第 55 卷 第 5 期
2020 年 10 月
JOURNAL OF SOUTHWEST JIAOTONG UNIVERSITY
Vol. 55 No. 5
Oct. 2020
ISSN: 0258-2724 DOI:10.35741/issn.0258-2724.55.5.30
Research article
Environmental Sciences
N
OISE
C
AUSED BY THE
V
OLUME OF
P
UBLIC
T
RANSPORT AND
P
RIVATE
T
RANSPORTATION DURING
COVID-19
新冠肺炎期间公共交通和私人交通量造成的噪音
Syaiful Syaiful a, b, *, Iswahyudi Iswahyudi a
a Civil Engineering Department, Ibn Khaldun University Bogor
Jl. KH. Sholeh Iskandar KM 2 Kedungbadak Tanah Sareal, Bogor, Indonesia, syaiful@ft.uika-bogor.ac.id
b
Multidisiplinary Doctoral Program IPB University and Awardee BUDI-DN-LPDP Kemenristekdikti RI Gedung Sekolah Pascasarjana Lt. 1, Kampus IPB Dramaga Bogor, 16680, Indonesia, sps@ipb.ac.id
Received: June 5, 2020 ▪ Review: September 17, 2020 ▪ Accepted: October 13, 2020
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
Abstract
This article explains the low noise that originates from the volume of public transportation and private transportation during COVID-19 (few vehicles are passing). The method used is surveying traffic data in the field (the noise is measured when the vehicle passes the roadside of the survey location), including when COVID-19 hits the city. This study refers to several cities, including the city of Bogor as the object of research. Traffic movements have slowed down due to the COVID-19 outbreak. This field method is effective in describing the current traffic conditions. In the findings, the indicator used to determine the vehicle volume noise contributes 12.10% of the calculation result of y = 70.718 + 0.013 x 1. The result is
a significant effect seen on sound level meter (SLM) 3, with an increase of 70.718 dBA, distance from the
sidewalk to the nearest building wall of 10.24 m, and vehicle speed of 80 km/hour. The results of this study will enhance the development of science and engineering technology as a contributor to cooperative support of government policies to comply with health protocols in this COVID-19 era.
Keywords: Noise of Effect, Volume, Public Transportation, Private Transportation, COVID-19
摘要本文介绍了在新冠肺炎(少量车辆通过)期间源自公共交通和私人交通量的低噪音。所使用 的方法是在野外调查交通数据(当车辆通过调查地点的路旁时测量噪音),包括当新冠肺炎撞到 城市时。本研究涉及多个城市,其中以茂物为研究对象。由于新冠肺炎的爆发,交通流量已经放 缓。该现场方法可有效描述当前的交通状况。在调查结果中,用于确定车辆噪音的指标占 y = 70.718 + 0.013 x 1 的计算结果的 12.10%。结果对声级计(SLM)3 产生了显着影响,增加了 70.718。分贝,从人行道到最近的建筑物墙的距离为 10.24 米,车速为 80 公里/小时。这项研究的 结果将促进科学和工程技术的发展,为在此新冠肺炎时代合作支持政府政策以符合卫生协议做出
贡献。
关键词: 效果噪声,音量,公共交通,私人交通,新冠肺炎
I. I
NTRODUCTIONThe amount of noise due to traffic volumes is an important consideration in the field of transportation, but the COVID-19 pandemic of 2020 has significantly altered traffic patterns. The initial concept of this research is to calculate the travel of people and vehicles from one place to another alternately (i.e., travel by foot or in
non-motorized vehicles such as bicycles or
BECAK/rickshaws (for a short distance)). This travel was carried out when COVID-19 was prevalent in Indonesia [2]. COVID-19 has changed the pattern of population activities. Of the many working outside now working at home. From school to home school, transacting at the mall, now shopping online shop. All these activities use vehicles to travel, with a significant reduction in the number of vehicles on the road, the noise due to motorized vehicles is also reduced.
