Development of a Highway Traffic Noise Prediction Model That Considers Various Road Surface Types

Abstract A highway traffic noise prediction model has been developed for environmental assessment in South Korea. The model is based on an outdoor sound propagation method and is fully compliant with ISO 9613 and the sound power level (PWL) estimation for a road segment, as suggested in the ASJ Model-1998 that is based on PWLs. Due to that model’s selection of two pavement types, such as asphalt or concrete pavement, an unacceptable traffic noise prediction is made in cases where the road surface is different from that on which the model is based. In order to address this problem, several road surface types are categorized, and the PWL of each surface type is determined and modeled by measuring the noise levels obtained from newly developed methods. An evaluation of the traffic noise prediction model using field measurements finds good agreement between predicted and measured noise levels.

Contents Introduction PWL Measurement Method Results and discussion Conclusions

I. Introduction Several traffic noise prediction models have been developed for the prediction of sound pressure levels (SPLs) at roadside. For Korea’s highways, a method of predicting the outdoor sound propagation of road traffic noise has been developed for highway design and as part of an assessment of existing and expected changes in traffic noise conditions. The presented model incorporates ISO 9613-2, which calculates sound attenuation during the propagation of outdoor noise, and the ASJ Model-1998, which uses sound power level (PWL) estimation. Thus, using the determined PWLs, a new model to predict road traffic noise can be implemented based on ISO 9613-2 for the calculation of attenuation during noise propagation. To supplement the physical effects not defined in the ISO standard, the ASJ Model-1998 is adopted to consider noise source geometry, wind effects, and the determination of the most effective diffraction path. The accuracy of the new prediction model of road traffic noise can be assessed by comparing predicted and measured noise levels in overall and octave bands.

Jungbu Inland Expressway II. PWL measurement method, traffic noise prediction model and vehicle categorization Test Road Southbound Jungbu Inland Expressway Northbound The KEC Test Road, located alongside Jungbu Inland Expressway, used for evaluating varying pavement surface types with respect to noise characteristics.

II. PWL measurement method, traffic noise prediction model and vehicle categorization (Contd.) A vehicle’s PWL is determined by using the relationship of LAmax (A-weighted maximum noise level) and LAeq (A-weighted square mean value of the sound pressure) obtained from the pass-by and NCPX methods, respectively. Thus, the vehicle’s PWL, LW,i, which characterizes the noise emitted by the vehicle as it runs on different road surface types, can be calculated from the following equation: The following PWL regression equation, which is based on several noise prediction models, can be determined: where the coefficients a,i and b,i are given for the octave band center frequency, and V is the vehicle driving speed.

II. PWL measurement method, traffic noise prediction model and vehicle categorization (Contd.) The noise prediction method is based on the attenuation of sound during propagation outdoors from all the significant mechanisms found in the method issued from ISO 9613-2. In this standard, the equivalent continuous down-wind octave band SPL at a receiver, LfT,i, is calculated for each point source, for its image sources, and for the eight octave bands with nominal frequencies from 63 Hz to 8 kHz, by the following equation: where LW,i is the octave band PWL, in dBA, radiated by the sound source relative to a reference sound power of one Pico watt (1 pW); Dc,i is the directivity correction, in dB, in the direction from the center of the source to the receiver; and A,i is the combined attenuation, in dB, that is due to geometrical divergence (Adiv,i), atmospheric absorption (Aatm,i), ground effect (Agr,i), a barrier (Abar,i), and miscellaneous other effects (Amisc,i).

Description of vehicle type II. PWL measurement method, traffic noise prediction model and vehicle categorization (Contd.) As shown in Table, there are six vehicle types for noise source modeling based on the PWL determination method. In the prediction of highway traffic noise, the power level of the vehicle type is important; therefore, the overall PWLs generated by each of the six types of vehicles running on 30 mm TPCC and DGA pavements, shown in Figure, are used to determine the possible categories of types. Vehicle classification used in the source modeling Vehicle type Description of vehicle type No. of axles Passenger car  Car where the maximum number of seats is 7 or less 2 Light vehicle  Minivan with 8-10 seats, SUV, pick-up truck Bus  Bus where the maximum number of seats is 30 or more 2-4 Light truck  Truck where the length is between 5 and 6 m Medium truck  Truck where the length exceeds 6 m, except for heavy truck  Medium bus where the maximum number of seats is 11 to 29 Heavy truck  Truck where the vehicle weight is 10,000 kg or more  Dump truck, trailer, large special purpose vehicle  2

II. PWL measurement method, traffic noise prediction model and vehicle categorization (Contd.) b Figure PWLs with respect to vehicle speed: (a) 30 mm TPCC pavement; and (b) DGA pavement.

