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Reliability analysis of traffic noise estimates in Hong Kong
Transpn Res.-D, Vol. 3, No. 4, pp. 239±248, 1998 # 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain 1361-9209/98 $19.00+0.00
Pergamon PII: S1361-9209(98)00002-9
RELIABILITY ANALYSIS OF TRAFFIC NOISE ESTIMATES IN HONG KONG WILLIAM H. K. LAM* and M. L. TAM
Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (Received 26 June 1997; in revised form 2 February 1998) AbstractÐThis paper examines the reliability of trac noise measurement techniques and the noise estimates in Hong Kong. A simulation model making use of the Monte Carlo technique is devised to incorporate the uncertainty in trac noise estimates in Hong Kong. Trac ¯ow, trac speed and trac composition (in terms of percentage of heavy vehicles) are identi®ed as the key factors in¯uencing the generation of trac noise. The new equations for estimating the noise descriptors L10, Leq, L50 and L90 were calibrated on the basis of the Hong Kong survey results. The eects of the key factors on trac noise estimates can be assessed. The probability distribution for each of the key factors is derived with the use of the survey data. The variation on the key factors is due to the sampling error in the survey. The reliability of the trac noise estimates is obtained from the combined probability distributions of the key factors. The reliability analysis gives the trac noise estimates with a particular probability or vice versa. # 1998 Elsevier Science Ltd. All rights reserved Keywords: trac noise, Monte Carlo simulation, roads, Hong Kong 1. INTRODUCTION
Provision of transport infrastructure such as road bridges or ¯yovers in Hong Kong will undoubtedly contribute to the bene®t of the community at large. Nevertheless, due to the limited availability of land resources and ®nances, it is inevitable that in some of the schemes there will be some adverse environmental eects to those living in close proximity to them. For example, some of the existing road bridges in Hong Kong have caused many socially unacceptable eects such as intolerable noise, loss of privacy, visual intrusion, fumes and dirt, and vibration aecting those people unfortunate enough to have to continue to live and work close by. In order to assess properly the likely eects which transport infrastructure projects will have on people and the environment, it is required to develop a measure, combining the elements of speci®c techniques of environmental measurement already available (Ferrary, 1990), which will enable a reliable assessment of environmental impacts of transport proposals to be made in general circumstances. In the Hong Kong Second Comprehensive Transport Study (Hong Kong Transport Department and Wilbur Smith Associates, 1989), road trac noise was considered as the major environmental impact and chosen for environmental evaluation of all candidate highway projects. The most common trac noise descriptors are L10, Leq, L50 and L90 where LN represented the noise level exceeded for N% of the time period. L10 gives an indication of the top end of the level range although it can be substantially less than the occasional peak. L90 corresponds to the background noise level in the absence of nearby noise sources. Leq is the equivalent continuous sound level contains the same quantity of sound energy over a de®ned time period as the actual time varying sound level. The trac noise model currently used in Hong Kong is the one developed by U.K. Department of Transport (Department of Transport, Welsh Oce, 1988). The new equations for estimating the trac noise levels will be calibrated using the survey data in Hong Kong. The noise descriptor L10 will be applied for analysis because it is commonly used in the evaluation of highway projects. *Author for correspondence. Fax: 00 852 2334 6389; e-mail: [email protected]
239
240
W. H. K. Lam and M. L. Tam
This paper highlights in particular the impacts of trac noise variations on highway projects as well as the major underlying factors aecting the noise estimates. The trac noise prediction models (Department of Transport, Welsh Oce, 1988) developed in U.K. has been adopted in the Hong Kong Planning Standard and Guidelines as the preferred calculation method. Empirical formulae relating various local factors aecting generation and propagation of trac noise are implemented to assess trac noise in Hong Kong (Lam and Lai, 1994). However, if the eects of variations of those factors are not examined, the trac noise measurements may be over-estimated or under-estimated. With advanced mathematical techniques, it is possible to incorporate reliability analysis in trac noise estimates. The following four stage process is the proposed methodology for assessing the reliability of estimating trac noise: 1. identi®cation of the key factors behind the trac noise estimates; 2. estimation of how these key factors aect the noise estimates; 3. derivation of probability distributions for the key factors which specify the range and shape of the probable future outcome over which each factor is likely to vary; and 4. estimation of the combined probability of the outcome. Basically, the ®rst and second stages involve identi®cation of data required and calibration of the trac noise model. The third stage is concerned with the use of the survey data to derive the probability distribution for each of the key factors. The last stage makes use of the Monte Carlo simulation technique (Ross, 1991) to consider a number of possible outcomes of each key factor with their values selected at random form within their probability distributions. As a result, the reliability of the trac noise estimates can be obtained from the combined probability distributions of the key factors. The reliability analysis can be carried out to assess the probability of reaching speci®c trac noise levels in given time periods for the candidate highway project. The new approach would give a better insight on the sustainable impact of trac noise due to implementation of transport infrastructure. 2. FACTORS AFFECTING THE GENERATION OF TRAFFIC NOISE
There are many factors which in¯uence the generation of trac noise level under Hong Kong circumstances. For instance, on the supply side, trac ¯ow, speed, composition, frequency of stopping and starting, road gradient and surface. When considering the aected side, the factors like proximity to road, type of land use and time of day, also aect the generation of trac noise. In Hong Kong, the basic method for calculating the trac level is the CRTN model (Department of Transport, Welsh Oce, 1988), in which the following variables are investigated: 1. 2. 3. 4. 5.
trac ¯ow Q (veh/h), trac speed V (km/h), trac composition (in terms of percentage of heavy vehicles) P (%), road gradient G (%); and road surface.