Sound is a longitudinal wave that propagates through a medium. The sound propagation medium can be a solid, liquid, or gas. Sound comes from a sound source, which is vibrated by energy (energy from within) or energy that is then emitted out (energy that goes outward) [1], [7]. If vibrations reach the ears, sound will be heard [2]. The vibration from the sound source propagates through an intermediate agent in the form of density and strain. With increasing age, the human ear will be less sensitive to high-frequency sounds [8], [9]. The velocity of sound propagation in air is 1.224 km/hour, and this
velocity will accelerate with increasing
temperature and air pressure because of an increased number of air particles. Sound cannot be heard in a vacuum because it requires intermediates in its propagation [10], [13]. The wave velocity varies for each medium. For the same type of medium, several factors, such as the source geometry, the state of the surrounding atmosphere, and surface effects influence the propagation of sound waves [11]. The geometry of the sound wave will be spherical if the sound source is a point. The sound comes from industrial activities. When the sound source makes a sound, the sound energy will automatically spread out simultaneously in all directions, and the longer it will get away from the sound source. Wave geometry is in cylindrical form. It will happen when the source
of the sound waves is a line like on a highway. As a result of the spread that occurs the sound waves will lose several decibels of energy [12], [16].
The sound wave geometry will be spherical if the sound source is a dot. The sound comes from industrial activities [3]. When viewed from the source of the sound, everything that makes a sound will have the same power. One of them is geometry. When the sound source makes a sound, the sound energy will automatically spread in all directions at once, and the longer the distance from the sound source. Conversely, cylindrical wave geometry occurs when the source of the sound waves is a line, such as on a highway. Due to the dispersion that occurs, sound waves will lose some of the energy decibels [5], [15].
There are sound intensity limits that can be perceived by normal humans. On the one hand, these include the smallest intensity that can cause stimulation in the human ear, which is 10–12
w/m2; this is called the hearing threshold
intensity [21]. On the other, the greatest intensity that can still be heard by the human ear without pain is 1 w/m2; this is called the pain threshold [4]. What is meant by the noise level quality standard is the maximum limit of noise level that can be dispersed to the environment of a business or activity so that it does not cause disturbance to human health and environmental comfort [14]. The energy produced is the same as intermittent noise energy in one period or time interval of measurement and can be used on all noise level fluctuations [17], [29].
The recommended and the maximum allowed noise levels are the average mode values of the noise levels during the day, evening, and night. The maximum value of noise generated when the data is taken is different, morning, noon, evening or night. The level of ambient noise (background noise level) is the average minimum sound level in a state without noise disturbance at the place and when measurements are taken, if the value taken from the distribution is 95% of the back ground noise level or Level-95. Sounds arising from transportation activities are sounds that are not constant [6]. Noise depends on the level of sound intensity, how often it occurs, and the frequency produced [30]. If the noise is too high, it can be mitigated by adjusting the speed of the
vehicle, keeping the distance and the vehicle using natural gas.
Noise in motor vehicles is mainly generated by vehicle engines during combustion, exhaust emission, use of horns, and braking, as well as from interactions between the wheels and the road in the form of friction, which produces sound [18], [19]. Most motorized vehicles in gear 2 or 3 produce 75 dBA of noise, with a frequency of 100–7,000 Hz [20]. When the engine is turned on and engaged in maximum acceleration, noise is produced by the engine; if the vehicle is traveling at high speed, the friction between the wheels of the vehicle and the road surface will cause noise [23], [31].
Many people take the COVID-19 case lightly. They do not care about the government's appeal not to travel outside the house using a vehicle when the Pandemic hits. A government program to break the chain of spreading the coronavirus is reducing direct contact with people and objects.
Besides conducting research in the field of public transportation and private transportation traffic, researchers find specific activities that challenging [22], [24], [25]. The toughest challenge is that, during the COVID-19 pandemic, public transportation trips have drastically reduced. This reduction is a direct impact of COVID-19. A decrease in COVID-19 can occur
if residents comply with government
recommendations not to travel during a pandemic. The journey of residents will result in many people being affected by COVID-19 through dirty air, motor vehicle noise, gathering with friends, colleagues and business partners. Doing Work from Home (WFH) will cut the spread of the coronavirus. Thus, it appears that commuters’ journey is relatively quiet in terms of motorized vehicle traffic. People and vehicles tend to move according to patterns, which will be helpful in carryout out the research. Measured noise in one place during the COVID-19 pandemic will show the performance of traffic criteria under COVID-19, clarifying its effect on people’s travel. The traffic criteria are based on existing regulations from the Government of Indonesia regarding noise level quality standards [2], also based on the researches [19], [27], [32], that the fewer vehicles passing in one place, the more noise which will result is also small. It is hoped that the motivation for travel on public transportation will balance the journey of private transportation [26], [27]. People will take private transportation like cars instead of public transportation like busses, and this could increase the noise. It is advisable to use private transportation if one has to, to reduce the spread of COVID-19. Researchers
compare these criteria to obtain balanced research results; this will allow recommendations that will become unique criteria as a travel model for everyone during the COVID-19 period [28], [32]. The Indonesian government is adopting a new life order by reducing travel if not forced or carrying out independent lockdowns.
