Choice Model and Influencing Factors of the Travel Mode for Motorcycle and BRT-lite in Banda Aceh, Indonesia
Sugiarto Sugiarto
1,*), Miftahul Jannah Huta Barat
1), Sofyan M. Saleh
1), Ashfa Achmad
2), Irham Iskandar
3)1Department of Civil Engineering, Universitas Syiah Kuala, Darussalam, Banda Aceh 23111, Indonesia
2Department of Architecture and Planning, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
3Regional Development Planning Agency, BAPPEDA Aceh, Banda Aceh, Indonesia, Banda Aceh 23111, Indonesia
1Corresponding author: [email protected]
1[email protected]; [email protected]; 2[email protected]
Abstract. The City of Banda Aceh continues to experience rapid development both in terms of economy, infrastructure, and transportation which has resulted in increased autos mobilities. This increased mobility has an impact on the increasing tendency to use motorcycles which harm the environment for instances traffic congestion, air pollution, noise, and accidents. The Government of Aceh has implemented a policy called Bus Trans Koetaradja with the concept of Bus Rapid Transit-Lite (BRT-lite), but due to inaccuracy and lack of exclusive lanes, it has caused people to be reluctant to shift from private mode to the urban Bus. Therefore, this aiming at investigates the determinants of shifting travel mode choice from motorcycle to BRT-lite based travel survey conducted in Banda Aceh using the contexts of Stated Preference. Due to pandemic Covid-19, the data was collected using online-based questionnaire survey using Google form. A total of 400 data is valid and used for the analysis of travel mode choice using Binary Logistic approach. The results of the binary logistic regression analysis reveal that determinants of selecting motorcycle as a representative daily mode used is the distance to make a trip, travel time within origin-destination, and vehicle operating costs. The probability of selecting travel modes is 70% and 30% for motorcycle and BRT-lite, respectively.
Keywords: BRT-lite, Motorcycle, Mode choice, Binary logistic, Banda Aceh.
INTRODUCTION
The development of a country is always in line with the development of its transportation services due to its significant role in improving the country's social and economic. In developing countries, the use of motorcycles continues to increase every year which significantly manifested congestion-related problem such economic loss, increasing travel time, and additional fuel consumption [1]. One of promising strategy to reduce the effects of motorcycle use is to promote the use of public transportation such as initiating Bus Rapid Transit especially in Indonesia [2] and Malaysia [3].
In case of Banda Aceh city, the Trans Koetaradja, a type of urban bus reform, it has been reported as an affective policy that able to reduce congestion within city center [4] but people to be reluctant to shift their mode to Trans Koetaradja due to inflexibility route [5]. Furthermore, the current Trans Koetaradja is operated within type C of public transport. That is operated within mixed traffic and there is no exclusive lane assigned for this bus system. Therefore, the Government has considered to improve the current bus into type B of public transport system. Within type B public transport system, the bus will have an exclusive lane and it refers to BRT-line [6].
In transport planning processes, the improvement of policy or introduce new an alternative policy is necessary to conduct the demand analysis. The aggregate demand modeling mainly consists of trip generation, trip attraction, trip distribution, mode choice and road choices. The disaggregate approach allow more flexibility in demand analysis as it drawn from individual level of travel behavior. In such case, the mode choice can be regarded as the most important part in demand forecasting. The modeling of the travel mode choice in transportation forecasting has considered several attributes that correlate with the mode choice provides a mathematical reference that is used to estimate the number of trips made by each mode according to individual level of travel attributes and corresponding to their social economic characteristics. This paper aims to investigate the modal split among motorcycle and BRT-lite and its influencing factors in Banda Aceh city based Stated Preference designed questionnaire. Due to Covid 19 Pandemic, the questionnaires were distributed and collected using online survey which conducted in Banda Aceh city in 2021.
The remaining part of this paper represents the materials and methods used in analysis and the conclusion will be summarized in the end of this paper.
RESEARCH MATERIALS AND METHODS Modeling approach
The proposed analysis of the mode choice of transportation is based on disaggregated data characterizing individual travel behavior. The disaggregate model treats each person independently, thus providing a broader prediction of behavior. The binary logit has been widely used in predicting travel demand and its contributing factors.
