NOISE LEVEL STUDY BASED ON TRAFFIC CHARACTERISTICS, PHYSICAL AND ENVIRONMENTAL ASPECTS OF ROAD

International Journal of Civil Engineering and Technology (IJCIET) Volume 7, Issue 1, Jan-Feb 2016, pp. 188-198, Article ID: IJCIET_07_01_016 Available online at http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=7&itype=1 Journal Impact Factor (2016): 9.7820 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6308 and ISSN Online: 0976-6316 IAEME Publication NOISE LEVEL STUDY BASED ON TRAFFIC CHARACTERISTICS, PHYSICAL AND ENVIRONMENTAL ASPECTS OF ROAD Irwan Lakawa Doctoral Student, Civil Engineering Dept, Faculty of Engineering, Hasanuddin University, Indonesia Lawalenna Samang Professor, Civil Engineering Dept., Faculty of Engineering, Hasanuddin University, Indonesia Mary Selintung Professor, Civil Engineering Dept., Faculty of Engineering, Hasanuddin University, Indonesia Muralia Hustim Senior Lecturer, Civil Engineering Dept., Faculty of Engineering, Hasanuddin University, Indonesia ABSTRACT This study aims to construct an interaction model among traffic characteristics, physics of road, and road environment, and the effect implications on noise level. The study was conducted on arterial and collector roads becoming the main line of traffic movement in Kendari city. Analytical approach used descriptive method and simultaneous equation system based on Partial Least Square (PLS). The results show that the traffic noise level has exceeded the threshold in accordance with designation of settlement environment as well as trade and services, in which the average noise level at the side of arterial and collector roads is 75.5 db and 73.4 db. Simultaneously, traffic characteristics, physical and environmental aspects of road have effects on noise with the determination coefficient (R²) 0.628. Traffic and road environment factors are significant direct effect on noise level. While the physical of road does not have significant direct effect. In this case, physical of road is more as a intervention factor triggering noisy through its effect on changes in traffic characteristics. Key words: Noise, Traffic, Physical, Environment, Model http://www.iaeme.com/ijciet/index.asp 188 editor@iaeme.com

Noise Level Study Based on Traffic Characteristics, Physical and Environmental Aspects of Cite this Article: Irwan Lakawa, Lawalenna Samang, Mary Selintung and Muralia Hustim, Noise Level Study Based on Traffic Characteristics, Physical and Environmental Aspects of, International Journal of Civil Engineering and Technology, 7(1), 2016, pp. 188-198. http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=7&itype=1 1. INTRODUCTION The rapid growth of motor vehicles nowadays would lead to the increasing problem of environmental noise. In general, motor vehicles are a major source of noise emissions in an urban environment, contributing 55% of total noise (Nirjar et al., 2003; Banerjee et al., 2008). At a certain intensity, noise can disturb the health and comfort of road users and the people in the surrounding settlements. The disturbance is in the form of psychological disturbance which is likely to lead to stress and hearing loss. The question now is how to control the traffic noise. During this time, the control efforts are performed using traffic engineering. The strategy is ultimately seen less than maximum, because vehicles will continue to grow over time. Therefore, to obtain handling solutions, it is first necessary to know what factors affecting the intensity of traffic noise on highway. Noise characteristics of a road must be different to the noise characteristics in another road. The difference occurs because the noise level is influenced by a number of parameters such as traffic, physics of road, environment, temperature, and residence (Handayani., 2007). Several previous studies tried to identify these factors, among others Sooriyaarachchi (2008) discussed building traffic noise prediction model based on the speed and distance of various types of vehicles. Golmohammadi (2009) did a comparison of some models in predicting traffic noise based on the conditions of the cities in Iran with the independent variables of road width and length, flatness, building height, vehicle volume, speed, and truck percentage. Wedagama (2012) made a simple and multiple regression model of the effect of local traffic on noise levels, using independent variables of traffic volume, speed, and observation distance. Murase et al., (2000) constructed a structural model of priority lanes comparison of community disturbance in Sweden, Japan and Thailand based on non-acoustic factors on traffic noise. Agarwal (2010) got the data of motor vehicles in Jaipur city (India) consisting of two wheels 72%, followed by car/jeep (15%), three wheels (12%), and the remaining, buses and trucks 1%. The results of analysis stated that light vehicles were a major source of noise pollution in urban areas although the composition was less than two-wheeled vehicles. Another study in Kendari (Indonesia) found that heavy vehicles with a composition of 4% had a significant role in triggering the noise, while in the composition of 3%, the noise was predominantly affected by motorcycles and light vehicles whose composition was 97% (Lakawa et al., 2015). A study was conducted in Makassar (Indonesia), measuring the sound power level of motorcycles with 110cc engine capacity and speed of 20-40 km/h. Statistical analysis showed that the noise levels had a significant relationship with speed (Hustim & Fujimoto, 2012). In general, the studies focus on the aspects of traffic. In theory, the sound produced by motor vehicles will spread with equal strength in all directions. Therefore, the intensity of noise on the highway is not only influenced by traffic factors such as volume and speed, but also the physical and environmental aspects of road. However, from a number of factors, it has not been identified which factors affecting directly and indirectly. http://www.iaeme.com/ijciet/index.asp 189 editor@iaeme.com

