more often was generally quite small but increased with traffic noise level. At 65 dB(A) (J_$), up to 8% of the population gave traffic noise as the reason they were not outside more often with a tendency for the highest responses to occur for the large community residents. Those subjects who went outside found it increasingly less pleasant outside as traffic noise level increased (F = 4.92, df = l/498, p < 0.03). In addition, high SES subjects rated being outside as more pleasant than low SES subjects (F = 10.10, df = l/484, p < 0.01). The percentage of subjects who liked the quietness of their neighbourhood decreased with increasing traflic noise level as shown in Figure 9. At lower noise levels, there is a tendency for the percentage of respondents to increase with community size. As shown in Figure 10, the percentage of people who gave traffic noise as their major dislike about
420
J. S. BRADLEY AND B. A. JONAH
60 2 E
50
a. 40
30
0 40
50
60
70
80
Lz4 w (dE(A)) Figure 9. Percentage of subjects reporting they liked the quietness of the neighbourhood versus noise level. Small: 0, A, low SES; 0, B, high SES. Medium: A., C, low SES; A, D, high SES. Large: x, E, low SES; +, D, high SES.
60
-
E g!
50-
$ 40
-
Lz4 ?? o (dt3tA))
Figure 10. Percentage of subjects reporting traffic noise was the major neighbourhood level. Key as Figure 9.
dislike versus noise
HUMAN
RESPONSES
01
TO TRAFFIC
I 50
40
I 60 Lz4 w
Figure 11. Percentage level. Key as Figure 9.
of subjects
reporting
NOISE,
421
PART III
I
I 70
80
(dB(A) 1
traffic noise was an annoying
neighbourhood
noise versus noise
their neighbourhood increased as a function of traffic noise level, and decreasing community size. The percentage of subjects indicating that traffic noise was an annoying neighbourhood noise increased as a function of traffic noise level, but was not simply related to SES or community size as shown in Figure 11. As traffic noise level increased, the percentage of subjects who wanted to move also increased, and tended to be greatest for low SES subjects. The percentage of people wanting to move because of traffic noise also increased with traffic noise level as shown in Figure 12. Contrary to expectation, as traffic noise level increased, windows were left open for a greater percentage of the time (F = 5.91, df = l/484, p < 0.02) and there was a significant
“(I30
E zk
20
a
IO
0 40
50
60
L,,
Figure 12. Percentage of subjects versus noise level. Key as Figure 9.
reporting
60
70
(dB(A ))
traffic noise was the reason
windows
were not open more often
422
J. S. BRADLEY
AND B. A. JONAH
SES by community size effect (F = 16.87, df = l/484, p < O@Ol)indicating a community size effect dependent on status. Decreased status and community size increased the likelihood that windows were left open. As traffic noise level increased, the percentage of subjects giving traffic noise as the reason for not opening windows more frequently increased as shown in Figure 12. The spontaneous responses clearly supported the earlier conclusion from the elicited responses that traffic noise level was the major predictor of adverse responses. The effects of status and community size observed on many elicited responses were not so clearly evident in the spontaneous responses. For elicited responses, a consistent size by noise level effect was found that indicated disturbance was greatest in the larger communities at the higher noise levels. Some spontaneous responses conflicted with this result. For example, subjects in small towns were less likely to like the quietness of their neighbourhood and more likely to give traffic noise as their major neighbourhood dislike. The effect of SES was not as clearly defined as with the elicited response data. However, SES effects were found on particular forms of activity interference but not on overall annoyance responses. Perhaps one should expect SES effects only on particular responses that concern a status related activity. Where SES effects were found, they did parallel those for the elicited responses and suggested low SES subjects were more disturbed. Thus, low SES subjects were less satisfied with their neighbourhood, found it less pleasant outside and more frequently wanted to move.
4. CONCLUSIONS
It was found that LlO, L,, and L,, values were essentially equally accurate predictors of responses. All three of these measures for the 24 hour period explained 22% or more of the variance in individual annoyance responses. This is more than double the variance explained in previous traffic noise studies. Vehicle flow measures were found to be successful predictors both independently and in combination with L$’ values. The importance of vehicle flow measures as predictors of responses in addition to L.2: values appeared to vary with community size. Traffic noise levels accounted for the largest portion of the explained variance in the major elicited responses. There were also significant SES, community size, size by noise level, SES by noise level and SES by size effects. Both the perceived necessity of vehicles and annoyance with aircraft noise were thought to contribute to several of these size related effects. Thus, the overall lower negative responses of small town residents may have been due to their greater dependence on vehicles and their very small exposure to aircraft noise. In addition, differences in the logarithm of the total vehicle flow rate were also capable of explaining the observed effects on annoyance. This relates to the results in Part I [l] showing that increased awareness of the vehicle flow rate appeared to increase annoyance for apartment dwellers. Spontaneously mentioned responses to traffic noise also indicated that traflic noise level was the major determinant of the measured responses and hence validated the specifically elicited response scores. In some cases, spontaneous responses indicated some conflicts with respect to the effect of community size. While elicited negative response scores increased with increasing community size, in some cases spontaneous negative responses were greatest in the smaller communities. The results confirm that responses to traffic noise do vary with community size and that it is unwise to extrapolate the results of studies in large cities to small towns and semirural areas. These measured differences were found to relate to variables that would be
HUMAN RESPONSESTO TRAFFIC NOISE,PART III
423
expected to be inherently related to community size. For example, people in small towns can generally be expected to perceive vehicles as being more necessary, and also to encounter less aircraft noise and lower vehicle flow rates. In many cases, it seemed that the largest differences occurred between the small communities and the other sizes of communities which were more similar. Further studies could be contemplated in which one would consider other sizes of small communities including residents of rural areas. In further analyses one might also consider the minor conflicts between the effects of community sjze on elicited responses and some spontaneous responses. It may be that small town residents make strong negative spontaneous responses, but that their realization of the importance of vehicles tempers their more considered elicited responses. The importance of vehicle flow rate somewhat confirms the results of Part I [l]. In further research one might more fully investigate this area in more detail with the view 01 considering various vehicle flow measures as predictors of responses.
ACKNOWLEDGMENT This work was sponsored by the Canadian Ministry of Transport. The helpful comments and advice of Dr R. C. Gardner, throughout gratefully acknowledged.
this work
are
REFERENCES 1. J. S. BRADLEY and B. A. JONAH1979 Journal of Sound and Vibration 66,589-604. The effects of site selected variables on human responses to traffic noise, Part I : Type of housing by traffic noise level. 2. J.S.BRADLEY and B. A. JONAH1979 Journal of Sound and Vibration 67,395407. The effects of site selected variables on human responses to traffic noise, Part II: Road type by socio-economic status by traffic noise level. 3. A. JENKINS,J. PAHL, F. CARROLL,N. ALYASSINIand S. HELLER1974 Research Center Report, Institute of Safety and Systems Management, University of Southern California. Community response to freeway noise in Los Angeles County. 4. ANON 1971 Automobile Manufacturers Association Report 2112. A survey of annoyance from motor vehicle noise. 5. E. RELSTER1975 Trafic Noise Annoyance. University of Copenhagen. 6. J. G. VASKOR,S. M. DICKINSONand J. S. BRADLEY1979 Applied Acoustics 12,11 l-124. The effect of sampling on the statistical descriptors of traffic noise.