Hence, in this paper, the same choice of the variables is adopted so as to assess the applicability of the CRTN model to Hong Kong. As a result, it can easily adjust the noise level that was estimated by the CRTN model in the past. It is impossible to separate the parameters completely, in particular trac speed and trac composition were found to interact and these are therefore considered together. When estimating trac noise levels for a road section it is important to ensure that compatible values of ¯ow, speed and composition are adopted. For noise prediction purposes, trac compositions are generally divided into two vehicles classes: 1. Light vehicle (<1525 kg unladen weight); and 2. Heavy vehicle (>1525 kg unladen weight). It is also recognized that trac noise levels are aected by the gradient of a road. The eect of gradient depends critically upon the percentage of heavy vehicles and the trac noise can be increased up to 12 dB(A) for a gradient of 1 in 8.
Reliability analysis of trac noise estimates in Hong Kong
241
Due to the interaction between the road and tyre, road surface texture aects directly the noise level generated by trac. The noise generated by vehicles traveling on impervious (concrete or bituminous materials) surface can be 2±3 dB(A) higher than that on pervious (open-textured materials) surface. In Hong Kong, four road sites were selected for measuring the trac noise levels at both peak and o-peak hours. The sites re¯ect dierent local characteristics which consist undivided carriageway, divided carriageway with and without tramway, impervious and pervious road surfaces. The site characteristics are summarized in Table 1. 3. ESTIMATION OF THE TRAFFIC NOISE LEVEL
The equation being used in practice for predicting the basic trac noise level at the reference distance of 10 m in terms of hourly L10 is the CRTN model (Department of Transport, Welsh Oce, 1988): L10 10 log Q 33 log V 40 500=V 10 log 1 5P=V ÿ 27:6
1
where further adjustments for gradient and road surface are required (Department of the Environment, 1976; Department of Transport, Welsh Oce, 1988). Figure 1 shows the ¯ow chart for estimating trac noise from road schemes. The adjustment for the extra noise from trac on a gradient (G) is expressed as a percentage. The adjustment for road gradient of 0.3G is applied when actual mean speed is used, or 0.2G when using design speed of road. In the special case of dual carriageways being separated by more than 5 m or if their outer edges dier in height by more than 1 m, each carriageway should be treated as a separate road. In such case, the gradient adjustment is applied only for the upward ¯ow. The adjustment for road surface is only applied if the road is concrete with 5 mm or more deep random grooving. The adjustment is (4±0.03P) where P is the percentage of heavy vehicles. In the study of Lam and Lai (1994), the validation of eqn (1) was carried out by comparing the estimated L10 values with the corresponding L10 values observed in Hong Kong. The estimated L10 values are calibrated by eqn (1), in which the data of Q, V and P was collected on sites. Using the linear regression model, the accuracy of the estimated L10 to the observed values (directly collected from the surveys) was examined. Figure 2 shows the result of regression analysis of the noise descriptor L10. It was found that the CRTN model is over-estimated trac noise level by 1.2 dB(A) on the average. As shown in Fig. 2, the coecient of determination (R2) of the 45 line (i.e. the observed values=the estimated values) is 0.892. With taking into account the eects of local environment and conditions, the equations for estimating the Hong Kong trac noise levels need to be re-calibrated. Without adding considerable complexity to the prediction model, a simply way of modifying the prediction procedure is only to recalculate the coecients of equation and the constant term using the survey data. Regression analysis was used to carry out on the four observed noise descriptors L10, Leq, L50 and L90, with the trac noise estimates (EstL10) from eqn (1), respectively. The residual term of each regression model is assumed to be normal. The following regression models (2)±(5) are found. The advantage is to easily adjust the noise level that was estimated by the Table 1. Characteristics of the four road sites in Hong Kong Site
1
2
3
4
Location Road type Carriageway width (m) No. of lanes Central divider (m) Tramway Road surface Ground cover Road surface Two-way peak ¯ow (veh/h) Heavy vehicles at peak (%)
Queensway Primary distributor 22.6 7 1.5 Yes Impervious Concrete Dry 4950 13.6
Harcourt Road Urban trunk road 23.5 7 1.2 No Impervious Concrete Dry 10,230 16.1
Kennedy Road District distributor 7 2 No No Impervious Flexible Dry 900 14.5
East Kowloon Way Urban trunk road 20 6 1.2 No Pervious Open-textured Dry 4200 16.1
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W. H. K. Lam and M. L. Tam
Fig. 1. Flow chart for estimating trac noise from road schemes.
CRTN model in the past. Therefore, it is not necessary to follow the estimating process again and the revised estimates can be simply found from the regression models. L10 1:0534 26:52 Est L10 ÿ 5:3347 ÿ1:71
Adj: R2 0:963
2
Leq 1:0548 23:52 Est L10 ÿ 7:8634 ÿ2:24
Adj: R2 0:953
3
Reliability analysis of trac noise estimates in Hong Kong
243
Fig. 2. Validation of estimated trac noise by the CRTN model.
L50 1:0976 22:03 Est L10 ÿ 12:083 ÿ3:09
Adj: R2 0:947
4
L90 1:0047 20:37 Est L10 ÿ 7:2044 ÿ1:86
Adj: R2 0:939
5
The t-statistics of the coecients are shown in the parentheses and they are signi®cant at the con®dence level 95%. The residual analysis shows that the problems of heteroscedasticity and multi-colinearity have not existed. The normality test is also conducted and found that the assumption of normality in the residual term is adequate. The standard deviations between observed and new estimated values of L10, Leq, L50 and L90 are 0.94, 1.07, 1.26 and 1.15, respectively. When comparing the adjusted coecients of determination (Adj: R2 ) of the equations, eqn (2) gives the highest accuracy of 96.3% to estimate the L10 trac noise, and eqn (5) has the lowest accuracy of 93.9% in the estimation of L90. The overall accuracy of the four equations is over 90% which shows a good estimation of trac noise levels under local circumstances. By substituting the eqn (1) into eqns (2)±(5), the general form of the revised equations of noise descriptors L10, Leq, L50 and L90 are listed as follows: L10 10:534 log Q 34:762 log V 40 500=V 10:534 log 1 5P=V ÿ 34:409
6
Leq 10:548 log Q 34:808 log V 40 500=V 10:548 log 1 5P=V ÿ 36:976
7
L50 10:976 log Q 36:221 log V 40 500=V 10:976 log 1 5P=V ÿ 42:377
8
L90 10:047 log Q 33:155 log V 40 500=V 10:047 log 1 5P=V ÿ 34:934
9
where further adjustments for gradient and road surface are required.