Irwan Lakawa, Lawalenna Samang, Mary Selintung and Muralia Hustim 2. METHODOLOGY 2.1. Research Object The research was conducted on arterial and collector roads becoming the main line of traffic movement in Kendari city, Southeast Sulawesi (Indonesia). Criteria the sample location used incidental sampling technique based on the variation of noisy triggering factors (traffic, physical and environment aspects of road,) by considering land use on the road side. The number of observation sites was 49 points, each of which it was placed two measuring instruments of SLM microphones at a distance of 1m and 6m from the road side. Survey of noise, volume, and vehicle speed were conducted for 10 minutes. While, the observation area of physical and environmental of road condition was 25m at each sample point. Figure 1 Research Location 2.2. Analysis Approach The analysis approach used descriptive method and Partial Least Square (PLS) based structural equation modeling. Descriptive method was used to analyze and describe the characteristics of indicators forming the model, while the structural equation (SEM-PLS) aimed at predicting the interaction between the factors in the model. The use of SEM-PLS in this study was compared to the covariance-based SEM because of the consideration of sample number, data were not normally distributed multivariate, data unit were not the same, and the main substance of the study was aimed at examining the predictive relationship or effects among the constructs. http://www.iaeme.com/ijciet/index.asp 190 editor@iaeme.com

Noise Level Study Based on Traffic Characteristics, Physical and Environmental Aspects of 2.3. Partial Least Square (PLS) PLS was an alternative approach shifting from covariance-based SEM into variantbased SEM. PLS analysis consisted of two sub-models, namely structural model (inner model) describing the strength of the relationship among factors (latent variable), while measurement model (outer model) described the indicator power in measuring latent variable. Model testing (goodness of fit) in the PLS consisted of measurement model evaluation and structural model. The testing basis to meet the criteria of validity and reliability in measurement model was loading factor value > 0.5, the evaluation of structural model was based on R-square value of each dependent construct as predictive power, Q-square was to measure how well the observation values generated by the model (predictive relevance), as well as t-test was to see the significance of structural path coefficient. The confidence level used in this study 95%, therefore, the opportunity of error or significance level (α) was 5% with the value of t-table 1.96. To simplify the analysis of the model, it was used SmartPLS 2.0M3 software. The conceptualization of the structural model is as shown in Figure 2. Figure 2 Conceptualization of Structural Model Based on Figure 2, the simultaneous equations system of interaction relations of traffic, road physical, road environment, and noise level is: FJ = γ 1 LJ + ε 1 (1) LL = β 2 FJ + γ 2 LJ + ε 2 (2) NL = β 1 FJ + β 3 LL + γ 3 LJ + ε 3 (3) Where, FJ is physical aspect of road, LL is traffic, LJ is road environment, NL is noise level, γ 1,2,3 is exogenous variable effect coefficient on endogenous variable, and β 1,2,3 is endogenous variable effect coefficient on endogenous variable, and ε 1,2,3 : error model. http://www.iaeme.com/ijciet/index.asp 191 editor@iaeme.com