There is no vaccine yet. Medical and health sciences are working to create antibodies for COVID-19 so that victims do not increase every day. The world of health care is working on creating a treatment for COVID-19 so that the victims of this disease do not multiply every day. This statement is an integral part of the program to support the Government of Indonesia not to travel by motorized vehicle if it is not forced at all, to reduce the volume of vehicles on the road.
A. Public Transportation
The definition of public transportation according to the traffic law is transportation that is free of charge for its users. The concept of public or public transportation arises because not all citizens have private vehicles so the state is obliged to provide transportation for the community as a whole [1], [2].
B. Private Transportation
One of the characteristics of private transportation is the freedom to determine the trajectory and the travel time. Private vehicles and public transportation have a high mobility of movement, thereby increasing a person’s ability to carry out activities [2].
C. Freight Transport
Unlike when people travel, goods are generally transported for longer distances, for fewer customers, and with more variety. Types of goods have different volumes and weight ratios, as well as various characteristics that may demand a specific transportation system [2], [5].
II. R
ESEARCHM
ETHODOLOGYThis section will present the research methodology and research chart based on Figure 1.
Figure 1. Research location
Furthermore, the research flowchart,
displayed from start to finish, can be seen in Figure 2.
Figure 2. Flowchart of research method
III. R
ESULTS ANDD
ISCUSSIONThe results of data collection on traffic volume of public and private transportation, obtained in the field, are illustrated in Figure 3.
Figure 3. Traffic volume results 1, 2, 3 and 4 A. Traffic Volume Data Results 1, 2, 3 and 4
On the first day, the highest volume of public transport traffic was 824.0 vehicles/hour, which occurred between 12:15–12:30, while for private transport traffic it is 11.304 vehicles/hour, which
occurred between 07.30–07.45. The lowest volume results for public transport traffic is 123.4 vehicles/hour, between 17:45–18:00 hours, while for private transport traffic it is 1.296.0 vehicles/hour, between 06.00–06.15. The average volume of traffic for public transport is 464.66 vehicles/hour, and 4.820.94 vehicles/hour for private transport. On the second day, the highest volume of public transport traffic was 668.0 vehicles/hour, which occurred between 06.45– 07.00, while for private transport traffic it was 8.216.0 vehicles/hour, between 16:45–17:00. The lowest volume results for public transport traffic, of 161.8 vehicles/hour, occurred between 17:45– 18:00, while for private transport traffic it was 2.332.0 vehicles/hour, between 09.45–10:00. The average volume of traffic for public transport is 406.71 vehicles/hour, and 5.135.49 vehicles/hour for private transport traffic. The highest volume results for public transport traffic obtained on the third day was 828.0 vehicles/hour, which occurred between 16:30–16:45, while for private
transportation traffic it was 15.208,0
vehicles/hour, between 06.30–06.45. The lowest volume results on public transport traffic amounted to 74.4 vehicles/hour, between 17:45– 18:00, while for private transport traffic it was 1.555.5 vehicles/hour, between 17:45–18:00. The average volume of traffic for public transport is 407.29 vehicles/hour, and 5.612.27 vehicles/hour for private transport traffic. The highest volume results for public transport traffic obtained on the fourth day was 1.032.0 vehicles/hour, which occurred between 11:45–12:00, while for private transportation traffic it was 9.580.0 vehicles/hour, between 06.30–06.45. The lowest volume results
on public transport traffic was 116.9
vehicles/hour, which occurred between 17:45– 18:00, while for private transport traffic it was 1.084.0 vehicles/hour, between 12:15–12:30. The average volume of traffic for public transport is 451.92 vehicles/hour, and 4.868.35 vehicles/hour for private transport traffic.