244
W. H. K. Lam and M. L. Tam
The new estimated noise descriptors were compared with the observed values to investigate the accuracy of the revised equations. Regression analysis was also performed to compare the noise descriptor L10 estimated by eqn (6) against the observed data. Figure 3 shows the result of the comparison. It was found that the coecient of determination (R2) is 0.964 which is comparatively higher than that of 0.892 as shown in Fig. 2. Comparing the eqns (1) and (6), it can be seen that the coecients of trac ¯ow Q, trac speed V and percentage of heavy vehicles P in eqn (6) are greater than that of eqn (1). However, the absolute value of the constant term in eqn (6) is comparatively greater than that of eqn (1) but with a negative sign. As shown in Fig. 2, the CRTN model tends to overestimate the trac noise as compared to the noise levels measured on sites in Hong Kong. This is due to the model was calibrated mainly to suit the urban characteristics, street systems and local conditions in U.K., while the urban characteristics and conditions in Hong Kong have not yet been tested. In Hong Kong, many mitigation measures for controlling the problem of road trac noise are implemented in recent years by the Government. Statutory provisions on the maintenance of vehicles require vehicles to be ®tted with ecient exhaust silencers. This would give rise to a decrease in the overall trac noise levels and to have annual inspections before renewing the licences. Moreover, improved road surface design has reduced noise generated from road/tyre interaction. Since the trac noise descriptor L10 represents the critical condition on the noise levels, it is decided to use L10 for the following analyses. With the use of the eqn (6), the eect of each factor on trac noise can be assessed by sensitivity analysis. Firstly, one of the factors is identi®ed and used to test the eect on trac noise estimates. Then, the value of this factor is varied to compute the noise levels while other factors are ®xed. The ranges of the key factors chosen for the sensitivity analysis is 500±6000 veh/h, 25±80 km/h and 0±100% for trac ¯ow, speed and percentage of heavy vehicles, respectively.
Fig. 3. Validation of estimated trac noise by new model.
Reliability analysis of trac noise estimates in Hong Kong
245
In the analysis, three dierent noise scenarios are de®ned: (a) high noise scenario: Flow (Q)=6000 veh/h, speed (V)=80 km/h and percentage of heavy vehicles (P)=100%. (b) medium noise scenario: Q=3250 veh/h, V=52.5 km/h and P=50%. (c) Low noise scenario: Q 500 ve=h, V 25 km=h and P 0%. The sensitivity of trac ¯ow on noise levels shows that the rate of changes of L10 levels on the three noise scenarios are decreasing. The decreasing rate is obvious from 500 to 300 veh/h. The increase of L10 level from 3000 veh/h onwards is approximately linear. Over the whole range, the maximum dierence of L10 levels for all noise scenarios is 11 dB(A). The sensitivity of trac speed on trac noise levels show that the L10 variation for high and medium noise scenarios are similar. The noise levels are decreasing slightly and then increasing in the ranges 25±40 and 40±80 km/h, respectively. The noise variations are small with a maximum dierence of 2 dB(A). The speed change is comparatively sensitive in the low noise region. The L10 level is increasing with a steady rate in the whole range. The maximum dierence of L10 level is about 6 dB(A). The sensitivity of heavy vehicle percentage on noise levels shows that the L10 variations for the all three noise scenarios are similar. The rate of changes are decreasing in the range 0±45% and then become steady from 45 to 100%. The maximum dierence of L10 levels for high, medium and low noise scenarios are 9, 10 and 14 dB(A), respectively. The overall results show that the three key factors have an increasing eect to the L10 noise level in all noise scenarios. The results also show that the trac ¯ow is the most signi®cant factor aecting trac noise, while the percentage of heavy vehicles is the second and the trac speed is the least one. The results of the sensitivity analysis are summarized in Table 2. 4. DERIVATION OF PROBABILITY DISTRIBUTIONS FOR THE FACTORS
In the reliability study of the trac noise estimates, all the essential factors were measured in nine locations on the Queensway in Hong Kong during October 1993 to January 1994. The recorded data includes trac noise, trac ¯ow, vehicle speeds and trac composition. All the vehicles were classi®ed into three categories: (a) cars, vans and light goods vehicles which less than 1525 kg unladen weight; (b) medium goods vehicles with two axles and heavy goods vehicles include all commercial vehicles with three or more axles greater than 1525 kg unladen weight; and (c) including public light buses, coaches and tram. Manual trac count was carried out at 30 min intervals at each location to obtain trac ¯ow rate. Spot speed study was conducted to obtain vehicle speeds. It was found that the hourly tram ¯ow was small and the noise generated by trams were insigni®cant. For simplicity, trams were classi®ed as heavy vehicles. Using the data collected from the surveys, the distributions of the trac ¯ow, speed and composition are obtained by goodness-of-®t test at 5% level of signi®cance. Since no any well-de®ned Table 2. Summary of the results of sensitivity analysis Factors Range Eect to L10 level Maximum Dierence in noise region [dB (A)] Sensitivity rankinga Overall sensitivitya a
High Medium Low High Medium Low Overallb Decrease in sensitivity
Flow (veh/h)
Speed (km/h)
Heavy vehicles (%)
500±6000 Increase 11 11 11 1 1 2 4 1
25±80 Increase 2 2 6 3 3 3 9 3
0±100 Increase 9 10 14 2 2 1 5 2
Sensitivity ranking: 1 ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ! 10. Sum of the sensitivity rankings of high, medium and low noise scenarios.