These models relate individual characteristics to more effective mode choice behavior and provide more accurate estimates of the elasticity of demand. Sugiarto et al [7] used binary model to predict mode choice during emergency evacuation in Banda Aceh. Comprehensive reference related to discrete choice model including binary choice can be referred to the transport modelling book [8-9]. In this study, we adopted the model formulation used by [7] to predict modal split and its relevant factors for motorcycle and BRT-lite. Assumed that, an individual i asked to choose one of two options (k), i.e., motorcycle and BRT-lite for their travel mode during distributed questionnaire as a representative mode. Then, the formulation of the utility function (U) and probability (P) of selecting mode choice can be drawn as:
U𝑘(𝚾𝑘|𝛃𝑘) = 𝜷𝐾𝑇𝚾𝑘+ 𝛆𝑘 (1)
P𝑖𝑘= exp(𝜷𝐾𝑇𝚾𝑖𝑘)
∑𝐾𝑝=1exp(𝜷𝑝𝑇𝚾𝑖𝑘)
(2)
where X is input variable or explanatory variables, β represents unknown parameters which need to calibrated using the input data and ε is systematic disturbance which is to be identical and independent (IID) and assumed to be type II Gumbel distributed. Model selection is conducted based on the Goodness of Fit model and the statistically significant of the estimated parameters. As for the goodness of fit, the coefficient of determination of rho-squared and the log-likelihood ratio (LR) test is conducted based on chi-squared distribution. Both LR test and significant parameters are considered to have significant error less than or equal of 5% error level.
Sampling and sample size
In this study, the determination of the number of samples was taken by taking the average of the calculation using Krejcie and Morgan formula and Slovin formula. The Krejcie and Morgan and Slovin formula can be drawn, respectively as:
n = X𝟐𝑵𝑷(𝟏−𝑷)
d𝟐 (𝑵−𝟏)+ X𝟐𝑷(𝟏−𝑷)) (3)
n = 𝑵
𝟏+(𝑵 𝒙 Ԑ𝟐) (4)
Where n is number of samples, N is total population, P is proportion of population (0.5), d is degree of accuracy (5%),
χ2 is the value of the chi-square used in the analysis, and ε is error level (error rate) generally used 1%, 5% and 10%.
By employing equation (3) and (4) the total of sample requires in this study is about 400 samples. Using self-reported experience online survey, a total of 470 samples has collected and 400 samples are valid and used in this paper.
Survey procedure
Stated preference (SP) is a method to find out the opinion of respondents in facing various choices offered through online-based survey. The survey with the SP method has several conveniences, one of which is as a strong basis for respondents' statements (travelers) regarding what they will feel in using real alternative modes of transportation such as vehicle operating costs, travel time, and other attributes.
In this study, the questionnaire was distributed online in the form of a google form with a choice of answers according to events in the field (before Covid-19). The criteria for respondents in this study are respondents who live in the city of Banda Aceh who are 17 years old and have their income (working) and has experienced both modes of transportation, i.e., motorcycle, and Bus Trans Koetaradja. The study area and bus line can be seen in Figure 1.
(a) (b)
FIGURE 1. study area: (a) Banda Aceh area; (b) the bus lines
In this case, prior to selecting their mode, we introduced the BRT-lite asked their willingness to choose and shift to BRT-lite from motorcycle mode. Samples were taken using probability sampling techniques, which are sampling techniques that provide equal opportunities for each member of the population. This technique is a type of random sampling, which is a random sampling technique. The respondents were also given a scenario for alternative mode choices by giving an illustration of what advantages Bus Rapid Transit (BRT) has over other modes as the public mode offered. The following is a mode choice scenario table with stated preference techniques.
TABLE 1. Mode choice scenario with SP technique
Service Motorcycle Existing Bus
(Trans Koetaradja) Proposed new system (BRT-Lite)
Cost > Rp. 150 / Km Government
subsidies (free) Government subsidies (free) Comfort Comfortable, flexible There are 5 corridors There are 5 corridors
Headway - 15 minutes 5-10 minutes
Lanes Join other vehicles Join other vehicles Has a special lane
Analysis procedure and data setting
As mentioned above, in this study the binary logit as taking into consideration in modeling mode choice. By simplifying the logit form in equation (2), the probability of selecting motorcycle (MC) or BRT-lite using the binary logit form can be specified, respectively as:
PMC = expUMC
(1+expUMC)
(5)
PBRT-lite = 1 – PMC (6)
TABLE 2. Explanatory variable setting
Variable Setting
data Codes setting used in the utility function of the model Mode choice Binary 1
(BRT-lite) 0 (MC) Travel destination
distance Ordinal 1 2 3 4 5 -
<4 Km 4-6.9 Km 7-9.9 Km 10-12.9
Km > 13
Km -
Traveling time Ordinal 1 2 3 4 5 6
<10 min. 10-19 Min. 20-29 Min.