B. Noise Data Results 1, 2, 3 and 4
On the first day the highest noise results
obtained in SLM1 amounted to 100.0 dBA, which
occurred at 06.45–07.00, in SLM2 amounted to 90.5 dBA which occurred at 09.45-10:00, and in
SLM3 amounted to 84.0 which occurred at 13.00-13.15. For the lowest noise results in
SLM1 amounted to 79.7 dBA, which occurred at
10.00-10.15, in SLM2 amounted to 69.9 dBA
which occurred at 11:30-11:45, in SLM3 amounted to 66.6 which occurred at 14:45-15:00.
The average noise level in SLM1 is 89.0 dBA, in
The second day the highest noise results obtained
in SLM1 amounted to 93.1 dBA, which occurred
at 16.00-16.15, in SLM2 amounted to 84.7 dBA
which occurred at 11.00-11.15, and in SLM3 amounted to 81.2 which occurred at 17.15-17.30. For the lowest noise results in SLM1 amounted to 77.7 dBA, which occurred at 12:15-12:30, in
SLM2 amounted to 66.4 dBA which occurred at
10.30-10.45, in SLM3 amounted to 61.0 which occurred at 15.00-15.15. The average noise level in SLM1 is 84.9 dBA, in SLM 2 it is 75.9 dBA,
and in SLM3 it is 70.4 dBA. The third day
obtained the highest noise results in SLM1 of
94.6 dBA, which occurred at 07.00-07.15, in
SLM2 amounted to 87.2 dBA which occurred at
15.45-16.00, and in SLM3 amounted to 80.8 which occurred at 06.30-06.45. For the lowest
noise results in SLM1 amounted to 77.7 dBA,
which occurred at 12:15-12:30, in SLM2
amounted to 66.4 dBA which occurred at 10.30 -
10.45, in SLM3 amounted to 63.2 which occurred at 12:30-12:45. The average noise level in SLM1 is 85.7 dBA, in SLM2 it is 76.9 dBA,
and in SLM3 it is 70.5 dBA. The fourth day
obtained the highest noise results in SLM1 of 96.4 dBA, which occurred at 07.00 - 07.15, in
SLM2 amounted to 88.7 dBA which occurred at
13.45-14.00, and in SLM3 amounted to 78.8 which occurred at 13.15-13.30. For the lowest
noise results in SLM1 amounted to 75.6 dBA,
which occurred at 09.15-09.30, in SLM2 amounted to 68.6 dBA which occurred at
12:45-13:00, in SLM3 amounted to 64.5 which occurred at 12.00-12.15. The average noise level in SLM1 is 85.8 dBA, in SLM2 it is 77.8 dBA,
and in SLM3 it is 71.1 dBA.
Figure 4. Noise results 1, 2, 3 and 4 C. Monday/The First Day
1) The Results of SLM1 Multiple Regression Statistical Analysis a Distance of 0.00 m from the Highway
Table 1.
Summary of the relationship of the volume of public transport and private transportation with noise on the first day in SLM1
Model R R square Adjusted R square Std. error of the estimate Change statistics
R square change F change df1 df2 Sig. F change
1 .027 a .001 -.044 5.01103 .001 .017 2 45 .983
a
Predictors: (Constant), volume of private transportation, volume of public transportation b
Dependent variable: POSITION SLM1 Table 2.
Anova relationship of the volume of public transport and private transportation with noise on the first day in SLM1 Model Sum of squares df Mean square F Sig. 1 Regression .837 2 .419 .017 .983 a Residual 1129.971 45 25.110 Total 1130.808 47 a
Predictors: (Constant), volume of private transportation, volume of public transportation
b
Dependent variable: POSITION SLM1
Research the relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM1 (y) located from the edge of the highway with a distance of 0.00 meters, with a 95% confidence level and a
probability value of 0.05 or 5%. The calculation
results obtained mean for SLM1 of 89.0063 dBA,
for public transportation 465 vehicles/hour and private transportation for 4.821 vehicles/hour. The validity of SLM1 analysis results, the volume of public transport and private transport has a value of one which means that all data is valid.
2) The Results of SLM2 Multiple Regression Statistical Analysis a Distance of 5.12 m from the Highway
The results of the study the relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM2 (y) located from the edge of the highway with a distance of 5.12 meters, with a confidence level of 95% and a probability value of 0 .05 or 5%. The calculations that get the mean value for SLM2 of 77.9896 dBA, for public transportation
465 vehicles/hour and private transportation for 4821 vehicles/hour. The validity of the results of SLM2 analysis, the volume of public transport
and private transport has a value of one which means that all data is valid.