b
246
W. H. K. Lam and M. L. Tam Table 3. Probability distribution functions of trac ¯ow, speed and composition
Factors
Probability distributions
Trac ¯ow Trac speed Trac composition (in terms of percentage of heavy vehicles) Notes:
1 ÿ1 1ÿx 2 ÿ1
Beta 1 ; 2 x
B 1 ; 2 1 x ÿ1 1
where B x1 ; x2 t 0
Empirical distribution Beta (0.9868, 0.6388)15.57+33.7143 Normal (17.4160, 5.49842)
ÿ ÿ2 ÿ 1 ; Normal ; 2 p e 22 2 2
1 ÿ tx2 ÿ1 dt
Table 4. The results of reliability analysis of trac noise estimates Observed mean trac noise [dB(A)] Estimated mean trac noise [dB(A)] Probability of reaching or greater than observed mean value 95% con®dent interval of the estimated trac noise [dB(A)] Estimated median trac noise [dB(A)]
77.06 77.73 73.54% 75.28
probability distribution function can be generalized for trac ¯ow distribution, empirical distribution of trac ¯ow will be used. It is observed that the probability distributions of trac speed is followed a Beta function and the trac composition (in terms of percentage of heavy vehicles) is normally distributed. The probability distribution functions of the three factors are given in Table 3. The probability distribution of trac noise can then be estimated by the Monte Carlo simulation using the derived distributions. 5. ESTIMATION OF THE COMBINED PROBABILITY OF THE TRAFFIC NOISE
With the use of the Monte Carlo simulation method, the combined probability of the estimated trac noise can be derived. A convergence test is performed to determine the number of simulations required. The result shows that 10,000 simulations are better than 5000 simulations, while the result of 10,000 and 15,000 simulations are nearly the same. Therefore, it is decided to simulate 10,000 possible outcomes of each factor for the reliability analysis of the trac noise estimates. The results of the reliability analysis of the simulated trac noise are summarized in Table 4. The probability density function and cumulative distribution function of the simulated trac noise are presented in Figs 4 and 5, respectively. The reliability analysis incorporates the variations of those key factors aecting the generation of trac noise. It can give the interval within a particular probability and/or probability of a
Fig. 4. Probability density function of trac noise (L10) estimates in the Queensway.
Reliability analysis of trac noise estimates in Hong Kong
247
Fig. 5. Cumulative distribution function of trac noise (L10) estimates in the Queensway.
speci®c trac noise level. The observed average trac noise measured in the nine locations on the Queensway is 77.06 dB(A), while the simulated average trac noise is 77.73 dB(A). It is observed that the percentage dierence between the observed and estimated average trac noise level is only 0.87%, which means that the revised eqn (6) gives a reasonable estimate for the average trac noise. There is about 73.5% of the trac noise reaches or greater than 77.06 dB(A) and 95% trac noise is below 79.41 dB(A). The 95% con®dence for the range of trac noise estimates on the Queensway is from 75.28 to 79.78 dB(A). The median trac noise estimate is 77.8 dB(A) which is close to the estimated mean value. The reliability analysis not only gives the average value of the estimated trac noise, but also displays the whole picture in terms of the probability with the trac noise estimate. Therefore, this analysis can give a better insight on the impacts of trac noise so as to help environmental evaluation of all candidate highway projects in Hong Kong. 6. CONCLUSIONS
Due to the adverse environmental eects existed by the transport infrastructure, attention has been given by the planners and engineers to establish environmental criteria and protect the minority from suering the pain. In order to assess properly the likely eects which transport infrastructure projects will have on people and the environment, it is required to develop a measure, which will enable a reliable assessment of environmental impacts of transport proposals to be made in general circumstances. Road trac noise is considered as the major environmental impact and chosen for environmental evaluation of all candidate highway projects in Hong Kong. In this paper, the impacts of trac noise variations on Hong Kong urban roads as well as the major underlying factors aecting the trac noise estimates are examined. The trac noise prediction model relating various local factors aecting generation of trac noise is implemented to estimate trac noise in Hong Kong. A reliability analysis for the Hong Kong trac noise model is given to consider the variation of the key factors (trac ¯ow, speed and composition which is in terms of percentage of heavy vehicles) in estimating the trac noise level. The analysis gives the trac noise estimates with a particular probability, or vice versa. This approach will give a better insight on the sustainable impact of trac noise due to implementation of transport infrastructure. AcknowledgementsÐThis study was supported with a research grant (350/382) awarded by the Research Committee of The Hong Kong Polytechnic University. REFERENCES Department of the Environment (1976) Predicting Road Trac Noise. HMSO, London. Department of Transport, Welsh Oce (1988) Calculation of Road Trac Noise (CRTN). HMSO, London. Ferrary, C. (1990) Environmental assessmentÐthe transport element. Highways and TransportationÐThe Journal of the Institution of Highways and Transportation and IHIE 37, 31±35.
248
W. H. K. Lam and M. L. Tam
Hong Kong Transport Department and Wilbur Smith Associates (1989) Hong Kong Second Comprehensive Transport Study. Government Printer, Hong Kong. Lam, W. H. K. and Lai, W. P. (1994) A time-dependent trac noise model for Hong Kong. Conference Proceedings of POLMET'94ÐPollution in the Metropolitan and Urban Environment, Beijing, China, 14±17 November 1994, pp. 628± 635. Ross, S. M. (1991) A Course in Simulation. Macmillan, New York.
