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J u r n a l P e n s i l : P e n d i d i k a n T e k n i k S i p i l 12 ( 2 0 2 3 ) 388 – 399
FACTORS AFFECTING THE DEVELOPMENT OF AN INTEGRATED TOLL
TRANSACTION SYSTEM TO IMPROVE TRAFFIC VOLUME DISTRIBUTION
Fitri Hidayah1*, Ayomi Dita Rarasati2
1,2 Departemen Teknik Sipil, Fakultas Teknik, Universitas Indonesia
Jalan Prof. DR. Ir R Roosseno, Depok, Jawa Barat, 16424, Indonesia
*1fitri.hidayati002@gmail.com
Abstract
The Indonesian Government has attempted to reduce traffic congestion
on toll roads by implementing a non-cash toll transaction system that has
been valid on all Indonesian toll roads since October 2017. However,
heavy traffic that causes traffic congestion on toll roads in urban areas
often occurs. This study aimed to find out the factors affecting the
development of the toll transaction system and vehicle distribution
through the preferences of whether road users choose to enter the toll
road or not. The research method used was a quantitative descriptive
approach with survey through Stated Preference. Respondents were
class I – V vehicle users who traveled from the Jakarta Intra Urban Toll
Roal to Prof. Dr. Ir. Soedijatmo Toll Road. The study’s findings
indicated that the implementation of Electronic Toll Collection (ETC)
can reduce the interest of road users to enter toll roads if fines are
imposed, but road users are still highly interested in entering toll roads
because there is a toll transaction time cut service. The distribution of
traffic volume increased under the congestion pricing scenario since road
users' interest to enter the toll road decrease in entering the toll road
owing to the higher toll rate rise.
Keywords: Congestion, Stated Preference, ETC, Congestion Pricing,
Travel Time Savings
P-ISSN: 2301-8437
E-ISSN: 2623-1085
ARTICLE HISTORY
Accepted:
15 Juni 2023
Revision:
28 September 2023
Published:
29 September 2023
ARTICLE DOI:
10.21009/jpensil.v12i3.36337
Jurnal Pensil :
Pendidikan Teknik
Sipil is licensed under a
Creative Commons
Attribution-ShareAlike
4.0 International License
(CC BY-SA 4.0).
Jurnal Pensil : Pendidikan Teknik Sipil
Factors Affecting the Development …−
Hidayah, F & Rarasati, AD
389
Introduction
Rapid economic growth in cities
cannot be separated from crucial problems.
These problems include the increase in the
movement of goods and services to the city
center which causes a high number of
vehicles crossing the road and leads to traffic
congestion. An example is the cities in the
Jakarta-Bogor-Depok-Tangerang-Bekasi
(Jabodetabek) area. Traffic congestion can
occur on non-toll roads and toll roads. Based
on the Jabodetabek Urban Transport Policy
Integration Project Report Phase 2 in the
Republic of Indonesia, Appendix 02 (Japan
International Cooperation Agency (JICA),
2019), a mapping of the condition of the
speed of vehicles passing on the DKI Jakarta
toll road network and its surroundings was
produced. A traffic speed survey conducted
on 14 October - 30 November 2018 during
rush hour showed that some vehicle speeds
in the Jakarta urban toll road area were ≤ 40
km/h. One of the toll roads in the DKI
Jakarta area with a high level of congestion is
the Prof. Dr. Ir. Soedijatmo Toll Road. This
toll road is the only main toll road that
connects the Jakarta Intra Urban Toll Road
(JIUT) to Soekarno-Hatta International
Airport, Cengkareng, Tangerang City. In the
JICA research, traffic speeds on the toll road
(traveling from JIUT) ranged between 20 and
40 km/h, causing a bottleneck in that area.
The performance of toll roads in serving the
community is measured based on Minister of
Public Works Regulation No.
16/PRT/M/2014 concerning Toll Road
Minimum Service Standards (Kementerian
PU, 2014) and the service substance for the
average travel speed under normal conditions
on inner-city toll roads is ≥ 40 km/h. As a
result, the JICA study's traffic speed fails to
meet the Toll Road Minimum Service
Standards.
