Vol. 5, No. 4, April 2024
E-ISSN: 2723 – 6692
P-ISSN: 2723 – 6595
http://jiss.publikasiindonesia.id/
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 705
A Comprehensive Analysis of Public Choices in Mass Transport
and Assessing Development Challenges in The Transportation
Sector
Ipoeng Martha Marsikun, Paulus Israwan Setyoko, Hikmah Nuraini,
Muslih Faozanudin, Denok Kurniasih
Universitas Jenderal Soedirman Purwokerto, Indonesia
Email: ipoeng.marsikun@mhs.unsoed.ac.id
Correspondence: ipoeng.mar[email protected]soed.ac.id
*
KEYWORDS
ABSTRACT
Public Preferences; Mode
of Transportation;
Banyumas Regency
The primary objective of this research is to investigate public
transportation governance in Banyumas Regency using the New
Public Management (NPM) paradigm, with the aim of enhancing
efficiency and effectiveness. The study focuses on understanding the
factors that influence people's preferences for public transportation
modes, particularly in relation to availability, cost, comfort, safety,
and environmental concerns. The research also aims to address the
challenges associated with the perception of private vehicles as
status symbols, recognizing their impact on economic and societal
development. The study employs quantitative methods and the
SmartPLS4 analysis tool to uncover significant findings regarding
the positive impacts of availability and comfort on preferences and
the varying influences of cost, environment, and security. The
ultimate goal is to provide insights that can inform the development
of transportation policies, promoting effectiveness and alignment
with the community's needs in Banyumas Regency.
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
1. Introduction
Transportation is transporting goods or people using various types of transportation
geographically (Steenbrink, 1974). Transportation has a vital role in shaping the face and
development of a region in the long run as a formative power. The role of transportation includes
support for other sectors and as a driver to open up regional isolation. In addition, transportation also
plays a role in supporting the community's economic growth by facilitating the movement of goods
and people, which can increase the economic value of an area (Adisasmita, 2012).
Analysis of factors influencing people's preference towards mass transportation in Indonesia is
considered necessary for understanding the challenges and opportunities associated with its
implementation (Cannas et al., 2020). By identifying and addressing these factors, policymakers can
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 706
develop strategies to promote public transit, increase mobility, reduce congestion, and make mass
transportation safer, more convenient, and more sustainable (Miller et al., 2016).
The congestion problem in a region's governance not only impacts the community's socio-
economic problems but also causes public problems such as the obstruction of public transportation
services, health services, and transportation of waste from the community. Congestion can also
damage public trust, so the government is responsible for providing services to solve congestion
problems (Nasution, 2004).
The development of the transportation sector is closely related to infrastructure development,
such as toll roads, airports, monorails, and freight transportation systems at ports. However, it should
be acknowledged that not all infrastructure development can fully solve transportation problems in
Indonesia. A study conducted by transportation infrastructure experts at the Bandung Institute of
Technology (ITB) shows that although infrastructure development can improve the transportation
system, it is only sometimes an absolute solution. There are several complex and dynamic factors
involved in transportation problems, including people's movement patterns, transportation policies,
and social and economic aspects (Savitri, 2022).
Thus, while infrastructure development can significantly contribute, it is essential to consider
its other aspects and investigate holistic solutions involving various areas, including traffic
management policies, regulations, and public awareness. Policies issued by the Government must also
be in line with the program proclaimed. For example, the bus ride program or returning to public
transportation will have a more positive impact if accompanied by incentives for purchasing public
transportation units, not even incentives given to private vehicles. This means that transportation
infrastructure development must be integrated with a broader and integrated approach to achieve
increased efficiency and effectiveness of the transportation system in Indonesia.