Table 3.
Summary of the relationship of public transport volume and private transportation with noise on the first day on SLM2
Model R R square Adjusted R square Std. error of the estimate Change statistics R square
change F change df1 df2 Sig. F change
1 .129 a .017 -.027 5.24786 .017 .380 2 45 .686
a
Predictors: (Constant), volume of private transportation, volume of public transportation b Dependent variable: POSITION SLM2
Table 4.
Anova relationship of the volume of public transport and private transportation with noise on the first day on SLM2
Model Sum of squares df Mean square F Sig. 1 Regression 20.922 2 10.461 .380 .686 a Residual 1239.303 45 27.540 Total 1260.225 47 a
Predictors: (Constant), volume of private transportation, volume of public transportation
b Dependent variable: POSITION SLM2
3) The Results of SLM3 Multiple Regression Statistical Analysis a Distance of 10.24 m from the Highway
The results of the study the relationship between the volume of public transport (x1) and
private transport (x2) to the noise that occurs in SLM3 (y) located from the edge of the highway with a distance of 10.24 meters, with a confidence level of 95% and a probability value of 0.05 or 5%, the calculation results obtained
mean for SLM3 of 72.3250 dBA, for public
transportation 465 vehicles/hour and private transportation for 4.821 vehicles/hour. The validity of SLM3 analysis results, the volume of public transport and private transport has a value of one which means that all data is valid.
Table 5.
Summary of the relationship of the volume of public transport and private transportation with noise on the first day in SLM3
Model R R square Adjusted R square Std. error of the estimate Change statistics R square
change F change df1 df2 Sig. F change
1 .162 a .026 -.017 4.41311 .026 .606 2 45 .550
a
Predictors: (Constant), volume of private transportation, volume of public transportation b Dependent variable: POSITION SLM3
Table 6.
Anova relationship between the volume of public transport and private transport with noise on the first day in SLM3
Model
Sum of
squares df Mean square F Sig.
1 Regression 23.610 2 11.805 .606 .550 a Residual 876.400 45 19.476
Total 900.010 47 a
Predictors: (Constant), volume of private transportation, volume of public transportation
b
Dependent variable: POSITON SLM3
D. Wednesday/The Second Day
1) The Results of SLM1 Multiple Regression Statistical Analysis a Distance of 0.00 m from the Highway
The results of the study the relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM1 (y) located from the edge of the highway with a distance of 0.00 meters, with a confidence level of 95% and a probability value of 0.05 or 5%, the calculation shows that the mean value for
SLM1 is 84.8667dBA, for public transportation is
407 vehicles/hour and private transportation is 5135 vehicles/hour.
2) The Results of SLM2 Multiple Regression Statistical Analysis a Distance of 5.12 m from the Highway
The results of the study the relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM2 (y) located from the edge of the highway with a distance of 5.12 meters, with a confidence level of 95% and a probability value of 0.05 or 5%, the calculation is obtained the mean value
for SLM2 of 75.9188 dBA, for public
transportation 407 vehicles/hour and private transportation for 5.135 vehicles/hour.
3) The Results of SLM3 Multiple Regression Statistical Analysis a Distance of 10.24 m from the Highway
The relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM3 (y) located from the edge of the highway with a distance of 10.24 meters, with a confidence level of 95% and a probability value of 0.05 or 5%, the calculation results obtained mean value for SLM3 of 70.4375 dBA, for public transportation 407 vehicles/hour
and private transportation for 5.135 vehicles/hour.
E. Thursday/The Third Day
1) The Results of SLM1 Multiple Regression Statistical Analysis a Distance of 0.00 m from the Highway
The relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM1 (y) located from the edge of the highway with a distance of 0.00 meters, with a confidence level of 95% and a probability value of 0.05 or 5 %, the calculation is that the mean value for SLM1 is 85.668.54 dBA, for public transportation 407 vehicles/hour
and private transportation is 5.612 vehicles/hour.