Regarding traffic congestion, city
expansion can also enhance traffic growth.
This can be observed in the rapid expansion
of the northern area of Jakarta and its
environs, which has the potential to give rise
to a greater generation of vehicles in the
surrounding areas. For instance, Soekarno
Hatta International Airport in Tangerang
City will be developed into an airport city and
aerotropolis which is influenced by the
condition of the airport as the highest flight
path in Indonesia (Adrian & Pradoto, 2017).
In the JICA report, it is also estimated that
the major impact of annual economic losses
due to traffic congestion in Jabodetabek is
the occurrence of economic losses of IDR
100 trillion during 2018. This loss is
equivalent to a loss of IDR 3 million per
person for a year.
The significant difference in rates on
the toll road network causes a high public
interest in deciding which toll road to pass.
Thus, many vehicles going through the toll
roads in the DKI Jakarta area pick cheaper
toll roads, causing traffic congestion. An
example is the rate for class I vehicles, namely
the Soedijatmo Toll Road rate of IDR
8,000.00 and the JIUT rate of IDR 10,500.00
which is relatively cheaper compared to the
JORR 1 rate of IDR 16,000.00. The rates for
several toll roads in the DKI Jakarta area and
its surrounds in 2022 are as follows.
Figure 1. Toll rates of class I vehicles
on several DKI Jakarta toll road and
its surroundings in 2022
The government has implemented
non-cash toll transactions (using electronic
cards) which have been 100% valid since
October 2017 to alleviate traffic congestion
at toll gates. However, the toll transaction
system still has toll booths, so according to
the statement by (Tan et al., 2017), when a
vehicle has to stop when paying a toll, it can
cause traffic congestion and reduce fuel
efficiency. Currently, the government is
developing a toll transaction system as a form
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390 − Volume 12, Nomor 3, September 2023
of digital transformation from non-cash to
contactless (electronic toll collection (ETC))
with Global Navigation Satellite System
(GNSS) technology based on multi-lane free
flow. In other words, multi-lane free flow is
a form of ETC and the latest system in multi-
lane non-stop transactions without stopping
or reducing vehicle speed at toll gates by
(Harnanda et al., 2022). The system will be
implemented in stages on all toll roads in
Indonesia. The development of the toll
transaction system is carried out based on
Minister of Public Works and Housing
Regulation Number 18 of 2020 concerning
Contactless Non-Cash Toll Transactions on
Toll Roads (Kementerian PUPR, 2020). This
transaction system will eliminate toll gates,
resulting in no more queues of vehicles at toll
gates. Therefore, the ETC system is designed
to ensure that traffic runs smoothly during
toll collection/toll transactions (Popoola et
al., 2017).
GNSS technology uses three
alternative payment tools where each option
can be adapted to the conditions
(convenience/needs) of road users. Vehicles
crossing the toll road will be equipped with a
position sensor device on the On-Board Unit
(OBU) that will automatically track the
vehicle's position and calculate the distance
traveled as well as the toll fee that must be
paid (Velaga & Pangbourne, 2014). Another
tool option is an electronic OBU for a
smartphone mobile application which is
equipped with a choice of payment
methods/bank accounts and other
information (Dias et al., 2014). Aside from
these two tools, another transaction tool that
can be used by road users without OBU
installation is purchasing tickets (Numrich et
al., 2012). In several prior studies, the public
responded positively to time savings in
implementing ETC utilizing OBU. Research
by (Ho et al., 2019) shows that queue time
can be lowered by 50% to 60% by using
OBU compared to transaction systems using
electronic cards. The results of the study by
(Rizal et al., 2019) on the Jakarta-Tangerang-
Cengkareng Toll Road showed that 92% of
respondents agreed with the implementation
of ETC with OBU devices because no more
queues at toll gates and only 23% of
respondents were willing to buy OBU
devices. This is because the price of the OBU
device of IDR 500,000 is not equal to the
savings in lost time value attained. In this
study, no research was conducted regarding
fines that must be paid by road users if there
is an insufficient or empty balance in the
transaction tool.