30-39 Min.
40-49 Min.
> 50 Min.
Vehicle operating
costs Binary 1 0 - - - -
Rp. 150 Rp. 0 - - - -
Gender Binary 1 0 - - - -
male female - - - -
Level of
education Ordinal 1 2 3 4 - -
Primary
school Diploma Bachelor Post
graduate - -
Ownership of
motorbikes Continue 1 2 3 >4 0 -
Ownership of
driver’s license Binary 1 0 - - - -
Yes Not - - - -
All data listed in the questionnaire were coded for several levels according to the Likert scale to obtain a comprehensive evaluation of each attribute as describes in Table 2. Some variables are categorized into two levels, such as the mode of transportation, i.e. (MC = 0 and BRT-Lite = 1), driver’s license ownership, i.e. (Yes = 1 and No
= 0), while other variables for 6 levels, such as occupation and monthly income. Several other variables use concepts such as almost every day, 3-4 weeks, 1-2 weeks, <1 time a week, and never. Therefore, for analysis purposes, the levels for the same concept are combined into one level or converted into a dummy form by taking the mean value of each attribute then making it i.e., <3 = 1 the rest = 0. This is because the software handles each level differently, so it does not reflect the actual situation.
RESULTS AND DISCUSSIONS
Table 3 summarizes the significance of each attribute that has been tested using bivariate correlation. The P-value helps to decide whether there is a correlation between the dependent and independent variables or not. The lower the P-value means that the higher the level of correlation among the variables. Variable with a P-value less than 0.05 were significantly correlated, while those greater than 0.05 were considered insignificant. Therefore, there are seven significant attributes with the mode choice set in this study, namely the distance of travel destination, travel time, vehicle operating costs, gender, latest education level, motorcycle ownership, and driver’s license ownership.
TABLE 3. Correlation result among explanatory variable of mode choice
Parameters Coefficient of
correlation P-Value
Travel time to the bus stop (X1) 0.215 0.095
Purpose of the trip (X2) 0.774 0.697
Travel destination distance (X3) -5,862 0.000
Origin zone district (X4) 0.195 0.018
Destination zone district (X5) 0.081 0.334
Travel time with representative modes (X6) -0.351 0.031
Vehicle operating costs (X7) 14,623 0.000
Gender (X8) 0.975 0.060
Age (X9) -0.351 0.317
Education level (X10) -0.486 0.045
Occupation (X11) -0.317 0.061
Monthly income (X12) -0.096 0.528
Motorcycle ownership (X13) 0.252 0.238
Driver’s license Ownership (X14) 0.732 0.139
TABLE 4. Calibrated parameters of mode choice (based outcome MC) using binary logit
Variables Coefficient P-Value
Parameters:
Intercepts -7,114 0.000
Travel Destination distance (X3) 2,278 0.001
Travel time (X6) 0.473 0.000
Vehicle operating costs (X7)) 2,715 0.000
Gender (X8) 0.648 0.035
Level of education (X10) 0.409 0.006
Ownership of motorcycle (X13) 0.324 0.018
Ownership of driver’s license (X14) 0.709 0.039
Summary of Statistics:
Sample (N) 400
χ2 test (sig) 76.58 (0.000)
Chi-Squared 159,895
Rho-Squared 0.326
All attributes that were significantly correlated with the selected mode by respondents were identified, all uncorrelated or ineligible attributes were excluded, and the analysis was repeated for attributes that met the requirements only for higher accuracy, and the results of this analysis are presented in Table 4.
According to the results of logistic regression analysis that has been carried out several times to obtain attributes that are significantly correlated with the mode choice set of the MC and BRT-lite. The utility function of the MC can be drawn from Table 4 as:
UMC = -7,114 + 2,278 (X3) + 0,473 (X6) + 2,715 (X7)) + 0,648 (X8) + 0,409
(X10) + 0,324 (X13) + 0,709 (X14) (7)
According to the utility function in equation (7), the contributing factors of selecting the MC as representative mode are travel destination, travel time, vehicle operating costs, male gender, education level, motorcycle ownership, driver’s license as it shown in Table 4. The model goodness of fit model has shown in the summary of statistic in the bottom of Table 4.