Table 1 Types of Transport
No
Types Of Transport
Number Of Fleets
2021
2022
1
Intercity Between Provinces
(Antar Kota Antar Propinsi/AKAP)
50
47
2
Intercity within Provinces
(Antar Kota dalam Propinsi/AKDP)
421
413
3
Bus Rapid Transit Trans Banyumas
(BRT Trans Banyumas)
0
52
4
Urban Transport
(Angkutan Perkotaan/Angkot)
328
294
5
Rural Transport (Angkutan Pedesaan
(Angkudes)
596
596
6
Taxi
46
46
7
Travel shuttle transportation
106
106
8
Tourism Transport (7 operators)
125
125
9
Bus Rapid Transit Trans Central Java
(BRT Trans Jateng)
14
14
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 707
Total
1.686
1.693
Source: Banyumas Regency Transportation Office 2023
However, this condition also has a negative impact in the form of increased traffic density and
congestion, especially on the main roads leading to the city center and industrial areas. The 2022
survey by the Banyumas Regency Transportation Office shows that the average traffic density level
on national roads and main roads has reached 0.8 (0.8 degree of saturation), close to the congestion
threshold. During peak hours, congestion points generally occur around Purwokerto, Purwareja
Klampok, Wangon, and the city center (source: Banyuma Regency Transportation Offices).
Most previous studies have focused more on user characteristics such as age, gender, and
income in examining transportation mode preferences (Chowdhury et al., 2018; Ho et al., 2020; Ismail et al.,
2012; Matubatuba & De Meyer-Heydenrych, 2022). Even so, comprehensive research has yet to be done on
public transportation mode preferences in the Banyumas Regency (Putro et al., 2022).
The research findings are expected to provide a comprehensive picture of the factors of
Banyumas people's preferences in choosing transportation modes so that they can be used as
recommendations for the Banyumas Regency Government and public transportation operators in
formulating policies and strategies to improve the quality of public transportation services according
to user preferences. Thus, public transportation is expected to be an efficient, environmentally
friendly mode of transportation and reduce traffic congestion and exhaust emissions of motorized
vehicles in the Banyumas Regency. This research is an integral part of supporting the development of
intelligent transportation systems in Banyumas Regency, which is a step toward the concept of a
smart city in the future (Marsikun et al., 2023).
2. Materials and Methods
The research method employed in this study utilizes a quantitative approach with a cluster
random sampling technique. The research location is in Banyumas Regency, with the object of
research involving users of transportation modes in the Regency consisting of 27 Districts with
divisions into 10 Clusters. The study population includes people who use transportation modes in
Banyumas Regency in 2022, with 3,267,059 users or passengers. The sampling method used is cluster
random sampling using the Slovin formula to determine the sample size. Based on calculations, the
minimum sample size required is 400 respondents, data analysis used with SmartPLS4 analysis
applications.
3. Results and Discussions
Research Results
The data obtained from the study conducted in January 2024 includes 535 respondents. After
the data cleansing process, the number of respondents that can be considered reaches 480, spread
across ten sampling areas or clusters. A total of 55 respondents were removed from the analysis
because they were considered not eligible for age, i.e., under 17 years old. To expand the coverage
area, additional respondents were involved in Wangon Terminal, covering the Lumbir and Rawaheng
areas; Ajibarang Terminal, covering the northern part of Ajibarang and Pekuncen; and Karanglewas
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 708
Terminal, covering the Kedungbanteng and Karanglewas Kidul areas. The results of the addition of
respondents are then recorded as follows:
Table 2 Number of Respondents
Cluster Sampling
Respondent
Target
Difference
%
Bulupitu Terminal
81
80
1
17%
Karanglewas Terminal
54
40
14
11%
Sokaraja Terminal
53
40
13
11%
Ajibarang Terminal
52
40
12
11%
Wangon Terminal
49
40
9
10%
Baturraden Terminal
47
40
7
10%
Pasarpon Terminal
37
30
7
8%
Bulupitu Terminal
37
30
7
8%
Notog Patikraja Terminal
36
30
6
8%
Banyumas Lama Terminal
34
30
4
7%
Total Respond
480
400
80
100%
Source: Primary Data of Research Results, January 2024
Discussion
The Banyumas Regency has experienced increased economic activity and population growth
recently. According to BPS data, in 2022, the population reached 1,806,013 people, with a growth rate
of 0.91% per year. Economic growth reached 5.86% in 2022, exceeding the local government's target
and Central Java's economic growth rate. The trade, hotel, and restaurant sectors are driving
economic growth. The positive impact of economic and population growth can be seen in the increase
in the needs and activities of the movement of people and goods in Banyumas Regency. Data shows
there are 879,023 units of vehicles in operation, including passenger cars, buses, freight cars,
motorcycles, and special vehicles (Source: korlantas.polri.go.id).