Irwan Lakawa, Lawalenna Samang, Mary Selintung and Muralia Hustim 3. RESULTS AND DISCUSSION 3.1. Noise Characteristics The analysis shows that the most frequency of roadside noise value of 32.7% is at the level of 75.1-76 db. While at a distance of 6m from the side of road pavement (yard) of 22.4% is at the level of 71.1-72 db. Conditions of noise levels on arterial roads and collector of each location can be seen in Figure 3. Figure 3 Noise Level Based on Figure 3, the noise level has exceeded the threshold of designation of settlement environment (NAB1) and trade/services (NAB2). The average noise level at the roadside is 75.5 db (arterial) and 73.4 db (collector). While on yard (6m from the side of pavement), the average is 70.9 db (arterial) and 69.7 db (collector). The difference in noise levels of roadside and yard is 1.5 to 7.4 db. The main factor causing the reduction in value is distance, because the sound produced from the source will be weaker along with increasing distance. On the other hand, road physical and environmental factors also affect the noise intensity. 3.2. Traffic Characteristics Traffic characteristics consist of traffic volume, speed, density, and composition of vehicles. The average traffic volume on arterial road is 2609 veh/h, while on collector road is 1900 veh/h. http://www.iaeme.com/ijciet/index.asp 192 editor@iaeme.com

Noise Level Study Based on Traffic Characteristics, Physical and Environmental Aspects of Figure 4 Traffic Volume The composition of traffic on arterial and collector roads are dominated by motorcycles (63%), followed by light vehicles (33%), and heavy vehicle (4%), with an average speed of motorcycle is 26 km/h, light vehicle is 23 km/h, and heavy vehicle is 21 km/h. The low speed is not only caused by inadequate road capacity, but also due to the condition of the pavement and driver behaviors that do not push ahead the vehicles. 3.3. Physical Characteristics Widht of road on the arterial varies between 6-16m and the collector is 4.5-14m with an average superelevation of 2.8%. The most flat roads are in AS3 region with the flatness of 0.6-1%. While the road section that is slightly sloping is in AS2 region with the slope of 0.4%-8%. Of the 49 samples of observation location, it is identified road types on the arterial road that are 2/2UD (13 points), 4/2UD (7 points), and 4/2D (14 points), while on the collector road are 2/2UD (20 points), 4/2UD (8 points), and 4/2D (2 points). The condition of pavement is assessed based on SDI (Surface Distress Index) criteria. The analysis results of pavement conditions are shown in figure 5. One factor that causes low vehicle speed is condition of the pavement. Figure 5 Pavement Condition http://www.iaeme.com/ijciet/index.asp 193 editor@iaeme.com

Irwan Lakawa, Lawalenna Samang, Mary Selintung and Muralia Hustim 3.4. Environment Characteristics The road environment characteristics consist of building density, tree density, leaf shade volume, types of road environment ground surface, and temperature. The average building density is between 20-100%. However, there are some locations in which there are no buildings. It is intended as a comparison to see the effect of reflection of noise from the building wall. Variation of tree density is between 18-88% with the volume of leaf shade between 3m³-259m³, but there are some locations in which there are trees. It is intended as a comparison to see the effect of tree density on noise level. Measurement temperatures fluctuate between 30 C - 38 C. In Figure 6 it can be seen clearly that the type of road environment surface is dominated by soil (41%), concrete rebate 29%, grass 20% and paving-block (10%). Concrete Rebate (29%) Grass (20%) [CATEGORY NAME] (10%) Soil (41%) Figure 6 Environment Surface Types 3.5. Structural Model Analysis Based on the identification of relationship between indicators and latent variables, it is concluded that the measurement model is reflexive, so the indicator is evaluated with convergent and discriminant validity. While the indicator block is evaluated with composite reliability. The research variables and indicators are as shown in Table 1. Table 1 Variables and Indicators Variables/Constructs Indicator Code Traffic volume Vol Composition of motorcycle KoMC Traffic Characteristics Composition of light vehicles KoLV (LL) Composition of heavy vehicles KoHV Average speed Vav Traffic density Dtr Width of Lja Superelevation Spel Physical (FJ) Gradient Grad Type of road Typ Pavement condition Kond http://www.iaeme.com/ijciet/index.asp 194 editor@iaeme.com

Noise Level Study Based on Traffic Characteristics, Physical and Environmental Aspects of Variables/Constructs Indicator Code Building density Dbg Tree density Dph Environment (LJ) Leaf Lush Volume Vda Type of ground surface Pbu Temperature Su Noise exceeded L 10 ExL 10 Noise exceeded L 50 ExL 50 Noise Level (NL) Noise exceeded L 90 Equivalent continous noise ExL 90 L eq (a) Output 1 SmartPLS (b) Output 2 SmartPLS Figure 7 Measurement Model Evaluation Figure 7(a) it can be seen that there are several indicators in the model having loading factor value under 0.50. For instance, pavement condition (Kond), superelevation (Spel), building density (Dbg), temperature (Su), and light vehicle composition (KoLV). While from noisy variable, all indicators are valid as they are above 0.50. Therefore, indicators having loading factor value below 0.50 are dropped from the model. From the results of model execution in Figure 7(b), it appears that all indicators have loading factor values above 0.50, meaning that it meets the criteria of convergent validity. http://www.iaeme.com/ijciet/index.asp 195 editor@iaeme.com