Pergamon PII: S1361-9209(98)00002-9
RELIABILITY ANALYSIS OF TRAFFIC NOISE ESTIMATES IN HONG KONG WILLIAM H. K. LAM* and M. L. TAM
Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (Received 26 June 1997; in revised form 2 February 1998) AbstractÐThis paper examines the reliability of trac noise measurement techniques and the noise estimates in Hong Kong. A simulation model making use of the Monte Carlo technique is devised to incorporate the uncertainty in trac noise estimates in Hong Kong. Trac ¯ow, trac speed and trac composition (in terms of percentage of heavy vehicles) are identi®ed as the key factors in¯uencing the generation of trac noise. The new equations for estimating the noise descriptors L10, Leq, L50 and L90 were calibrated on the basis of the Hong Kong survey results. The eects of the key factors on trac noise estimates can be assessed. The probability distribution for each of the key factors is derived with the use of the survey data. The variation on the key factors is due to the sampling error in the survey. The reliability of the trac noise estimates is obtained from the combined probability distributions of the key factors. The reliability analysis gives the trac noise estimates with a particular probability or vice versa. # 1998 Elsevier Science Ltd. All rights reserved Keywords: trac noise, Monte Carlo simulation, roads, Hong Kong 1. INTRODUCTION
Provision of transport infrastructure such as road bridges or ¯yovers in Hong Kong will undoubtedly contribute to the bene®t of the community at large. Nevertheless, due to the limited availability of land resources and ®nances, it is inevitable that in some of the schemes there will be some adverse environmental eects to those living in close proximity to them. For example, some of the existing road bridges in Hong Kong have caused many socially unacceptable eects such as intolerable noise, loss of privacy, visual intrusion, fumes and dirt, and vibration aecting those people unfortunate enough to have to continue to live and work close by. In order to assess properly the likely eects which transport infrastructure projects will have on people and the environment, it is required to develop a measure, combining the elements of speci®c techniques of environmental measurement already available (Ferrary, 1990), which will enable a reliable assessment of environmental impacts of transport proposals to be made in general circumstances. In the Hong Kong Second Comprehensive Transport Study (Hong Kong Transport Department and Wilbur Smith Associates, 1989), road trac noise was considered as the major environmental impact and chosen for environmental evaluation of all candidate highway projects. The most common trac noise descriptors are L10, Leq, L50 and L90 where LN represented the noise level exceeded for N% of the time period. L10 gives an indication of the top end of the level range although it can be substantially less than the occasional peak. L90 corresponds to the background noise level in the absence of nearby noise sources. Leq is the equivalent continuous sound level contains the same quantity of sound energy over a de®ned time period as the actual time varying sound level. The trac noise model currently used in Hong Kong is the one developed by U.K. Department of Transport (Department of Transport, Welsh Oce, 1988). The new equations for estimating the trac noise levels will be calibrated using the survey data in Hong Kong. The noise descriptor L10 will be applied for analysis because it is commonly used in the evaluation of highway projects. *Author for correspondence. Fax: 00 852 2334 6389; e-mail: [email protected]
239
240
W. H. K. Lam and M. L. Tam
This paper highlights in particular the impacts of trac noise variations on highway projects as well as the major underlying factors aecting the noise estimates. The trac noise prediction models (Department of Transport, Welsh Oce, 1988) developed in U.K. has been adopted in the Hong Kong Planning Standard and Guidelines as the preferred calculation method. Empirical formulae relating various local factors aecting generation and propagation of trac noise are implemented to assess trac noise in Hong Kong (Lam and Lai, 1994). However, if the eects of variations of those factors are not examined, the trac noise measurements may be over-estimated or under-estimated. With advanced mathematical techniques, it is possible to incorporate reliability analysis in trac noise estimates. The following four stage process is the proposed methodology for assessing the reliability of estimating trac noise: 1. identi®cation of the key factors behind the trac noise estimates; 2. estimation of how these key factors aect the noise estimates; 3. derivation of probability distributions for the key factors which specify the range and shape of the probable future outcome over which each factor is likely to vary; and 4. estimation of the combined probability of the outcome. Basically, the ®rst and second stages involve identi®cation of data required and calibration of the trac noise model. The third stage is concerned with the use of the survey data to derive the probability distribution for each of the key factors. The last stage makes use of the Monte Carlo simulation technique (Ross, 1991) to consider a number of possible outcomes of each key factor with their values selected at random form within their probability distributions. As a result, the reliability of the trac noise estimates can be obtained from the combined probability distributions of the key factors. The reliability analysis can be carried out to assess the probability of reaching speci®c trac noise levels in given time periods for the candidate highway project. The new approach would give a better insight on the sustainable impact of trac noise due to implementation of transport infrastructure. 2. FACTORS AFFECTING THE GENERATION OF TRAFFIC NOISE
There are many factors which in¯uence the generation of trac noise level under Hong Kong circumstances. For instance, on the supply side, trac ¯ow, speed, composition, frequency of stopping and starting, road gradient and surface. When considering the aected side, the factors like proximity to road, type of land use and time of day, also aect the generation of trac noise. In Hong Kong, the basic method for calculating the trac level is the CRTN model (Department of Transport, Welsh Oce, 1988), in which the following variables are investigated: 1. 2. 3. 4. 5.
trac ¯ow Q (veh/h), trac speed V (km/h), trac composition (in terms of percentage of heavy vehicles) P (%), road gradient G (%); and road surface.