Analysis of Research Results
1. Results of Measurement Model Analysis (Outer Model)
The outer loadings assessment assesses the correlation between the score item or indicator and
its construct score, which shows a statement item's validity level. Outer loadings testing is carried out
based on the results of questionnaire trials that have been carried out for all research variables. There
are stages of testing with data analysis techniques to assess outer loadings, namely individual item
reliability, internal consistency reliability, average variance extracted, discriminant validity, and
Variant analysis (R2) or Determination Test. There are the following Variable descriptions: 1. AVA:
Availability, 2.COM: Comfort, 3. CST: Cost, 4. SFT: Safety, 5. ENV: Environment and 6. PRP: Public
Preference.
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 709
Testing is done by looking at the value of the Standardized Loading Factor. The value describes
the magnitude of the correlation between indicators and variables. If there is a value above 0.7, it can
be considered valid as an indicator to measure variables, but if there is a value above 0.6, it can still
be used as a minimum standard (Henseler et al., 2009). After testing, eight indicators have values
below 0.6, namely, AVA2, COM1.1, COM2, CST5, SFT3, ENV4, ENV5, and PRP4 so that these indicators
are removed, but the variables from the indicators that have been eliminated are still used because
other variable indicators still represent them. Retesting is carried out by looking at the value of the
Standardized Loading Factor used to see the criteria for values that can be valid with values above 0.7
as an indicator to measure variables, but if there are values above 0.6 as the minimum limit value.
Overall, judging from the value in the figure above, it is fully qualified, which is above 0.6. Next, look
at the value of Average Variance Extracted (AVE) as follows:
Table 3 Average Variance Extracted (AVE) Test Result
Variable
Average Variance
Extracted (AVE)
Availability
0.531
Comfort
0.467
Cost
0.743
Safety
0.723
Environment
0.677
Public Preference
0.723
Source: Primary Data of Research Results, January 2024.
The table above shows that the Average Variance Extracted (AVE) value with a minimum
standard value above 0.5 is valid. However, in the Comfort indicator with a value of 0.467, judging
from this value cannot be continued, it must eliminate the value on the lowest Comfort indicator,
namely the COM1.2, COM4.1, and COM4.2 indicators.
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 710
Source: Primary Data of Research Results, January 2024.
Retesting is done after eliminating unqualified indicators by looking at the value of the
Standardized Loading Factor. The correlation value between indicators and variables is valid and can
be used. The values for each variable indicator can be seen in the table below.
Table 4 SmartPLS Loading Factor Test Results
Indikator
Availability
Comfort
Cost
Safety
Environm
ent
Public
Preference
AVA1
0.612
AVA3
0.609
AVA4.1
0.824
AVA4.2
0.770
AVA4.3
0.796
COM1.2
0.647
COM3
0.740
COM5.1
0.712
COM5.2
0.786
COM5.3
0.765
COM6
0.705
CST1
0.861
CST2
0.846
CST3
0.898
CST4
0.841
SFT1
0.821
SFT2.1
0.844
SFT2.2
0.885
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 711
ENV1
0.698
ENV2.1
0.865
ENV2.2
0.870
ENV2.3
0.844
PRP1.1
0.763
PRP1.2
0.774
PRP1.3
0.745
PRP2.1
0.722
PRP2.2
0.712
PRP2.3
0.709
PRP3
0.738
Source: Primary Data of Research Results, January 2024
1. Internal Consistency Reliability
This study used a composite reliability value with a threshold of 0.7, as indicated by Henseler
et al. (2009). Here are the SmartPLS test results.
Table 5 SmartPLS Composite Reliability Test Results
Variable
Composite Reliability
Availability
0.848
Comfort
0.870
Cost
0.920
Environment
0.893
Public Preference
0.893
Safety
0.887
Source: Primary Data of Research Results, January 2024.