TABLE 5. Level of model accuracy
Mode Observation Estimate Discrepancy
Motorcycle (MC) 70 81 0.136
Bus Rapid Transit-Lite
(BRT-Lite) 30 19 0.367
CONCLUSIONS
The main conclusions of this study can be summarized as follows:
a) The determinants factors of selecting motorcycle as representative daily mode in Banda Aceh consist of travel destination, travel time, vehicle operating costs, gender, education level, motorcycle ownership, and driver’s lisence ownership.
b) The probability of modal split among mode choice set obtained using binary logit are 70%, 30%, for Motorcycle and BRT-lite, respectively.
c) The derived model is considered very good based on discrepancy among abserved and predicted value about 0.136 and 0.367 for Motorcycle and BRT-lite, respectively.
ACKNOWLEDGEMENTS
The author would like to express the deepest gratitude and appreciation to Universitas Syiah Kuala for financially support this research under contract No. 308/UN11.2.1/PT.01.03/PNBP/2021 (Penelitian H-Indeks).
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Exploring Mode Choice Model for Motorcycle Transport with Latent Motivation Variables
Sugiarto Sugiarto
*,1), Sofyan M. Saleh
1), Ashfa Achmad
2), Tjut Rizqi Maysyarah Hadi
1)Renni Anggraini
1)1
Department of Civil Engineering, Universitas Syiah Kuala, Darussalam, Banda Aceh 23111, Indonesia
2
Department of Architecture and Planning, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
*Corresponding author: [email protected]
Abstract
Banda Aceh is one of the cities in Indonesia that is experiencing an increase in the ownership of private vehicles. The private cars and motorcycle take part in carrying out mandatory and non-mandatory activities. The increase in private vehicle owners can have an impact on the transportation system. Currently, the Government of Aceh has provided public transportation, named the Trans Koetaradja Bus which is expected to reduce congestion and the use of private vehicles in the city. The purpose of this study is to analyze the effect of individual motivation variable (latently) on their daily mode used, namely private vehicle or the Trans Koetaradja bus taking into consideration on mandatory and non-mandatory activities. The multivariate approach of Confirmatory Factor Analysis (CFA) is used to explore and determine the contributing motivation factors on their selecting daily mode choice. The empirical findings from this study have explicitly investigate the relationship between motivation factors and their attributes/indicators. The first results of the measurement model show that mandatory trips have the highest significant correlation with the attribute of liking to use private vehicles, meanwhile the non-mandatory trips have the most significant sign congestion increasing within the city due to highly autos dependency. The second model is the relationship amongst motivation variables. The results depict that as for mandatory activities appear that linkage with private vehicles significantly affects private vehicle problem, while in non-mandatory trips the quality of service of current bus system has significantly affects satisfaction on using public transport.
Keyword: Mode choice, motivations, factors, mandatory activities, non-mandatory activities, Confirmatory Factor Analysis
INTRODUCTION
According to the data from the Aceh Revenue and Wealth Service, [1] every year the use of motorcycles and private cars have increased quite rapidly, causing traffic jams, parking chaos, increasing traffic accidents and increasing noise emissions. The community is very dependent on private vehicles [2] to carry out daily activities such as mandatory and non-mandatory activities in Banda Aceh City. Community activities are divided into 3 (three) groups, namely mandatory, maintenance, and discretionary. Work and school activities are mandatory activities that were carried out by individuals. Activities such as student activities outside of school hours, daily shopping, shopping at many stores are considered maintenance activities. Other activities such as recreation, social visits, and other fun activities are categorized as discretionary activities. Maintenance and discretionary activities can be grouped into non-mandatory activities [3-6]. To minimize the negative impact of the current traffic jam, the Aceh Government has provided public transportation, namely the Trans Koetaradja Bus which is expected to reduce congestion and the use of private vehicles in Banda Aceh. In previous studies [7-9] on the effectiveness of the implementation in Aceh, it was concluded that Trans Koetaradja is a policy that is acceptable to the community, but generally, people are less interested in using it due to limited route access and non-existent collecting transportation. The Department of Transportation as provider of transportation needs to study by considering the important aspects of the community related to the very increasing use of private vehicles. To find out what motivations/factors influence the choice of mode, the model used is the CFA model, one of the branches of the MIMIC model. In a previous study [10] the MIMIC model was used to obtain psychological determinants estimated as latent variables.