The delay in the movement of goods
and passengers caused by traffic congestion
and delays has several effects, including
increased cost of roads, increased vehicle
pollution due to emissions, and gas waste
(Selmoune et al., 2020). The implementation
of ETC will eliminate queues at toll gates,
however, this will not always result in a
reduction in the number of vehicles crossing
a toll road. As a result, other techniques of
responding to supply-demand traffic are
required. Another way to deal with
congestion besides implementing ETC is to
use traffic demand management (TDM)
through the application of congestion
pricing. The advantage that GNSS
technology has for ETC devices is its
flexibility because it can be used for other
purposes such as traffic control, congestion
charges, parking payments, and others
(Milenković et al., 2018). The scheme for
implementing congestion pricing is carried
out by providing several levels of tariff
increases for road users who cross a road
during peak hours. According to prior
studies, this has the potential to reduce the
number of vehicles on the road as well as
vehicle exhaust emissions. According to
Yamamoto et al. (in (Rizki et al., 2016)) one
of the promising TDM schemes is
congestion pricing which causes road users to
change routes, departure times, activity
involvement, or means of travel. In addition,
negative externalities caused by traffic, such
as congestion, accidents, and different
emission levels can be reduced by urban
roadpricing (Croci, 2016). In the
identification of (Hamilton et al., 2014), the
three major groups of attitudes related to
congestion charges are attitudes toward
various pricing policies, public interventions,
and environmental concerns. When entering
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Factors Affecting the Development …−
Hidayah, F & Rarasati, AD
391
a traffic restriction zone, road users will be
offered various rate levels and their opinions
to cancel trips or choose alternative modes
(Mirbaha et al., 2014). (Hermawan et al.,
2013) developed a method of increasing toll
road fare scenarios during peak hours, which
were divided into four scenarios, namely
increases of 10%, 25%, 50%, and 100% rate
rise. Another example proves that if rates are
applied differently for each lane in one
direction, as researched by (Alemazkoor &
Burris, 2014), demand can be optimized for
each lane when toll rates are set differently on
different lanes during traffic peak hours.
Further, the total value of travel time savings
reaches 11% of the total value of time spent
when compared to a uniform toll rate for all
lanes on the toll road. Based on research by
(Bueno et al., 2017), truck drivers who
regularly use toll facilities for long-distance
trips are more likely to accept a fixed-cost
strategy. Another study conducted by (Brent
& Gross, 2018) indicated a reduction in usage
of 1.6% for an average response to an
increase in toll rates of 10%. Time-saving
commute reliability was valued by drivers
despite heterogeneity in the relative value of
time and reliability by destination to or from
work and time.
A survey of respondents' road use
preferences for implementing congestion
pricing was performed utilizing the Stated
Preference (SP) method. Several SP
indicators used by (Bueno et al., 2017)
include gender, age, region, income, type of
user, type of trip, type of vehicle, quality of
toll road facilities, perceptions of toll road
costs, qualitative willingness to pay (WTP),
the perception of the contribution of toll
roads to save time, and others. Based on
research (Purba et al., 2020), income and
expenses on transportation attributes are the
factors that most significantly influence route
selection by road users when deciding to
travel on toll roads. (Abulibdeh, 2022)
examined the effect of the average WTP of
respondents on the frequency of trips and
travel destinations of the most widely used
toll roads. As a result, there is no significant
difference in respondents’ WTP when
experiencing congestion at the same fare on
the four main toll roads in the city. In terms
of gender-related demographic data, research
by (Chiou & Fu, 2017) found that high-
income male respondents will continue to
drive during the morning rush hour and pay
congestion charges. The result of the study
by (Linn et al., 2016) revealed that individual
travel behavior is more likely to be influenced
by CP in making discretionary trips than
those who are commuting. Congestion
reduction improves not just the environment
by lowering air pollution caused by vehicles
delayed in traffic and increasing travel times
and reliability, but it also reduces the risk of
accidents and fatalities. At the same time,
spatial, temporal, and specific types of
vehicles can lead to unwanted substitution as
traffic and accidents move to other nearby
areas, hours, and vehicles that are not
charged (Green et al., 2014). The successful
introduction of CP in major cities is due to
an alliance between three groups:
environmentalists want environmental
benefits, traffic planners want increased
efficiency, and politicians seek revenue
streams. In cities that have successfully and
unsuccessfully introduced CP, both can be
learned about what to do and what to avoid
(Eliasson, 2019).