The table above shows a Composite Realibility value above 0.7, valid in the Composite
Realibility test.
2. Average Variance Extracted (AVE)
Convergent validity testing by looking at the average variance extracted (AVE) value column.
AVE value to show the amount of variance in a variable that is in a latent variable. The minimum
standard AVE value of 0.5 indicates a good measure of convergent validity (Henseler et al., 2009).
Here are the test results from SmartPLS.
Table 6 SmartPLS Average Variance Extracted (AVE) Tets Results
Variable
Average Variance Extracted (AVE)
Availability
0.531
Comfort
0.529
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 712
Cost
0.743
Environment
0.677
Public Preference
0.545
Safety
0.723
Source: Primary Data of Research Results, January 2024.
Table 25 shows that the Average Variance Extracted (AVE) value is above 0.5, so it can be said
to be valid, and there are no problems in AVE testing.
3. Uji Discriminant Validity
The test uses cross-loading, where the Average Variance Extracted root values will be
compared. Cross-loading is a statistical tool used to assess the correlation between one indicator and
another in a variable. A higher correlation between indicators and constructs indicates a superior
value compared to other indicators. Below are the test results obtained by SmartPLS.
Table 7 SmartPLS Discriminant Validity Test Results
Availability
Comfort
Cost
Safety
Environment
Public
Preference
AVA1
0.612
0.469
0.431
0.432
0.371
0.484
AVA3
0.609
0.475
0.384
0.364
0.305
0.414
AVA4.1
0.824
0.530
0.488
0.415
0.525
0.466
AVA4.2
0.770
0.481
0.497
0.383
0.486
0.461
AVA4.3
0.796
0.517
0.496
0.399
0.519
0.458
COM1.2
0.434
0.647
0.309
0.426
0.466
0.428
COM3
0.635
0.740
0.670
0.632
0.653
0.617
COM5.1
0.416
0.712
0.391
0.513
0.372
0.422
COM5.2
0.445
0.786
0.456
0.538
0.416
0.476
COM5.3
0.479
0.765
0.475
0.501
0.449
0.449
COM6
0.515
0.705
0.465
0.449
0.485
0.487
CST1
0.567
0.573
0.861
0.549
0.566
0.487
CST2
0.502
0.499
0.846
0.486
0.492
0.493
CST3
0.545
0.595
0.898
0.573
0.589
0.554
CST4
0.579
0.581
0.841
0.569
0.548
0.541
SFT1
0.594
0.613
0.710
0.821
0.650
0.570
SFT2.1
0.360
0.584
0.388
0.844
0.495
0.456
SFT2.2
0.423
0.609
0.473
0.885
0.512
0.490
ENV1
0.410
0.428
0.487
0.437
0.698
0.439
ENV2.1
0.570
0.630
0.613
0.615
0.865
0.537
ENV2.2
0.536
0.574
0.526
0.567
0.870
0.508
ENV2.3
0.483
0.542
0.459
0.532
0.844
0.456
PRP1.1
0.471
0.524
0.450
0.496
0.449
0.763
PRP1.2
0.482
0.506
0.387
0.441
0.415
0.774
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 713
PRP1.3
0.500
0.495
0.579
0.479
0.453
0.745
PRP2.1
0.488
0.539
0.558
0.459
0.485
0.722
PRP2.2
0.405
0.451
0.317
0.418
0.407
0.712
PRP2.3
0.430
0.452
0.369
0.393
0.393
0.709
PRP3
0.476
0.491
0.413
0.405
0.446
0.738
Source: Primary Data of Research Results, January 2024.
The table above shows that the value of the construct in bold is more excellent than the value
not in bold. Thus, the research model used already has good characteristics in the tests.
4. Variant Analysis (R2) or Determination Test
Variant Analysis (R2) or the Determination test determines the amount of influence the
independent variable has on the dependent variable. Here are the results of SmartPLS testing.
Table 8 SmartPLS R-square value result
Variable
R Square
Public Preference
0.545
Source: Primary Data of Research Results, January 2024.