Hence, in this paper, the same choice of the variables is adopted so as to assess the applicability of the CRTN model to Hong Kong. As a result, it can easily adjust the noise level that was estimated by the CRTN model in the past. It is impossible to separate the parameters completely, in particular trac speed and trac composition were found to interact and these are therefore considered together. When estimating trac noise levels for a road section it is important to ensure that compatible values of ¯ow, speed and composition are adopted. For noise prediction purposes, trac compositions are generally divided into two vehicles classes: 1. Light vehicle (<1525 kg unladen weight); and 2. Heavy vehicle (>1525 kg unladen weight). It is also recognized that trac noise levels are aected by the gradient of a road. The eect of gradient depends critically upon the percentage of heavy vehicles and the trac noise can be increased up to 12 dB(A) for a gradient of 1 in 8.
Reliability analysis of trac noise estimates in Hong Kong
241
Due to the interaction between the road and tyre, road surface texture aects directly the noise level generated by trac. The noise generated by vehicles traveling on impervious (concrete or bituminous materials) surface can be 2±3 dB(A) higher than that on pervious (open-textured materials) surface. In Hong Kong, four road sites were selected for measuring the trac noise levels at both peak and o-peak hours. The sites re¯ect dierent local characteristics which consist undivided carriageway, divided carriageway with and without tramway, impervious and pervious road surfaces. The site characteristics are summarized in Table 1. 3. ESTIMATION OF THE TRAFFIC NOISE LEVEL
The equation being used in practice for predicting the basic trac noise level at the reference distance of 10 m in terms of hourly L10 is the CRTN model (Department of Transport, Welsh Oce, 1988): L10 10 log Q 33 log V 40 500=V 10 log 1 5P=V ÿ 27:6
1
where further adjustments for gradient and road surface are required (Department of the Environment, 1976; Department of Transport, Welsh Oce, 1988). Figure 1 shows the ¯ow chart for estimating trac noise from road schemes. The adjustment for the extra noise from trac on a gradient (G) is expressed as a percentage. The adjustment for road gradient of 0.3G is applied when actual mean speed is used, or 0.2G when using design speed of road. In the special case of dual carriageways being separated by more than 5 m or if their outer edges dier in height by more than 1 m, each carriageway should be treated as a separate road. In such case, the gradient adjustment is applied only for the upward ¯ow. The adjustment for road surface is only applied if the road is concrete with 5 mm or more deep random grooving. The adjustment is (4±0.03P) where P is the percentage of heavy vehicles. In the study of Lam and Lai (1994), the validation of eqn (1) was carried out by comparing the estimated L10 values with the corresponding L10 values observed in Hong Kong. The estimated L10 values are calibrated by eqn (1), in which the data of Q, V and P was collected on sites. Using the linear regression model, the accuracy of the estimated L10 to the observed values (directly collected from the surveys) was examined. Figure 2 shows the result of regression analysis of the noise descriptor L10. It was found that the CRTN model is over-estimated trac noise level by 1.2 dB(A) on the average. As shown in Fig. 2, the coecient of determination (R2) of the 45 line (i.e. the observed values=the estimated values) is 0.892. With taking into account the eects of local environment and conditions, the equations for estimating the Hong Kong trac noise levels need to be re-calibrated. Without adding considerable complexity to the prediction model, a simply way of modifying the prediction procedure is only to recalculate the coecients of equation and the constant term using the survey data. Regression analysis was used to carry out on the four observed noise descriptors L10, Leq, L50 and L90, with the trac noise estimates (EstL10) from eqn (1), respectively. The residual term of each regression model is assumed to be normal. The following regression models (2)±(5) are found. The advantage is to easily adjust the noise level that was estimated by the Table 1. Characteristics of the four road sites in Hong Kong Site
1
2
3
4
Location Road type Carriageway width (m) No. of lanes Central divider (m) Tramway Road surface Ground cover Road surface Two-way peak ¯ow (veh/h) Heavy vehicles at peak (%)
Queensway Primary distributor 22.6 7 1.5 Yes Impervious Concrete Dry 4950 13.6
Harcourt Road Urban trunk road 23.5 7 1.2 No Impervious Concrete Dry 10,230 16.1
Kennedy Road District distributor 7 2 No No Impervious Flexible Dry 900 14.5
East Kowloon Way Urban trunk road 20 6 1.2 No Pervious Open-textured Dry 4200 16.1
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Fig. 1. Flow chart for estimating trac noise from road schemes.
CRTN model in the past. Therefore, it is not necessary to follow the estimating process again and the revised estimates can be simply found from the regression models. L10 1:0534 26:52 Est L10 ÿ 5:3347 ÿ1:71
Adj: R2 0:963
2
Leq 1:0548 23:52 Est L10 ÿ 7:8634 ÿ2:24
Adj: R2 0:953
3
Reliability analysis of trac noise estimates in Hong Kong
243
Fig. 2. Validation of estimated trac noise by the CRTN model.
L50 1:0976 22:03 Est L10 ÿ 12:083 ÿ3:09
Adj: R2 0:947
4
L90 1:0047 20:37 Est L10 ÿ 7:2044 ÿ1:86
Adj: R2 0:939
5
The t-statistics of the coecients are shown in the parentheses and they are signi®cant at the con®dence level 95%. The residual analysis shows that the problems of heteroscedasticity and multi-colinearity have not existed. The normality test is also conducted and found that the assumption of normality in the residual term is adequate. The standard deviations between observed and new estimated values of L10, Leq, L50 and L90 are 0.94, 1.07, 1.26 and 1.15, respectively. When comparing the adjusted coecients of determination (Adj: R2 ) of the equations, eqn (2) gives the highest accuracy of 96.3% to estimate the L10 trac noise, and eqn (5) has the lowest accuracy of 93.9% in the estimation of L90. The overall accuracy of the four equations is over 90% which shows a good estimation of trac noise levels under local circumstances. By substituting the eqn (1) into eqns (2)±(5), the general form of the revised equations of noise descriptors L10, Leq, L50 and L90 are listed as follows: L10 10:534 log Q 34:762 log V 40 500=V 10:534 log 1 5P=V ÿ 34:409
6
Leq 10:548 log Q 34:808 log V 40 500=V 10:548 log 1 5P=V ÿ 36:976
7
L50 10:976 log Q 36:221 log V 40 500=V 10:976 log 1 5P=V ÿ 42:377
8
L90 10:047 log Q 33:155 log V 40 500=V 10:047 log 1 5P=V ÿ 34:934
9
where further adjustments for gradient and road surface are required.