Currently, there has never been a policy
related to congestion pricing specifically on
toll roads in Indonesia. Referring to the
above matters, this study aims to determine
the factors that influence the development of
integrated toll transactions with the
implementation of the MLFF-based ETC as
well as the application of CP, the distribution
of traffic volume, and the mode shift of land
transportation for class I vehicle users when
CP is implemented.
Research Methodology
The variables used in this study were
exogenous latent variables (independent)
such as socio-economic demographic
characteristics, travel characteristics, existing
rates and services, willingness to pay CP,
ETC, and endogenous latent variables
(dependent) such as reduction in traffic
congestion and decision change in the mode
of transportation. Then, the intervening
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392 − Volume 12, Nomor 3, September 2023
variables, namely travel time savings. The
intended travel time savings were higher time
savings when ETC and CP were applied
together.
Secondary data was taken from the
Indonesia Toll Road Authority, Ministry of
Public Works and Housing, while the
primary data came from respondents who
crossed the Soedijatmo Toll Road with
directions from the JIUT. This study used a
quantitative descriptive approach through
online and offline Stated Preference surveys
and data analysis using the Structural
Equation Modeling method and the Smart
Partial Least Square application. The
sampling technique used was a proportionate
stratified random sampling method with
clean data obtained from as many as 341
respondents based on vehicle type consisting
of 316 respondents in class I vehicles, 17
respondents in class II vehicles, 4
respondents in class III vehicles, 2
respondents in class IV vehicles, and 2
respondents in class V vehicles.
Types of vehicles (Kementerian PU,
2007) that cross the Soedijatmo Toll Road are
as in Table 1. On this toll road, an open
payment system or one-time transaction with
a flat rate applies along the toll road
(Kementerian PUPR, 2021) as shown in
Table 2. To facilitate data processing,
particularly for the WTP analysis, the fare
increase scenario is ranked by percentage
uniform rate increase by rounding up to the
nearest IDR 500.00 for each class of vehicles,
as shown in Table 3.
Table 1. Types of vehicles
Class
Vehicle Type
I
Sedan, Jeep, Pick Up/Small Truck, and
Bus
II
Truck with 2 (two) axles
III
Truck with 3 (three) axles
IV
Truck with 4 (four) axles
V
Truck with 5 (five) axles
Table 2. Existing toll rates for vehicle
classes
The Group of Vehicle
I
II
III
IV
V
8,000
10,500
10,500
11,500
11,500
Table 3. The scenario of toll rates increase
based on vehicle classes
The
Scena
rio of
toll
rates
(%)
The Rates of Vehicle Classes
I
II
III
IV
V
50%
12,000
16,000
16,000
17,500
17,500
75%
14,000
18,500
18,500
20,500
20,500
100%
16,000
21,000
21,000
23,000
23,000
125%
18,000
24,000
24,000
26,000
26,000
The questionnaire in this study used a
Likert scale for preferences for the use of the
toll road or non-toll roads as shown in Table
4.
Table 4. Likert scale for road use preference
No
Preference
1
Certainly choose non-toll road
2
Probably choose non-toll road
3
Probably choose toll road
4
Certainly choose toll road
The relationship between the number
of vehicles entering the toll road due to the
implementation of CP during peak hours
based on the Likert scale is illustrated
through three conditions as shown in Table
5.
Table 5. Conditions for reducing vehicle
volume based on prefences table 4
Condition
Vehicle Reduction (%)
Pessimistic
(1)
Moderate
(1) + (2)
Optimistic
(1) + (2) + (3)
Preferences for switching modes of
transportation by class I vehicle users when
implementing CP can be seen in Table 6.