Based on the table above, the R-square value shows that the Public Preference variable is
54.5%, with the rest of the variable's value influenced by other factors. Based on these results, the
research model carried out has qualified to be continued on structural model testing (inner model).
Results of Measurement Model Analysis (Inner Model)
Structural Model Testing (Inner Model) includes parameter coefficients, t-statistics, and p-
values to see if a hypothesis is acceptable or rejected. This study tested hypotheses using SmartPLS
software. This value is seen from the bootstrapping results. The rules of thumb used in this study are
t-statistics >1.96, a significance level of p-value of 0.05, and a positive coefficient. The results of this
study can be seen in the table below.
Table 9 Path Coefficients SmartPLS Result
Hipotesis
Original
Sample (O)
Sample
Mean (M)
Standard
Deviation
(STDEV)
T Statistics
(|O/STDEV|)
P
Values
Availability -
> Public
Preference
0.230
0.229
0.049
4.730
0.000
Comfort ->
Public
Preference
0.263
0.265
0.053
4.967
0.003
Cost ->
Public
Preference
0.136
0.139
0.074
1.826
0.065
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 714
Environment
-> Public
Preference
0.101
0.097
0.063
1.593
0.116
Safety ->
Public
Preference
0.135
0.137
0.055
2.463
0.003
Source: Primary Data of Research Results, January 2024.
1. Availability towards Public Preference
After testing the hypothesis above, the Availability variable significantly affects the Public
Preference for transportation modes in Banyumas Regency. Where the results of calculating SmartPLS
Availability show that the Original Sample is 0.230, the Sample Mean is 0.229, the standard deviation
is 0.049, and p values are 0.000, it can be concluded that Availability affects the Public Preference of
transportation modes in Banyumas Regency. This aligns with the previous hypothesis that H1 =
Availability affects public preference positively and significantly. The mode of transportation in
Banyumas Regency is relatively easy, and often, the availability of a service is so that the community
feels fulfilled regarding the availability of transportation modes in Banyumas Regency. The results of
this study are in line with research from (Cattaneo et al., 2018; Tuffour & Asiama, 2023), which states that
Availability is a factor that can influence people's preferences in choosing public transportation modes
from students' attitudes towards mobility in choosing transportation modes sustainably and Ghanaian
city public preferences in choosing public transportation.
2. Comfort towards Public Preference
After testing the hypothesis above, it shows that the Comfort variable significantly affects the
Public Preference of transportation modes in Banyumas Regency. Where the SmartPLS Comfort
calculation results show that the Original Sample is 0.263, the Sample Mean is 0.265, the standard
deviation is 0.053, and p values are 0.000, it can be concluded that Comfort affects the Public
Preference of transportation modes in Banyumas Regency. This aligns with the previous hypothesis
that H2 = Comfort positively and significantly affects public preference. The mode of transportation
in Banyumas Regency is comfortable, so people feel fulfilled in terms of comfort. This study's results
align with Batarce et al., (2015). Comfort in public transportation in terms of passenger density and
transportation mode facilities significantly affects the utility of transportation modes in Santiago,
Chile. In addition, research by Soza-Parra et al. (2019). Service reliability in improving the comfort of
transportation modes almost always plays a vital role in the satisfaction and choice of transportation
modes.
3. Cost towards Public Preference
After testing the hypothesis above, it shows that the Cost variable does not significantly affect
the Public Preference for transportation modes in Banyumas Regency. Where the results of
calculating SmartPLS Cost show that the Original Sample is 0.136, the Sample Mean is 0.139, the
standard deviation is 0.074, and p values are 0.068, it can be concluded that Cost does not affect the
Public Preference of transportation modes in Banyumas Regency. This contradicts the previous
hypothesis that H3 = Cost affects Public Preference positively and significantly. The sign of a positive
coefficient on Cost means that the condition of Cost in transportation modes in Banyumas Regency
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 715
on tariff affordability, tariff competitiveness, tariff suitability, and price with benefits does not affect
the preferences of the people of Banyumas Regency. People do not consider the cost of using
transportation modes and tend to prefer comfort and safety in the Banyumas Regency. The results of
this study are in line with research from Alessandrini et al., (2014), Where public preference shows a
relatively high number when supported in terms of facilities, but based on economics does not show
significance for the choice of transportation modes based on the results of European surveys.