2) The Results of SLM2 Multiple Regression Statistical Analysis a Distance of 5.12 m from the Highway
The relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM2 (y) which is from the edge of the highway with a distance of 5.12 meters, with a confidence level of 95% and a probability value of 0.05 or 5 %, the calculation shows that the mean value for SLM2 is 76.8875 dBA, for public transportation 407 vehicles/hour
and private transportation is 5612 vehicles/hour.
3) Statistical Analysis of Multiple Regression SLM3 10.24 m Distance from the Highway
The relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM3 (y) which is from the edge of the highway with a distance of 10.24 meters, with a confidence level of 95% and a probability value of 0.05 or 5 %, the calculation
is that the mean value for SLM3 is 70.5333 dBA,
for public transportation 407 vehicles/hour and private transportation is 5.612 vehicles/hour.
F. Saturday/The Fourth Day
1) The Results of SLM1 Multiple Regression Statistical Analysis a Distance of 0.00 m from the Highway
The results of the study the relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM1 (y) located from the edge of the highway with a distance of 0.00 meters, with a confidence level of 95% and a probability value of 0.05 or 5%, the calculation is obtained the mean value
for SLM1 of 85.8229 dBA, for public
transportation 427 vehicles/hour and private transportation for 4.868 vehicles/hour.
2) The Results of SLM2 Multiple Regression Statistical Analysis a Distance of 5.12 m from the Highway
The results of the study the relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM2 (y) located from the edge of the highway with a distance of 5.12 meters, with a confidence level of 95% and a probability value of 0, 05 or 5%, the calculation is obtained the mean value
for SLM2 of 77.7937 dBA, for public
transportation 427 vehicles/hour and private transportation for 4.868 vehicles/hour.
3) The Results of SLM3 Multiple Regression Statistical Analysis a Distance of 10.24 m from the Highway
The results of the study the relationship between the volume of public transport (x1) and private transport (x2) to the noise that occurs in SLM3 (y) located from the edge of the highway with a distance of 10.24 meters, with a confidence level of 95% and a probability value of 0, 05 or 5%, the calculation is obtained the
mean value for SLM3 of 71.0812 dBA, for public
transportation 427 vehicles/hour and private transportation for 4.868 vehicles/hour.
IV. R
ESULTS ANDD
ISCUSSIONIn this study, this was carried out in accordance with the health protocol for the COVID-19 condition in 2020. This study was divided into four research days. For 4 days, namely Monday, Wednesday, Thursday and Saturday from 06.00 AM to 18.00 PM. Every day, field data collection uses SLM1 at a distance of 0.00 m off the highway. SLM2 with a distance of 5.12m on the building fence and SLM3 with a distance of 10.24m for the building wall. All study results are combined with the volume of public transportation x1 and volume of private transportation x2. The results and discussion can be seen in Table 7.
A. Discussion on SLM1 is 0.00 Meters from the Highway Effect of Public Transport Volume
1) Hypothesis
Ha = There is a significant effect between the
volume of public transport and noise
Ho = There is no significant effect between the
volume of public transport and noise α = 5.00%
2) Test Criteria
Model summary test results obtained by the
value of RSquare = 0.001 which means that x1 only
affects 0.1% of y.
ANOVA test results obtained Fcalculate value =
0.017 with a probability value (sig) = 0.983. From the input data we get the value of FTable =
3.19 so FCalculate<FTable, then Ha is rejected and Ho
is accepted.
The coefficients test results, the volume of public transport (x1) has a constant value (a) = 88.582 (B) = 0.001 and the value of tCalculate =
0.120 and the value (sig) = 0.905. From the data
obtained the value of tTable = 2014, then
tCalculate<tTable, then Ha is rejected and Ho is
accepted.
3) Hypothesis Decision
The results of the test statistics above can be drawn from the decision of the hypothesis regarding the influence of public transport on noise, that there is no significant influence or relationship between the volume of public transport on noise that occurs in SLM1 on the first day. The calculation output above gets the following equation. y = a + bx1 = 88.582 + 0.001 x1, meaning that if there is no increase in the volume of public transport, the noise level in
SLM1 is 88.582 dBA.
B. Influence of Private Transport Volume
1) Hypothesis
Ha = There is a significant effect between the
volume private transport and noise
Ho = There is no significant effect between volume private public transport and noise
α = 5.00%
2) Test Criteria
The results of the summary model testing and ANOVA test are the same as the results of the influence of the volume of public transport.