DATA AND DISTRIBUTIONS
This study will use Revealed Preference (RP) data with total of 400 respondents who are divided evenly into 2 groups, for mandatory and non-mandatory activities by distributing questionnaires using Google Form. Questionnaires were distributed to respondents with the target of 17 years old, had used a private vehicle and Trans Koetaradja Bus domiciled in Banda Aceh City and its surroundings as shown in figure 1. Furthermore, tables 2 - 9 are a summary of respondents' perceptions.
Figure 1. study area: Banda Aceh city (Google Earth, 2021)
In this study, respondents were asked about indicators of linkage to private vehicles, factor of private vehicle problems, quality of Trans Koetaradja and its satisfaction. The questionnaire was prepared using a Likert scale with a scale of 1-4 from very negative to very positive and strongly disagree to strongly agree. The method used is Confirmatory Factor Analysis (CFA) executed using Analysis Moment of Structures. There are several fit indexes and cut-offs to test whether a model can be accepted or rejected. The Cut-off for Goodness of Fit (GoF) can be seen in Table 1.
Table 1. Cut-off Value GoF
GoF Index Cut-off Value
Goodness of Fit Index (GFI) ≥0,90
Adjusted Goodness of Fit Index (AGFI) ≥0,90
Comparative Fit Index (CFI) ≥0,90
The Root Mean Square Error of Approximation
(RMSEA) ≤0,80
Table 2. Summary of responses of Linkage to Private Vehicles (Mandatory Activities) Indicators Factors of Linkage to Private
Vehicles (LPV)
Responses (Percentage of Responses) Strongly
Disagree Disagree Agree Strongly agree LPV1 Private vehicles are indispensable in
daily activities 4.0% 4.5% 41.5% 50.0%
LPV2 Likes to drive private vehicles 3.5% 4.5% 51.5% 40.5%
LPV3 Prefer traveling personal space 3.0% 12.5% 59.0% 25.5%
Table 3. Summary of responses of Private Vehicle Problems for Mandatory Activities Indicators of Factor Private Vehicle Problems
(PVP)
Responses (Percentage of Responses) Strongly
Disagree Disagree Agree Strongly agree PVP1 The number of accidents is increasing 1.5% 7.5% 47.0% 44.0%
PVP2 Congestion is increasing 3.0% 8.5% 59.0% 29.5%
PVP3 Emissions and noise are increasing 2.5% 4.5% 48.0% 45.0%
PVP4 Driving a private vehicle is at risk of
being more unsafe 7.0% 31.0% 44.5% 17.5%
Table 4. Summary of responses of Quality Trans Koetaradja for Mandatory Activities Indicators Factor of Quality Trans Koetaradja
(QTK)
Responses (Percentage of Responses) Strongly
Disagree Disagree Agree Strongly agree QTK1 Satisfactory bus stop facilities 6.5% 39.0% 47.0% 7.5%
QTK2 Cleanliness and Comfort 0.5% 14.5% 65.5% 19.5%
QTK3 The officers serve friendly and provide
a sense of security 2.0% 11.0% 68.0% 19.0%
Table 5. Summary of responses of Satisfaction with Trans Koetaradja for Mandatory Activities
Indicators Factors of Satisfaction with Trans Koetaradja (STK)
Responses (Percentage of Responses) Strongly
Disagree Disagree Agree Strongly agree STK1 The bus is needed in daily activities 5.0% 27.0% 48.5% 19.5%
STK2 The distance between work/home is not
far from the bus stop 7.5% 44.5% 41.5% 6.5%
STK3 The waiting time at the bus stop doesn't
take long time 8.5% 51.0% 34.0% 6.5%
STK4 The bus conditions that are not
crowded/full during peak hours 11.0% 48.0% 30.5% 10.5%
Table 6. Summary of responses of Linkage to Private Vehicles (Non-Mandatory Activities) Indicators Factors of Linkage to Private
Vehicles (LPV)
Responses (Percentage of Responses) Strongly
Disagree Disagree Agree Strongly agree LPV1 Private vehicles are indispensable in
daily activities 4,0% 13,0% 41,5% 41,5%
LPV2 Likes to drive private vehicles 3,0% 12,5% 46,5% 38,0%
LPV3 Prefer traveling personal space 4,5% 14,5% 53,5% 27,5%