However, the results of this study are different from the research (Goulas et al., 2023), where the cost
affects the choice of mode of transportation in Athens. This study is also reviewed based on
transportation provided free of charge.
4. Safety Towards Public Preference
After testing the hypothesis above, it shows that the Safety variable significantly affects the
Public Preference of transportation modes in Banyumas Regency. Where the results of the SmartPLS
Safety calculation show that the Original Sample is 0.135, the Sample Mean is 0.137, the standard
deviation is 0.055, and p values are 0.014, it can be concluded that Safety affects the Public Preference
of transportation modes in Banyumas Regency. This contradicts the previous hypothesis that H4 =
Safety positively and significantly affects Public Preference. The sign of a positive coefficient on Safety
means that the Safety conditions in transportation modes in Banyumas Regency on travel safety,
safety in waiting places, and fearlessness on the way are suitable based on the preferences of the
people of Banyumas Regency. The mode of transportation in Banyumas Regency is relatively safe, so
people do not hesitate to use transportation modes in Banyumas Regency.
The results of this study show similarities and a significant influence of transportation safety
on public preferences, and this is very common because safety is the main factor of a fleet or mode of
transportation to be used, especially the selection of transportation modes with a high level of
security dramatically affects people's preferences in using public transportation (Chai et al., 2022;
Jain et al., 2014).
5. Environment Towards Public Preference
After testing the hypothesis above shows that the environmental variable has no significant
effect on the Public Preference of transportation modes in Banyumas Regency. Where the results of
the SmartPLS Environment calculation show that the Original Sample is 0.101, the Sample Mean is
0.097, the standard deviation is 0.055, and p values are 0.112, it can be concluded that the
Environment does not affect the Public Preference for transportation modes in Banyumas Regency.
This contradicts the previous hypothesis that H5 = Environment positively and significantly affects
Public Preference. The sign of a positive coefficient in the Environment means that the condition of
the Environment in transportation modes in Banyumas Regency on environmental concerns,
environmentally friendly products, price-friendly products, and product brand image does not affect
the preferences of the people of Banyumas Regency. People feel that the mode of transportation has
yet to be distributed to environmental friendliness. Besides that, the community also needs to think
about the mode of transportation used to impact the environment. Based on the analysis of this
research is very contrary to research from (Ambarwati et al., 2017; Bernasconi et al., 2009), Where
public preferences in choosing modes of transportation in terms of the environment are very
influential. These people see automated transportation structures' visual strengths and weaknesses
in urban environments. They can help plan similar transportation systems.
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 716
6. Availability, Comfort, Cost, Safety, and Environment Towards Public Preference
The statement refers to the results of regression analysis (or linear regression model)
conducted to understand the relationship between availability, comfort, cost, environment, safety,
and public preference variables. Here is an explanation of the main elements of the statement:
1. R Square (R^2) worth 0.545 > 0.50:
- R Square measures the extent to which variations in the dependent variable (in this case, Public
Preference) can be explained by independent variables (Availability, Comfort, Cost,
Environment, and Safety).
- The given Square value (0.545) indicates that approximately 54.5% of the variation in Public
Preference can be explained by the combination of Availability, Comfort, Cost, Environment, and
Safety variables in this regression model.
- The number 0.50 is often used as a lower bound to determine how well the model can account
for variations in data. An R Square value greater than 0.50 indicates that the model has a good
level of explanation.
2. Significant influence and moderate value:
- The statement states that the variables Availability, Comfort, Cost, Environment, and Safety
significantly affect Public Preference.
- The "moderate" rating refers to interpreting the strength of the relationship between these
variables and the Public Preference. According to Harahap (2020), the criterion "moderate" is
used to describe relationships that are strong enough or relevant. Judging from the medium
criteria, according to Harahap (2020), if = or > 0.50:
- The statement refers to the criteria or standards used by Harahap in 2020 to assess the strength
of the relationship/regression.