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The new estimated noise descriptors were compared with the observed values to investigate the accuracy of the revised equations. Regression analysis was also performed to compare the noise descriptor L10 estimated by eqn (6) against the observed data. Figure 3 shows the result of the comparison. It was found that the coecient of determination (R2) is 0.964 which is comparatively higher than that of 0.892 as shown in Fig. 2. Comparing the eqns (1) and (6), it can be seen that the coecients of trac ¯ow Q, trac speed V and percentage of heavy vehicles P in eqn (6) are greater than that of eqn (1). However, the absolute value of the constant term in eqn (6) is comparatively greater than that of eqn (1) but with a negative sign. As shown in Fig. 2, the CRTN model tends to overestimate the trac noise as compared to the noise levels measured on sites in Hong Kong. This is due to the model was calibrated mainly to suit the urban characteristics, street systems and local conditions in U.K., while the urban characteristics and conditions in Hong Kong have not yet been tested. In Hong Kong, many mitigation measures for controlling the problem of road trac noise are implemented in recent years by the Government. Statutory provisions on the maintenance of vehicles require vehicles to be ®tted with ecient exhaust silencers. This would give rise to a decrease in the overall trac noise levels and to have annual inspections before renewing the licences. Moreover, improved road surface design has reduced noise generated from road/tyre interaction. Since the trac noise descriptor L10 represents the critical condition on the noise levels, it is decided to use L10 for the following analyses. With the use of the eqn (6), the eect of each factor on trac noise can be assessed by sensitivity analysis. Firstly, one of the factors is identi®ed and used to test the eect on trac noise estimates. Then, the value of this factor is varied to compute the noise levels while other factors are ®xed. The ranges of the key factors chosen for the sensitivity analysis is 500±6000 veh/h, 25±80 km/h and 0±100% for trac ¯ow, speed and percentage of heavy vehicles, respectively.
Fig. 3. Validation of estimated trac noise by new model.
Reliability analysis of trac noise estimates in Hong Kong
245
In the analysis, three dierent noise scenarios are de®ned: (a) high noise scenario: Flow (Q)=6000 veh/h, speed (V)=80 km/h and percentage of heavy vehicles (P)=100%. (b) medium noise scenario: Q=3250 veh/h, V=52.5 km/h and P=50%. (c) Low noise scenario: Q 500 ve=h, V 25 km=h and P 0%. The sensitivity of trac ¯ow on noise levels shows that the rate of changes of L10 levels on the three noise scenarios are decreasing. The decreasing rate is obvious from 500 to 300 veh/h. The increase of L10 level from 3000 veh/h onwards is approximately linear. Over the whole range, the maximum dierence of L10 levels for all noise scenarios is 11 dB(A). The sensitivity of trac speed on trac noise levels show that the L10 variation for high and medium noise scenarios are similar. The noise levels are decreasing slightly and then increasing in the ranges 25±40 and 40±80 km/h, respectively. The noise variations are small with a maximum dierence of 2 dB(A). The speed change is comparatively sensitive in the low noise region. The L10 level is increasing with a steady rate in the whole range. The maximum dierence of L10 level is about 6 dB(A). The sensitivity of heavy vehicle percentage on noise levels shows that the L10 variations for the all three noise scenarios are similar. The rate of changes are decreasing in the range 0±45% and then become steady from 45 to 100%. The maximum dierence of L10 levels for high, medium and low noise scenarios are 9, 10 and 14 dB(A), respectively. The overall results show that the three key factors have an increasing eect to the L10 noise level in all noise scenarios. The results also show that the trac ¯ow is the most signi®cant factor aecting trac noise, while the percentage of heavy vehicles is the second and the trac speed is the least one. The results of the sensitivity analysis are summarized in Table 2. 4. DERIVATION OF PROBABILITY DISTRIBUTIONS FOR THE FACTORS
In the reliability study of the trac noise estimates, all the essential factors were measured in nine locations on the Queensway in Hong Kong during October 1993 to January 1994. The recorded data includes trac noise, trac ¯ow, vehicle speeds and trac composition. All the vehicles were classi®ed into three categories: (a) cars, vans and light goods vehicles which less than 1525 kg unladen weight; (b) medium goods vehicles with two axles and heavy goods vehicles include all commercial vehicles with three or more axles greater than 1525 kg unladen weight; and (c) including public light buses, coaches and tram. Manual trac count was carried out at 30 min intervals at each location to obtain trac ¯ow rate. Spot speed study was conducted to obtain vehicle speeds. It was found that the hourly tram ¯ow was small and the noise generated by trams were insigni®cant. For simplicity, trams were classi®ed as heavy vehicles. Using the data collected from the surveys, the distributions of the trac ¯ow, speed and composition are obtained by goodness-of-®t test at 5% level of signi®cance. Since no any well-de®ned Table 2. Summary of the results of sensitivity analysis Factors Range Eect to L10 level Maximum Dierence in noise region [dB (A)] Sensitivity rankinga Overall sensitivitya a
High Medium Low High Medium Low Overallb Decrease in sensitivity
Flow (veh/h)
Speed (km/h)
Heavy vehicles (%)
500±6000 Increase 11 11 11 1 1 2 4 1
25±80 Increase 2 2 6 3 3 3 9 3
0±100 Increase 9 10 14 2 2 1 5 2
Sensitivity ranking: 1 ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ! 10. Sum of the sensitivity rankings of high, medium and low noise scenarios.