Table 6. Likert scale for land transportation
mode preference
No
Preference
1
Certainly use private vehicle
2
Probably use private vehicle
3
Probably use public transportation
4
Certainly use public transportation
Research Results and Discussion
Following are the results of the variable
characteristics of socio-economic
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393
demographic, travel characteristics, and
opinions of road users on existing rates and
toll road services. In this study, it was
discovered that female respondents had a
higher percentage than male respondents,
with women having 53.37% and men having
46.63%. The respondents of the Soedijatmo
Toll Road were dominated by users who had
an income of around IDR4,000,00.00–
IDR8,000,000.00 or medium-level income as
many as 58.36%. The toll road users exited at
the Cengkareng Toll Gate were found to be
78.89%, the remainder at the Kamal Toll
Gate at 15.25%, and the Benda Interchange
at 5.87%. The type of vehicle that dominated
the trip was class I vehicles for the
sedan/passenger car/private car category as
many as 86.51%.
According to the survey results, the
variables in the relationship between the
increase in toll rates and the decrease in
traffic volume on the toll road, the increase
in toll rates of 50%, 75%, 100%, and 125%
for 20 minutes of time-saving when CP is
applied caused a reduction in the number of
vehicles that access the toll road. This can be
seen in class I vehicle, where a 50% increase
in toll rates and a 20-minute time saving
resulted in 48% of vehicles certain to access
the toll road and 52% of vehicles certain not
to access the toll road. In this class I vehicle,
the higher the increase in toll rates, the
smaller the percentage of vehicles that are
certain to enter the toll road, so that the
number of vehicles that are not to enter the
toll road increases. Meanwhile, all
respondents from class III-V vehicles chose
to enter the toll road with certainty if CP is
applied to all variations of the toll rate
increase with a time saving of 20 minutes and
30 minutes.
Electronic toll collection (ETC), travel
time savings, traffic congestion reduction,
and the decision to convert to switch
transportation modes are other variables in
the road usage preference category.
According to the survey results, if the ETC
was applied, as many as 63.64% of
respondents would prefer to use the toll road
because there is a time cut for toll
transactions, and only 3.81% of respondents
would prefer to enter the non-toll road. In
the fine indicator that is applied if the balance
is empty or insufficient when crossing the
Soedijatmo Toll Road, only 23.75% of
respondents chose to continue entering the
toll road, whereas up to 33.72% of
respondents might choose the toll road. The
approach used for the perception of the
number of fines that must be paid at certain
times to road users if the balance is lacking or
empty is the Government Regulation
Number 15 of 2005 concerning Toll Roads
which refers to Article 86 paragraph 2 which
is twice the toll rate (Indonesia, 2005). In the
decision variable to switch to land
transportation when CP is implemented,
25.51% of respondents selected to use public
transportation when driving on toll roads,
with up to 37.83% potentially using public
transportation.
Hypothesis testing in this study was
undertaken using SmartPLS 3.0 software and
structural model analysis by looking at the
direct and indirect effects. Convergent
Validity is a measure of the validity of a
reflexive indicator as a variable measure
which can be observed from the outer
loading of each variable indicator. The test
results produce several outer loading values
below 0.50, which must be dropped. The
outer loading value can still be tolerated up to
0.50 and an indicator is said to have good
reliability if the outer loading value is above
0.70 (Haryono, 2019). The remaining
indicators on the socio-economic
demographic characteristic variables are age,
income, and status as a driver/passenger &
vehicle ownership. For variable travel
characteristics, the remaining indicators are
toll exits, the frequency of trips before and
after the COVID-19 pandemic on the toll
road (per year), and the type of vehicle used.
Furthermore, the toll rate indicator is an
indicator of the remainder of the existing
rates and services variable, while other
variables in this study have fulfilled the outer
loading value.
The number on the average variance
extract (AVE) is used to determine whether
the average indicator variance for each
variable is homogeneous or not for each
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research variable shows a number greater
than 0.5. Thus, the data collected meets the
requirements to be homogeneous. Because
the discriminant validity number is greater
than 0.6 based on the measurement findings,
all variables are declared valid. Furthermore,
the composite reliability value of all research
variables is more than 0.7, which means that
all independent latent variables are
appropriate and feasible to be used as
variables tested to determine their effect on
dependent latent variables. In PLS, the
reliability test is strengthened by the presence
of Cronbach's alpha where the consistency of
each answer is tested. Cronbach's alpha is
said to be good if α ≥ 0.6 and is said to be
sufficient if α ≥ 0.3. The Cronbach's alpha
value for the ETC variable is 0.516. These
results indicate that
ETC variables are quite reliable and the
other variables in this study are reliable and
good. Based on the results of the inner model
test, the R Square value ranges from 0.012 to
0.504 and the average is 0.2 for all variables,
indicating that this value is considered weak.