Irwan Lakawa, Lawalenna Samang, Mary Selintung and Muralia Hustim Table 2 Result for Cross Loadings Discriminant Validity Noise Level Physical Traffic Environment DTr 0.686 0.219 0.926-0.072 Dph -0.145-0.339-0.105 0.911 ExL10 0.903 0.228 0.66-0.099 ExL50 0.963 0.226 0.771-0.172 ExL90 0.882 0.224 0.787-0.113 Grad 0.131 0.648 0.215-0.228 KoHV 0.334 0.126 0.564 0.095 KoMC 0.275 0.089 0.581 0.221 Leq 0.922 0.19 0.624-0.13 Lja 0.204 0.899 0.213-0.297 Pbu -0.17-0.175-0.024 0.571 Vav 0.319-0.1 0.553-0.014 Typ 0.242 0.933 0.307-0.407 Vda -0.053-0.378 0.062 0.881 Vol 0.814 0.413 0.861-0.116 In Table 2, it is showed that the correlation value of indicators on the variable is higher than the value of correlation on the other variables. These results indicate good discriminant validity, meaning that the indicators are valid and can be used to measure each of the variables. For example, the indicator of traffic density (DTr) has the highest value of cross loading on the traffic construct, that is 0.926 compared to other constructs such as; noisy (0.686), physical of road (0.219) and environment (- 0.072). This suggests that the density (DTr) indicator is really a traffic indicator. Similar results can also be seen in other indicator, namely motorcycle composition (KoMC) that has the highest loading factor on traffic that is 0.581 compared to the value of cross loading on noisy variable 0.275, physical of road 0.089, and road environment 0.221. Table 3 Test Results of Composite Reliability, Cronbach s Alpha, R-Square Composite Variables/Constructs Cronbach s Alpha R² Reliability Noise Level 0.955 0.938 0.628 Physical 0.872 0.775 0.148 Traffic 0.818 0.737 0.098 Environment 0.839 0.709 In Table 3, the value of composite reliability and Cronbach's alpha is > 0.7, meaning that all the variables in the estimated model meet the reliability criteria. The value determination coefficient (R²) for noisy variable is 0.628, meaning that simultaneous the variability of noise levels can be explained by the three factors in the model, namely traffic, physical of road, and road environment by 62.8%, while 37.2% is explained by other factors not examined in this model. R-square value is also contained in physical of road variable which is influenced by road environment 14.8%, and traffic variable affected by physical of road and road environment 9.8%. From the results of predictive relevance calculation, it is obtained Q² value by 0.4. This suggests that the resulting model has fairly good prediction level, in which the value of Q² is in the range of 0-1. The closer to 1, the better the predictive relevance. The interactions among noise trigger variables and the effect on noise are identified based on t-stat value through bootstrapping procedure. In table 4, it http://www.iaeme.com/ijciet/index.asp 196 editor@iaeme.com

Noise Level Study Based on Traffic Characteristics, Physical and Environmental Aspects of appears that there are two hypothesis having t-stat value smaller than t-table (1.96), they are the physical of road.on noise and road environment on traffic. Therefore, both hypotheses are rejected, meaning that the hypotheses do not have significant direct effect on the endogenous variables. The factors having direct effect on noise level are only traffic and road environment with t-stat value of 13.842 and 2.643 > 1.96. On the other hand, road environment factor has significant effect on physical of road with t-stat value of 3.496. Hypothesis Table 4 Path Coefficients (STDEV, T-statistic, P-values) Standard Deviation (STDEV) T Statistics ( O/STERR ) P-Values Discription Physical -> Noise 0.085 0.629 0.529 H1 rejected Physical -> Traffic 0.142 2.386 0.017 H1 accepted Traffic -> Noise 0.057 13.842 0.000 H1 accepted Env. -> Noise 0.085 2.643 0.012 H1 accepted Env. -> Physical 0.110 3.496 0.001 H1 accepted Env. -> Traffic 0.221 0.462 0.645 H1 rejected Figure 8 Interaction models of Traffic Factor, Physical, Environment, and Noise Level 4. CONCLUSIONS The noise level of traffic in Kendari city has exceeded the threshold in accordance with designation of settlement environment as well as trade and services, in which the average noise level at the side of arterial and collector roads is 75.3 db and 74.1 db. While on yard (6m from the roadside), the average is 70.9 db (artery) and 69.7 db (collector). Simultaneously, the characteristics of traffic, physical aspects of road, and road environment have effects on noise with the determination coefficient (R²) 0.628. Factors of traffic and road environment are significant factor having direct effect on noise level. While the physical of road does not have significant direct effect. In this case, the physical of road is more as a intervention factor triggering noisy through its effect on changes in traffic characteristics. On the other hand, among the factors triggering noisy themselves, they have interaction relationship, such as physics of road on traffic and road environment on physical of road. http://www.iaeme.com/ijciet/index.asp 197 editor@iaeme.com

