International Journal of Engineering Advanced Research eISSN: 2710-7167 | Vol. 5 No. 2 [June 2023]
Journal website: http://myjms.mohe.gov.my/index.php/ijear
LAND AND SEA ISLAND INTERMODAL NETWORK INTEGRATION IN DKI JAKARTA PROVINCES,
INDONESIA
Askia Esa Aulia1*, R. Jachrizal Sumabrata2 and Nahry3
1 2 3 Faculty of Engineering, University of Indonesia, Depok, INDONESIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 18 April 2023 Revised date : 2 May 2023 Accepted date : 23 May 2023 Published date : 6 June 2023 To cite this document:
Aulia, A. E., Sumabrata, R. J., &
Nahry, N. (2023). LAND AND SEA ISLAND INTERMODAL NETWORK INTEGRATION IN DKI JAKARTA PROVINCES, INDONESIA.
International Journal of Engineering Advanced Research, 5(2), 22-31.
Abstract: Network integration between land and water modes in Kepulauan Seribu regency, DKI Jakarta Province is crucial to sustain significant trip distribution. This study is to examine how scheduling and physical infrastructure impact network integration.
The technique employed is to elicit thoughts from respondents regarding the current physical infrastructure and timetables. Structural Equation Model (SEM) Partial Least Square (PLS) analysis was used to examine the data after its stated valid and reliable, which provided indicators with a significance of 24% between the independent and dependent variables (poor significance). However, there is a significant correlation between the independent variables (infrastructure, time, service, and special facilities) and its indicators. The study's give a general picture of the variables influencing network integration between land- and sea-based modes in the Jakarta Islands, which helps to raise the quality of transportation in the area.
Keywords: Intermodal, Network integration, Structural Equation Model Partial Least Square (SEM-PLS).
1. Introduction
Integrating all modes of transportation used to carry people and/or products from one location (of origin) to another is known as intermodal transportation (Cortés, 2011; Rodrigue, 2020).
Transportation systems can prioritize serving the requirements of people through coordinating payment methods, timetables, intermodal connections, information, and service (Skinner, 2000; Schoeman, 2017). Good integration can create an efficient public facility for residents and have a good impact on urban order (Schoeman, 2017; Du, 2020).
The land mode and the sea mode are the two available public transportation options in Jakarta, Indonesia. (Kurniawati, 2010), but currently, these modes have an integration system that is not optimal (Kurniawati, 2010).
According to calculations of trip generation and passenger trip attraction in each zone of Kepulauan Seribu regencys, the Jakarta Zone has the highest levels of both (Mahardika, 2020).
This is due to the Jakarta Zone being a sizable metropolis that serves as Kepulauan Seribu regencys' primary hub for sea transportation, which accounts for the majority of movements to and from the zone (Mahardika, 2020). There is a need for a transfer system that can focus on network integration between the island's existing land and sea modes because of the high number of trips being generated (Kurniawati, 2010).
Figure 1: Jakarta Water Transportation Map Source: https://www.transportforjakarta.com/
The situation is not accompanied by good network integration because of the large trip generation between Jakarta and Kepulauan Seribu regencys. The goal of this research was to bridge the gap between the integration that now exists and the integration that is ideal.
2. Literature Review
The goal of the transportation system is to move people, objects, or data efficiently while also ensuring their safety, comfort, and smoothness. (Tamin, 2008). There are various types of intermodal integration, one of which is network integration, which combines physical and scheduling integration.
According to (Ólafsdóttir, 2017) waiting time is one of the metrics used to assess how well a transportation service is performing. Customer satisfaction is impacted by wait times, which impacts the evaluation of disseminated questionnaires (Girma, 2022).
One of the most crucial operational characteristics of public transportation services is service reliability (Redman, 2013). In timed-transfer systems, (Dorbritz, 2009) addressed how being on time is crucial because even minor delays in getting to timed-transfer points might result in users missing connections. Delays and missed connections were shown to be a main source of anxiety related to riding on routes involving transfers (Cheng, 2010). Several of studies on timetable scheduling have been done to determine methods of improving reliability (Carey, 1994). (Furth, 2009) research focused on how improved planning can reduce users' transfer- related discomfort. Results highlighted the need for optimum offset in schedule design to minimise missed connections and minimize transfer waiting times.
3. Method
SEM PLS analysis is used to determine whether there is a significant relationship between the independent and dependent variables. Validity tests and reliability tests are performed to determine whether a question on the questionnaire that will be distributed is a valid and reliable measuring instrument.