According to Jogiyanto (Haryono, 2019), the
higher the R Square value in the proposed
research model the better the prediction
model. For example, the R Square value of
0.7 implies that the variation in changes in the
dependent variable that can be explained by
the dependent variable is 70% and the rest is
explained by other variables outside the
proposed model. However, the basis of the
theoretical relationship is the most important
parameter in explaining the causality
relationship, hence R Square is not an
absolute parameter in determining the
accuracy of the prediction model. The
prediction relevance test (Q Square) for all
variables is 0.768. This indicates that
exogenous constructs have great predictive
relevance for endogenous constructs.
The results of testing the direct
influence research hypothesis show that of
the eleven hypotheses, three hypotheses are
rejected. The first hypothesis that was
rejected was the social-economic and
demographic characteristics of the WTP
Congestion Pricing (CP) as indicated by the
original sample value of 0.049 and the t-
statistic of 0.866. The measurement results
showed that the t-statistic <T table (5% =
1.96), implies that the research hypothesis is
rejected. It can be interpreted that the sample
data on the social-economic and
demographic characteristics such as age,
income, and status as a driver/passenger &
vehicle ownership of toll road users do not
have a significant influence on the WTP CP
variable in the form of an increase in
variation toll rates and reduced travel time
savings. The results of this study are
consistent with the results of research
(Sunitiyoso et al., 2020) on the relationship
between demographic characteristics and
acceptance of Electronic Road Pricing
policies, which suggests that this has no
significant influence. Gender, age, income,
expenses, and car ownership were all
indicators of demographic characteristics of
road users in a prior study. The second
hypothesis that was rejected (the highest t-
statistic value) was the relationship between
the effect of WTP CP on the decision to
switch to land transportation with a t-statistic
of 1.071 and the original sample value of -
0.108 with a negative relationship. This value
means that the rising toll rates during peak
hours on the toll road will not entice the
interest of road users to switch modes of
transportation from private vehicles to public
transportation. It can be indicated that
people’s culture prefers to use private cars to
get to their destination.
In the accepted hypothesis, it was
found that the measurement results of the t-
statistic value > T table (5% = 1.96
significance level). The highest t-statistic
value of 11,036 and the original sample value
of 0.511 were obtained in the relationship
between the effect of ETC on travel time
savings, with a positive relationship direction.
This shows that ETC has a positive effect on
cutting toll transaction time so that travel
time is saved on the toll road. The smallest t-
statistic value for the accepted hypothesis has
been proven successful in the effect of travel
characteristics on WTP CP with an original
sample value of 0.156 and a t-statistic of
2.563, with a positive relationship direction.
Based on this, it can be interpreted that the
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Hidayah, F & Rarasati, AD
395
travel characteristics of the choice of road
users for the intended exit gate category, the
frequency of trips before and after the
COVID-19 pandemic on the toll road (per
year), and the type of vehicle used, all have a
significant influence on the willingness of toll
users to pay increased toll rates when CP is
implemented.
In addition to testing the direct effect,
hypothesis testing was also carried out to test
the indirect effect. In the twenty hypothesis
tests for this indirect effect research, eight
hypotheses were accepted and twelve
hypotheses were rejected. An example of a
rejected hypothesis is the relationship
between the effect of WTP CP → travel time
savings → traffic congestion as indicated by
the original sample value of 0.014 and the t-
statistic of 0.819 with a positive relationship.