Irwan Lakawa, Lawalenna Samang, Mary Selintung and Muralia Hustim REFERENCES [1] Agarwal, S. & Swami, B.L., (2011). Comprehensive approach for the development of traffic noise prediction model for Jaipur city. Environment Monitoring and Assessment; DOI: 10.1007/s 10661-010-1320-z. 172, pp: 113-120. [2] Banerjee, D., Chakraborty, S. K., Bhattacharyya, S., Gangopadhyay, A., (2008). Evaluation and Analysis of Traffic Noise in Asansol: An Industrial Town of Eastern India. International Journal of Environmental Research, Public Health, Vol. 5(3), pp: 165 171. [3] Golmohammadi, R., Abbaspour, M., Nassiri, P., Mahjub, H. (2009). A compact model for predicting road traffic noise. Journal Environ Health Sci. Eng, Vol. 6(3), pp: 181-186. [4] Handayani, Rr. Dini. (2007). Pengkajian Faktor - Faktor Tingkat Kebisingan Jalan Perkotaan. Jurnal Puslitbang Jalan dan Jembatan. Vol. 24(2). [5] Hustim, M & Fujimoto, K., (2012). Power Level of Motorcycle in Makassar City, Indonesia. Journal of Architectural and Urban Design. Kyushu University. No. 22. pp: 91-96. [6] Lakawa, I., Samang, L., Selintung, M., Hustim, M., (2015). Relationship Models of Traffic Volume Vs Noise Level in Arterial and Collector s. International Journal of Development Research, Vol. 5(9), pp: 5463-5466. [7] Murase, S., Sato, T., Yano, T., Bjorkman, M., Rylander, R., Dankittikul, W. (2000). Comparison of Path Models of Traffic Noise Annoyance in Sweden, Japan and Thailand. Proceeding. WESTPRA VII. The Seventh Western Pacific Regional Acoustics Conference. Kumamoto, Japan, 3-5 October. [8] Nirjar, R. S., Jain, S. S., Parida, M., Kartiyar, V. S., Mittal, N. (2003). A Study of Transport Related Noise Pollution in Delhi. Journal of the Institution of Engineers, Vol. 84(1), pp: 6 15. [9] Dr. B. Santhaveerana Goud Sachinkumar Savadatti and Prathibha D, Application of Caline 4 Model To Predict Pm2.5 Concentration at Central Silk Board Traffic Intersection of Bangalore City, International Journal of Civil Engineering and Technology, 6(10), 2015, pp. 191-200. [10] Dr. Md. Shamsul Hoque, Mohammad Ahad Ullah and Dr. Hamid Nikraz, Investigation of Traffic Flow Characteristics of Dhaka-Sylhet Highway (N-2) of Bangladesh, Physical and Environmental Aspects of, International Journal of Civil Engineering and Technology, 4(4), 2013, pp. 55-65. [11] Sooriyaarachchi, R.T & Sonnadara, D.U.J. (2008). Modelling Free Flowing Vehicular Traffic Noise. Engineer. Vol. 40(2), pp: 43-47. [12] Wedagama, D.M.P. (2012). The Influence of Local Traffic on Noise Level (Case Studi: By Pass Ngurah Rai and Sunset, Bali). Bumi Lestari Journal of Environment. Vol. 12(1), pp: 24-31. http://www.iaeme.com/ijciet/index.asp 198 editor@iaeme.com