II. PWL measurement method, traffic noise prediction model and vehicle categorization (Contd.) For example, HARMONOISE and the ASJ Model-1998 categorize five types and four types of vehicles, respectively. For practical purposes, this categorization of the various types of vehicle can be simplified; the following three categories of vehicle types are suggested through observations made from the previous Figure  Category I: heavy truck; medium truck; light truck; bus + light vehicle; and passenger car  Category II: heavy truck; medium truck; light truck + bus + light vehicle; and passenger car  Category III: heavy truck; medium truck; light truck + bus; and light vehicle + passenger car

III. Prediction and measurement of highway traffic noise In order to evaluate the suggested three categories (Categories I, II, and III) of vehicle categorization, which are based on the measured and modeled PWLs obtained from the KEC Test Road, highway traffic noise levels at nine sites were measured, as shown in Table 2. Measurements were made at distances of 7.5 m from lane centers; the receiver heights of the measurements are positioned at 0.1, 1.2, 2.0, 3.0, and 4.0 m above ground level. While the field measurements of highway traffic noise levels were being taken, other tests and measurements were also underway. For example, two laser speed guns were used to measure vehicle speeds; a video-recorder was used to determine traffic flow and vehicle types; and a weather monitoring instrument was used to measure air temperature, humidity, and wind velocity. For each site, the SPLs and traffic and environmental data over a 15-minute period were measured at least two times in each direction.

III. Prediction and measurement of highway traffic noise (Contd.) Field locations for highway traffic noise measurement Location Surface type No. of total lanes Gradient (%) Barrier 1 DGA 4 0.30 - 2 SMA 0.31 3 1.95 30 mm TPCC 5 8 0.95 6 18 mm LPCC 1.40 7 25 mm LPCC PAC 0.79 9 DGPCC 0.63 One side

III. Prediction and measurement of highway traffic noise (Contd.) b c Figure. Comparison between the measured and the predicted A-weighted overall PWLs: (a) Category I; (b) Category II; (c) Category III of vehicle categorization.

III. Prediction and measurement of highway traffic noise (Contd.) b a c Figure. Comparison between the measured and the predicted A-weighted SPLs at 1.2 m receiver point above ground level and 7.5 m horizontal distance from a lane center in the case of AC pavements: (a) DGA; (b) SMA; and (c) PAC.

III. Prediction and measurement of highway traffic noise (Contd.) b c a d Figure. Comparison between the measured and the predicted A-weighted SPLs at 1.2 m receiver point above ground level and 7.5 m horizontal distance from a lane center in the case of PCC pavements: (a) 30 mm TPCC; (b) 18 mm LPCC; (c) 25 mm LPCC; (d) DGPCC.

Individual road surface IV. Conclusions Absolute mean |L| and standard deviation L (in dBA) of the value of the measured SPLs subtracted from the predicted SPLs overall Vehicle categorization Statistic (dBA) Individual road surface All road surfaces DGA SMA PAC 30 mm TPCC 18 mm LPCC 25 mm LPCC DGPCC Category I |L| 0.73 0.78 0.50 0.62 0.58 1.94 0.92 L 0.66 0.87 0.51 0.77 0.70 0.69 1.0 Category II 0.75 0.54 0.47 0.65 0.39 2.30 1.20 0.76 0.67 0.43 0.31 Category III 1.18 1.39 1.01 0.52 0.99 1.32 0.79 0.40 1.12

IV. Conclusions (Contd.) In this study, a PWL measurement method, a traffic noise prediction model and vehicle categorization are introduced to determine a vehicle’s PWL, effectively implement ISO 9613-2 with some improved techniques, and to assign an appropriate vehicle categorization among the possible three categories. A comparison of the results of the measured and predicted noise levels with respect to overall value and octave bands shows good correspondence when the four-vehicle category classification, Category II, is used. Therefore, the proposed model can be effectively applied not only to predict noise but also to consider various road surface types as part of the prediction process.