Thus, the results of the effect analysis
showed that variations in the increase in toll
rates when implementing CP on the toll road
have a positive effect on travel time savings
but have no significant influence on reducing
congestion. It can also be indicated that travel
time savings is not commensurate with the
large amount of traffic demand. In a previous
study conducted by (Hermawan et al., 2013),
the results showed that limited road capacity
and time values and high transportation
needs caused the level of service and volume
capacity ratio (VCR) values to not
significantly change as a result of price
increases. Meanwhile, for example, the
indirect effect hypothesis that is accepted is
the relationship between the effect of existing
rates and services → WTP CP → traffic
congestion with an original sample value of -
0.086 and a t-statistic of 3.918 showing a
significant influence with a negative
relationship. It can be concluded that the
increase in toll rates from the existing toll
rates will reduce the interest of vehicle users
passing on the toll road, resulting in a
decrease in vehicles and congestion.
However, the smaller the value of the
increase in toll rates chosen by road users
(particularly from class I vehicles) in the
variation of toll rates compared to the
existing toll rates, the more road users will
choose to enter the toll road, resulting in only
slight decrease in congestion. This is in line
with the results of a study conducted by
(Hadji Hosseinlou et al., 2016) in Tehran,
Iran, which found that the most significant
influenceis observed in the increase in toll
rates, which causes the willingness of road
users to pay tolls to decline drastically.
Aside from the studies mentioned
above, there are several other previous
studies with research results that are similar
or different from the existing literature. For
example, (Cornago et al., 2019) stated that
road pricing can encourage behavioral
changes that urge persons to switch to
environmentally friendly modes of
transportation. This immediately decreases
demand for travel by private motorized
vehicles, such as cars or motorbikes.
Research by (Milenković et al., 2019)
regarding the introduction of CP in the city
of Belgrade, Serbia, showed that around 16%
of respondents switched modes of
transportation from cars to public
transportation. Furthermore, based on the
results of a survey on perceptions about the
value of travel time-saving in Oman by (Javid
et al., 2022), it was revealed that more than
73% of people used cars for transportation,
indicating that most do not like waiting for a
mode of public transportation. This can be
viewed as these people preferring private
transportation over public transit to save
time. A study related to the implementation
of CP (Özgenel & Günay, 2017) in Istanbul,
Turkey was carried out by asking several
questions about travel characteristics and
demographics of road users through a stated
preference survey, such as age, income,
education, travel purpose, and travel time.
The research results related to the age of the
respondents revealed that the highest
number of respondents were aged 25-35
years, accounting for 34% of the total
respondents. According to 66% of the age
group, the congestion charge will not reduce
the level of congestion. According to
(Shatanawi et al., 2020), in the congestion
charge policy, the congestion charge scheme
for each area or city must be designed
uniquely for the best results. In addition, the
potential benefits of implementing
Jurnal Pensil : Pendidikan Teknik Sipil
396 − Volume 12, Nomor 3, September 2023
congestion pricing policies may vary
according to the length of time after
implementation and the type of road user
(Singichetti et al., 2021).
Conclusion
According to the survey results, the
most targeted toll road exit was the
Cengkareng Toll Gate with a total of 78.89%
of respondents.
In a moderate scenario, the increase in
toll rates was 50% and a 20-minute travel
time savings during rush hour reduced the
interest of class I vehicle users to enter the
toll road by 15%. This indicates that the
number of vehicles can be lowered by 15% in
normal conditions. Furthermore, the greater
the increase in toll rates, the smaller the
interest of road users to enter the toll road.
This is evident from the 75%, 100%, and
125% increases in toll rates, which have the
potential to reduce the number of vehicles by
25%, 44%, and 55%.
The results of a survey on plans to
implement ETC showed that as many as
63.64% of respondents were interested in
entering the toll road because there was a toll
transaction time cut. However, when fines
are imposed if the balance is empty or
insufficient while crossing the toll road, only
23.75% of respondents chose to continue to
enter the toll road.
Hypothesis testing was carried out on
the factors affecting the increase in the
distribution of traffic volume through the
analysis of the hypotheses of direct and
indirect effects. In general, it appears that the
indirect effect hypotheses are rejected since
they have no significant effect. The factors of
socioe-conomic demographics (age, income,
and status as a driver/passenger & vehicle
ownership) and travel time savings (bigger
time savings (the application of CP & ETC))
have no significant influence. Meanwhile, the
most influential criteria include travel
characteristics, existing prices, and services,
willingness to pay congestion pricing, and
electronic toll collection.
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