3.1 Materials 3.1.1 Samples
The data collected is using questionnaires distributed to users of transportation modes from and to Jakarta to the thousand islands. According to (Mahmud, 2011), the minimal sample size for studies including statistical data analysis is 30. (Gay, 2009) state that a sample size of 30 respondents is necessary for a correlation study. (Gye-soo, 2016) asserts that this study has a gap since the sample size for SEM PLS requires ten times the number of variables.
3.1.2 Procedures
At Muara Angke Port, questionnaires with inquiries on current physical infrastructure and timetables were handed out offline. A Likert scale was employed in this study to evaluate participants familiarity with, objectivity toward, and perspectives on a social issue. The variables that need to be altered will convert into variable indicators based on the Likert scale.
Each instrument that uses a Likert scale has answers that range from very positive to very negative.
The variables used in this study are independent variables which can be seen in Table 1 and dependent variables which can be seen in Table 2.
Table 1: Independent Variable
No Variable No Code Indicators Reference
1 Infrastructure (X1)
1 X1.1 There is a Port Benchmarking
2 X1.2 Which port is used (Anita Sanda
Pusparini, 2022)
3 X1.3 There is a waiting room (Anita Sanda
Pusparini, 2022; Cao, 2017)
4 X1.4 There is a toilet (Anita Sanda
Pusparini, 2022;
Sitorus, 2019)
5 X1.5 There is a cafeteria (Anita Sanda
Pusparini, 2022; Cao, 2017)
6 X1.6 There is a pedestrian walk (Anita Sanda
Pusparini, 2022; Cao, 2017) 7 X1.7 There are road safety facilities (Anita Sanda
Pusparini, 2022;
Atombo, 2021) 8 X1.8 The port location is easy to reach (Anita Sanda
Pusparini, 2022; Cao, 2017)
9 X1.9 There is CCTV (Anita Sanda
Pusparini, 2022;
Sitorus, 2019) 2 Time (X2) 10 X2.1 Time required to switch modes (Cao, 2017;
Ólafsdóttir, 2017) 11 X2.2 The existing time for switch modes is too
long
(Ólafsdóttir, 2017)
12 X2.3 Expected time for switch modes (Ólafsdóttir, 2017) 13 X2.4 Waiting time when switching modes (Cao, 2017;
Ólafsdóttir, 2017) 14 X2.5 The existing of waiting time is too long (Ólafsdóttir, 2017)
15 X2.6 Expected time of waiting time (Ólafsdóttir, 2017)
16 X2.7 How often trips are made in a week (Cao, 2017;
Ólafsdóttir, 2017) 3 Service (X3) 17 X3.1 There are arrival and departure schedules (Atombo, 2021;
Aurora, 2020) 18 X3.2 There is fare information (Atombo, 2021;
Aurora, 2020) 19 X3.3 There is information on other transportation (Atombo, 2021;
Aurora, 2020)
No Variable No Code Indicators Reference 20 X3.4 There is road access to the parking lot (Atombo, 2021;
Aurora, 2020) 21 X3.5 There is a service officer (Aurora, 2020; Sitorus,
2019) 22 X3.6 There is a service officer who serves (Atombo, 2021;
Girma, 2022) 23 X3.7 There is a security guard (Girma, 2022; Sitorus,
2019) 4 Special facilities
(X4)
24 X4.1 There are stair facilities for disabled passengers
(Andriani, 2019)
25 X4.2 There is a toilet for disabled passengers (Andriani, 2019)
26 X4.3 There is an access road for passengers with disabilities
(Andriani, 2019)
27 X4.4 There is a nursing mother’s room (Andriani, 2019)
28 X4.5 There is a prayer room (Andriani, 2019)
29 X4.6 There is a health room (Andriani, 2019)
30 X4.7 Emergency medical equipment is available (Andriani, 2019)
Infrastructure, which is at the centre of activity while transferring, time, which considers how smoothly the transfer goes, services, which support things in terms of infrastructure, and special facilities, which support the infrastructure to be used, are the independent variables employed.
Table 2: Dependent Variable
No Variable No Code Indicators Reference
1 Integration (Y1)
1 Y1.1 Existing facilities already support land and sea transportation movements
Bechmarking
2 Y1.2 Existing services support transportation movement
Bechmarking
3 Y1.3 Services that support (Anita Sanda
Pusparini, 2022; Cao, 2017)
Based on the responses to the questions posed about the previous independent variable, the dependent variable seeks to assess how helpful the current transfer between sea and land modes is.
In this study, the independent and dependent variables are analysed for correlation. To ascertain the presence of a correlation, the findings of the questionnaire were first checked for validity and reliability before being subjected to PLS-SEM analysis.