In this context, an R Square value equal to or greater than 0.50 is considered an indicator that
the variables Availability, Comfort, Cost, Environment, and Safety have a strong enough influence on
Public Preference. So, in conclusion, the results show that the variables Availability, Comfort, Cost,
Environment, and Safety have a significant and moderate effect on Public Preference, as measured by
an R Square value greater than 0.50.
4. Conclusion
Conclusion of data from research on public preferences for transportation modes in (Banyumas
Regency: 1) Availability: The positive coefficient on Availability shows that fleet availability, ease of
use, and fleet completeness in Banyumas Regency are suitable according to community preferences.
However, it is necessary to update the fleet, especially in certain areas/routes with old/expired
vehicles. 2) Comfort: The positive coefficient of Comfort shows that the convenience of access,
transactions, and transportation benefits in Banyumas Regency are suitable according to community
preferences. However, high congestion during rush hour can create inconvenience, and adjusting the
headway/distance between buses during rush hour is necessary. 3) Cost: The positive coefficient
indicates that the cost increase does not influence people's preferences positively. Affordability and
fare competitiveness are not significant factors, with preference more likely to be influenced by travel
comfort and safety. 4) Safety: A positive coefficient on Safety indicates that a higher safety level
positively influences people's preferences in Banyumas Regency. Maintenance and improvement of
safety can be an effective strategy to increase public acceptance of transportation modes. 5)
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 717
Environment: Environment variables do not significantly affect Community Preferences in Banyumas
Regency. People do not perceive the significant contribution of modes of transportation to the
environment, and environmental impact is not a significant consideration in shaping preferences for
modes of transportation.
With these findings, the analysis provides valuable insights into the factors influencing people's
preference for transportation modes in Banyumas Regency. Recommendations include fleet renewal,
increased comfort during peak hours, evaluation of fare policies, and improved safety as strategies to
increase public acceptance of transportation services.
5. References
Adisasmita, S. A. (2012). Perencanaan Infrastruktur Transportasi Wilayah (1st ed., Vol. 1). Graha Ilmu.
Alessandrini, A., Alfonsi, R., Site, P. D., & Stam, D. (2014). Users’ Preferences towards Automated Road
Public Transport: Results from European Surveys. Transportation Research Procedia, 3, 139–
144. https://doi.org/10.1016/j.trpro.2014.10.099
Ambarwati, L., Verhaeghe, R., Arem, B. van, & Pel, A. J. (2017). Assessment of transport performance
index for urban transport development strategies — Incorporating residents’ preferences.
Environmental Impact Assessment Review, 63, 107–115.
https://doi.org/10.1016/j.eiar.2016.10.004
Batarce, M., Muñoz, J. C., de Dios Ortúzar, J., Raveau, S., Mojica, C., & Ríos, R. A. (2015). Use of Mixed
Stated and Revealed Preference Data for Crowding Valuation on Public Transport in Santiago,
Chile. Transportation Research Record: Journal of the Transportation Research Board, 2535(1),
73–78. https://doi.org/10.3141/2535-08
Bernasconi, C., Strager, M. P., Maskey, V., & Hasenmyer, M. (2009). Assessing public preferences for
design and environmental attributes of an urban automated transportation system. Landscape
and Urban Planning, 90(3–4), 155–167. https://doi.org/10.1016/j.landurbplan.2008.10.024
Cannas, V. G., Ciccullo, F., Pero, M., & Cigolini, R. (2020). Sustainable innovation in the dairy supply
chain: enabling factors for intermodal transportation. International Journal of Production
Research, 58(24), 7314–7333. https://doi.org/10.1080/00207543.2020.1809731
Cattaneo, M., Malighetti, P., Morlotti, C., & Paleari, S. (2018). Students’ mobility attitudes and
sustainable transport mode choice. International Journal of Sustainability in Higher Education,
19(5), 942–962. https://doi.org/10.1108/IJSHE-08-2017-0134
Chai, N., Zhou, W., & Hu, X. (2022). Safety evaluation of urban rail transit operation considering
uncertainty and risk preference: A case study in China. Transport Policy, 125, 267–288.