The coefficients test results, the volume of private transportation (x2) has a constant value (a) = 88.582 (B) = 2.283E-5 and the value of
t-Calculate = 0.060 and value (sig) = 0.952. From the
data that is obtained in the value of tTable = 2014,
then tCalculate<tTable, then Ha is rejected and Ho is
accepted.
3) Hypothesis Decision
The results of the test statistics above can be drawn from the results of a hypothetical decision regarding the effect of private transport volumes on noise, that there is no significant influence or relationship between private transport volumes on noise that occurs in SLM1 on the first day. The calculation output above gets the following equation. y = a + bx2 = 88.582 + 2.283E-5x2, meaning that if there is no increase in the volume of public transport, the noise level in SLM1 is
88.582 dBA. And every addition of public
transportation is 2.283E-5, there is an increase of
1 dBA in SLM1.
Table 7.
Results of the discussion on the influence of pubic transportation and private transportation volumes to noise
The day Description Distance (m)
Test criteria
Public transport Private transportation Decision
Monday SLM1 0.00 Received y = a + bx1 = 88.582 + 0.001 x1 y = a + bx2 = 88.582 + 2.283E-5x2 No effect SLM2 5.12 Received y = a - bx1 = 80.116 – 0.697x1 y = a - bx2 = 80.116 – 0.697x2 Take effect SLM3 10.24 Received y = a + bx1 = 70.708+ 0.00001x1 y = a + bx2 = 70.708 + 0.000001x2 Take effect
Wednesday SLM1 0.00 Received y = a -bx1 = 83.761 – 0.006x1 y = a + bx2 = 83.761+0.001x2 No effect SLM2 5.12 Received y = a + bx1 = 76.709- 0.032x1. y = a + bx2 = 76.709- 0.051x2 Take effect SLM3 10.24 Received y = a + bx1 = 70.718 + 0.013x1 y = a - bx2 = 70.718-0.001 x2 Take effect Thursday SLM1 0.00 Received y = a + bx1 = 83.504 + 0.003x1 y = a + bx2 = 83.504 + 0.000x2 No effect SLM2 5.12 Received y = a + bx1 = 75.113+ 0.004x1 y = a +bx2 = 75.113+ 2.347E-5x2 Take effect SLM3 10.24 Received y = a - bx1 = 69.891 – 0.004x1 y = a + bx2 = 69.891 + 0.00001x2 Take effect Saturday SLM1 0.00 Received y = a - bx1 = 82.018-0.005x1. y = a + bx2 = 82.018+ 0.001x2 Take effect SLM2 5.12 Received y = a - bx1 = 74.854- 0.006x1 y = a +bx2 = 74.854+ 0.001 x2 Take effect SLM3 10.24 Received y = a - bx1 = 68.100- 0.022x1 y = a +bx2 = 68100+ 0,001x2 Take effect
V. C
ONCLUSIONThe influence of traffic volume on
transportation caused by public transport and private transportation, obtained by the results of public transport traffic volume does not have a significant effect on transportation that occurs, of all analytical calculations, obtaining the largest analysis on the second day of research on the transition (Sound Level Meter3), with a contribution of 12.10%. To calculate this analysis, the calculation is obtained: y = a + bx1 = 70.718 + 0.013x1. Increasing the volume of public transportation, the approval level in SLM3 is
70.718 dBA. For every payment of public
transport volume of 0.013 vehicles/hour,
expenditure will increase by 0.013 dBA in SLM3.
Private trasportation volume has a significant effect on the conversation that occurs in all analytical calculations SLM1 with a contribution of 19.50%. Calculation analysis obtained Calculation: y = a + bx2 = 82.108 + 0.001x2.
Hoping that there will be an increase in the volume of public transportation, the level of
expenditure in SLM1 is 82.018 dBA. That for
each increase in the volume of public transport is 0.001 vehicles/hour, then transportation will
increase by 0.001 dBA in SLM1.
A
CKNOWLEDGEMENTSThe Authors wish to thank the Organizers Scholarships from Kemenristekdikti Republik
Indonesia/Kementerian Keuangan Republik
Indonesia/BUDI-DN-LPDP 2016 has provided support for this paper to be published (Contract Number: PRJ-6221/LPDP.3/2016, November 2016).