3.2 Data Analysis
3.2.1 Validity and Reliability
The criteria measured on the outer model include:
1. The factor loading value on the latent variable with associated indications is known as convergent validity, the expected value is ≥ 0.7
2. Cross loading value, or discriminant validity, which compares whether the loading value for the intended variable is greater than the loading value for other variables.
3. The expected Average Variance Extracted (AVE) value is> 0.5. This means that one latent variable can explain more than half of the variance of its indicators on average.
4. Internal coherence for internal consistency indicator values, Cronbach Alpha is the lower limit and composite reliability is the upper limit. In exploratory research, the minimal predicted value is 0.70 or 0.60. While 0.95 is the upper limit to prevent indicator redundancy.
5. According to (Hair, 2014), if the composite reliability value is greater than 0.70, a construct is said to have a high-reliability value.
Furthermore, the link between latent constructs is examined using the structural model test.
The coefficient of determination for endogenous constructions is known as the R Square value.
The R square values are 0.67 (strong), 0.33 (moderate), and 0.19 (weak), according to Chin (1998).
4. Results and Discussion
The validity and reliability outcomes of the PLS-SEM after numerous iterations are displayed in Table 3:
Table 3: Validity and Reliability Cronbach's
Alpha
Composite Reliability
Average Variance Extracted (AVE)
X1 0.913 0.929 0.651
X2 0.876 0.936 0.880
X3 0.828 0.896 0.742
X4 0.950 0.963 0.867
Y1 0.760 0.861 0.675
According to the analysis's findings, the existing data may be regarded as legitimate because its Cronbach's Alpha value is greater than 0.7, and it can also be regarded as dependable because its Composite Reliability value is greater than 0.7. AVE value is> 0.5, this value illustrates adequate convergent validity which means that a latent variable is able to explain more than half of the variance of its indicators on average.
The creation of a structural model is the following step, as shown in Figure 2.
Figure 2: Structural Model Construct
Discriminant Validity (Cross Loading) value, all values are more than 0.7, indicating a correlation between the indicators and their constructs as shown in Figure 2.
Furthermore, the R square value is 0.242, which indicates that the influence of variable X (infrastructure, time, service, and special facilities) on variable Y, or intermodal integration, is only 24% (weak) as seen in Table 4, Meanwhile according to R adjusted, there’s just 12.5%
for the signification. However, there is a strong link between variable X and its indicators.
There is a strong association between the availability of waiting areas, restrooms, canteens, pedestrian walk, road safety facilities, CCTV, and port sites with the infrastructure variable (X1). The waiting time for switching modes, which is categorized as long, and the waiting time both have substantial correlations with the time variable (X2). There is a strong association between the service variable (X3), the number of serving service officers present, and the frequency of the trip made each week. There is a strong association between the availability of accessible staircases, accessible restrooms, accessible roads, and emergency medical equipment for the special facilities variable (X4).
Table 4: R Square Values R
Square R Square Adjusted
Y1 0.242 0.125
Table 5 displays the results of a hypothesis test using T statistics. If the results are more than 1.96 (two-tailed), the hypothesis is rejected because variable X is not significantly correlated with variable Y. If the P value is less than 0.05, the hypothesis is accepted. Based on the result, all the hypothesis are rejected because t-statistics <1.96.
Table 5: Hypothesis Test
T Statistics (|O/STDEV|) P Values
X1 -> Y1 1.411 0.159
X2 -> Y1 0.764 0.445
X3 -> Y1 0.677 0.499
X4 -> Y1 0.449 0.653
The tests that have been run can validate that the data obtained is genuine and reliable, allowing it to be continued into the following analysis, namely SEM PLS. According to the findings of SEM PLS, there is no correlation between integration (variable Y) and facilities, time, services, and special facilities (variable X). These findings may be a result of the small sample size (30), compared to the 330 samples required for SEM PLS (Gye-soo, 2016). This is this study's shortcoming. (Aurora, 2020)
5. Conclusion
This study rates the physical infrastructure, wait times, services, and specialized facilities for land and sea intermodal integration in Jakarta. 30 participants were surveyed to gather information as they integrated. Validity and reliability tests were run on the existing data in this study, which produced reliable and valid data. A significance of 24% was found between the independent factors (infrastructure, time, service, and special facilities) and the dependent variable (integration) after SEM PLS analysis was completed. The results are barely significant. This may happen since the current sample has fewer than 330 respondents (Gye- soo, 2016), which can serve as a starting point for future studies.
(Anita Sanda Pusparini, 2022), (Aurora, 2020), (Girma, 2022), and (Ólafsdóttir, 2017) are a few studies that have previously investigated the possibility of taking physical facilities, waiting times, services, and special facilities into account when evaluating integration in various cities and nations.
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