https://doi.org/10.1016/j.tranpol.2022.05.002
Chowdhury, Md. S., Osman, Md. A., & Rahman, Md. M. (2018). Preference-Aware Public Transport
Matching. 2018 International Conference on Innovation in Engineering and Technology (ICIET),
1–6. https://doi.org/10.1109/CIET.2018.8660857
Goulas, E., Kontaxi, A., & Yannis, G. (2023). Free Public Transport in Athens: a stated preference
approach. Transportation Research Procedia, 72, 926–932.
https://doi.org/10.1016/j.trpro.2023.11.519
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in
international marketing (pp. 277–319). https://doi.org/10.1108/S1474-
7979(2009)0000020014
e-ISSN: 2723-6692 🕮P-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 4, April 2024 718
Ho, C. Q., Mulley, C., & Hensher, D. A. (2020). Public preferences for mobility as a service: Insights from
stated preference surveys. Transportation Research Part A: Policy and Practice, 131, 70–90.
https://doi.org/10.1016/j.tra.2019.09.031
Ismail, R., Hafezi, M. H., Mohd Nor, R., & Ambak, A. (2012). Passengers preference and satisfaction of
public transport in Malaysia. Australian Journal of Basic and Applied Sciences, 6(8), 410–416.
Jain, S., Aggarwal, P., Kumar, P., Singhal, S., & Sharma, P. (2014). Identifying public preferences using
multi-criteria decision making for assessing the shift of urban commuters from private to public
transport: A case study of Delhi. Transportation Research Part F: Traffic Psychology and
Behaviour, 24, 60–70. https://doi.org/10.1016/j.trf.2014.03.007
Marsikun, I. M., Zaelani, A., Faozanudin, M., & Kurniasih, D. (2023). Implementasi Program Buy The
Service Kementerian Perhubungan Pada Transportasi Massal Di Kabupaten Banyumas.
Innovative: Journal of Social Science Research , 3(2), 5167–5180.
Matubatuba, R., & De Meyer-Heydenrych, C. F. (2022). Moving towards smart mobility: Factors
influencing the intention of consumers to adopt the bus rapid transit (BRT) system. Cogent
Business & Management, 9(1). https://doi.org/10.1080/23311975.2022.2089393
Miller, P., de Barros, A. G., Kattan, L., & Wirasinghe, S. C. (2016). Public transportation and
sustainability: A review. KSCE Journal of Civil Engineering, 20(3), 1076–1083.
https://doi.org/10.1007/s12205-016-0705-0
Nasution, M. N. (2004). Manajemen Transportasi (2nd ed., Vol. 1). Ghalia Indonesia.
Putro, P. A., Malkhamah, S., & Muthohar, I. (2022). Konektivitas danAksesibilitas Bus Trans Banyumas
Berdasarkan Persepsi Pengguna. Journal of Syntax Literate, 7(9).
Savitri, F. N. (2022, February 15). Tantangan Pembangunan Infrastruktur Transportasi Berkelanjutan
di Indonesia. Institut Teknologi Bandung. https://www.itb.ac.id/berita/tantangan-
pembangunan-infrastruktur-transportasi-berkelanjutan-di-indonesia/56543
Soza-Parra, J., Raveau, S., Muñoz, J. C., & Cats, O. (2019). The underlying effect of public transport
reliability on users’ satisfaction. Transportation Research Part A: Policy and Practice, 126, 83–93.
https://doi.org/10.1016/j.tra.2019.06.004
Steenbrink, P. A. (1974). Transport network optimization in the Dutch integral transportation study.
Transportation Research, 8(1), 11–27. https://doi.org/10.1016/0041-1647(74)90014-8
Tuffour, M., & Asiama, R. K. (2023). Public transport preferences amongst Ghana’s urban dwellers.
International Journal of Social Economics, 50(3), 419–435. https://doi.org/10.1108/IJSE-05-
2022-0360