b
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W. H. K. Lam and M. L. Tam Table 3. Probability distribution functions of trac ¯ow, speed and composition
Factors
Probability distributions
Trac ¯ow Trac speed Trac composition (in terms of percentage of heavy vehicles) Notes:
1 ÿ1 1ÿx 2 ÿ1
Beta 1 ; 2 x
B 1 ; 2 1 x ÿ1 1
where B x1 ; x2 t 0
Empirical distribution Beta (0.9868, 0.6388)15.57+33.7143 Normal (17.4160, 5.49842)
ÿ ÿ2 ÿ 1 ; Normal ; 2 p e 22 2 2
1 ÿ tx2 ÿ1 dt
Table 4. The results of reliability analysis of trac noise estimates Observed mean trac noise [dB(A)] Estimated mean trac noise [dB(A)] Probability of reaching or greater than observed mean value 95% con®dent interval of the estimated trac noise [dB(A)] Estimated median trac noise [dB(A)]
77.06 77.73 73.54% 75.28
probability distribution function can be generalized for trac ¯ow distribution, empirical distribution of trac ¯ow will be used. It is observed that the probability distributions of trac speed is followed a Beta function and the trac composition (in terms of percentage of heavy vehicles) is normally distributed. The probability distribution functions of the three factors are given in Table 3. The probability distribution of trac noise can then be estimated by the Monte Carlo simulation using the derived distributions. 5. ESTIMATION OF THE COMBINED PROBABILITY OF THE TRAFFIC NOISE
With the use of the Monte Carlo simulation method, the combined probability of the estimated trac noise can be derived. A convergence test is performed to determine the number of simulations required. The result shows that 10,000 simulations are better than 5000 simulations, while the result of 10,000 and 15,000 simulations are nearly the same. Therefore, it is decided to simulate 10,000 possible outcomes of each factor for the reliability analysis of the trac noise estimates. The results of the reliability analysis of the simulated trac noise are summarized in Table 4. The probability density function and cumulative distribution function of the simulated trac noise are presented in Figs 4 and 5, respectively. The reliability analysis incorporates the variations of those key factors aecting the generation of trac noise. It can give the interval within a particular probability and/or probability of a
Fig. 4. Probability density function of trac noise (L10) estimates in the Queensway.
Reliability analysis of trac noise estimates in Hong Kong
247
Fig. 5. Cumulative distribution function of trac noise (L10) estimates in the Queensway.
speci®c trac noise level. The observed average trac noise measured in the nine locations on the Queensway is 77.06 dB(A), while the simulated average trac noise is 77.73 dB(A). It is observed that the percentage dierence between the observed and estimated average trac noise level is only 0.87%, which means that the revised eqn (6) gives a reasonable estimate for the average trac noise. There is about 73.5% of the trac noise reaches or greater than 77.06 dB(A) and 95% trac noise is below 79.41 dB(A). The 95% con®dence for the range of trac noise estimates on the Queensway is from 75.28 to 79.78 dB(A). The median trac noise estimate is 77.8 dB(A) which is close to the estimated mean value. The reliability analysis not only gives the average value of the estimated trac noise, but also displays the whole picture in terms of the probability with the trac noise estimate. Therefore, this analysis can give a better insight on the impacts of trac noise so as to help environmental evaluation of all candidate highway projects in Hong Kong. 6. CONCLUSIONS
Due to the adverse environmental eects existed by the transport infrastructure, attention has been given by the planners and engineers to establish environmental criteria and protect the minority from suering the pain. In order to assess properly the likely eects which transport infrastructure projects will have on people and the environment, it is required to develop a measure, which will enable a reliable assessment of environmental impacts of transport proposals to be made in general circumstances. Road trac noise is considered as the major environmental impact and chosen for environmental evaluation of all candidate highway projects in Hong Kong. In this paper, the impacts of trac noise variations on Hong Kong urban roads as well as the major underlying factors aecting the trac noise estimates are examined. The trac noise prediction model relating various local factors aecting generation of trac noise is implemented to estimate trac noise in Hong Kong. A reliability analysis for the Hong Kong trac noise model is given to consider the variation of the key factors (trac ¯ow, speed and composition which is in terms of percentage of heavy vehicles) in estimating the trac noise level. The analysis gives the trac noise estimates with a particular probability, or vice versa. This approach will give a better insight on the sustainable impact of trac noise due to implementation of transport infrastructure. AcknowledgementsÐThis study was supported with a research grant (350/382) awarded by the Research Committee of The Hong Kong Polytechnic University. REFERENCES Department of the Environment (1976) Predicting Road Trac Noise. HMSO, London. Department of Transport, Welsh Oce (1988) Calculation of Road Trac Noise (CRTN). HMSO, London. Ferrary, C. (1990) Environmental assessmentÐthe transport element. Highways and TransportationÐThe Journal of the Institution of Highways and Transportation and IHIE 37, 31±35.
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Hong Kong Transport Department and Wilbur Smith Associates (1989) Hong Kong Second Comprehensive Transport Study. Government Printer, Hong Kong. Lam, W. H. K. and Lai, W. P. (1994) A time-dependent trac noise model for Hong Kong. Conference Proceedings of POLMET'94ÐPollution in the Metropolitan and Urban Environment, Beijing, China, 14±17 November 1994, pp. 628± 635. Ross, S. M. (1991) A Course in Simulation. Macmillan, New York.