Table 7. Summary of responses of Private Vehicle Problems (Non-Mandatory Activities) Indicators of Factor Private Vehicle Problems
(PVP)
Responses (Percentage of Responses) Strongly
Disagree Disagree Agree Strongly agree PVP1 The number of accidents is increasing 0,5% 16,5% 39,5% 43,5%
PVP2 Congestion is increasing 0,5% 13,5% 55,0% 31,0%
PVP3 Emissions and noise are increasing 2,5% 12,0% 49,0% 36,5%
PVP4 Driving a private vehicle is at risk of
being more unsafe 8,5% 31,5% 47,0% 13,0%
Table 8. Summary of responses of Quality Trans Koetaradja for Non-Mandatory Activities
Indicators Factor of Quality Trans Koetaradja (QTK)
Responses (Percentage of Responses) Strongly
Disagree Disagree Agree Strongly agree QTK1 Satisfactory bus stop facilities 9,0% 28,5% 48,0% 14,5%
QTK2 Cleanliness and Comfort on the bus 1,5% 16,0% 61,5% 21,0%
QTK3 The officers serve friendly and provide
a sense of security 4,0% 10,5% 66,5% 19,0%
Table 9. Summary of responses of Satisfaction with Trans Koetaradja for Non-Mandatory Activities
RESULTS AND DISCUSSION
In this study, the analysis using the Confirmatory Factor Analysis (CFA) model is used to investigate and determine the relationship between indicators and their latent variables, as well as the relationship between latent variables. Figure 2 shows that the LPV latent variable, the LPV1 indicator as to the standard variable then the LPV2 indicator has a coefficient value of 0,996 which is automatically fulfilled to 1,00 and becomes an indicator that affects the LPV latent. The indicator that affects the PVP latent variable is the PVP3 indicator which has the largest coefficient value of 1,08. The QTK3 indicator is an indicator that affects the latent
Indicators Factors of Satisfaction with Trans Koetaradja (STK)
Responses (Percentage of Responses) Strongly
Disagree Disagree Agree Strongly agree STK1 The bus is needed in daily activities 4,5% 18,5% 54,0% 23,0%
STK2 The distance between work/home is not
far from the bus stop 10,0% 32,5% 49,0% 8,5%
STK3 The waiting time at the bus stop doesn't
take long time 12,0% 47,5% 36,0% 4,5%
STK4 The bus conditions that are not
crowded/full during peak hours 12,0% 44,0% 32,5% 11,5%
variable QTK with the largest coefficient value of 1,09. In the STK variable, the indicator that affects is the STK4 indicator with the largest coefficient value, namely 1,05. The fit model test results showed that the Goodness of Fit values are as follows: GFI is 0,932 > 0,900 (good fit), AGFI is 0,903 > 0,900 (good fit), CFI is 0,978 > 0,900 (good fit) and RMSEA is 0,044 < 0,080 (good fit). Figure 3 shows that the LPV latent variable, the indicator that affects is the LPV2 indicator with the largest coefficient value of 1,05. The PVP3 indicator is an indicator that affects the latent PVP variable with the largest coefficient value, which is 1,002. The indicator that affects the latent variable QTK is the QTK3 indicator with a coefficient value of 1,06. In the STK variable, the indicator that affects is the STK4 indicator with the largest coefficient value, namely 1,05. The fit model test results showed that the Goodness of Fit values are as follows: GFI is 0,939 > 0,900 (good fit), AGFI is 0,914> 0,900 (good fit), CFI is 0,985 > 0,900 (good fit) and RMSEA is 0,036 < 0,080 (good fit).
Table 10 shows the based on the results obtained for mandatory activities, the highest t-value is in the latent variable Linkage to Private Vehicle (LPV) indicator “Likes to drive private vehicles (LPV2)” with the significant value of 14,717 > 1,96. Then in the latent variable Private Vehicle Problems (PVC) indicator “Emissions and noise are increasing (PVP3)” with the significant value of 11,488 > 1,96. Then, the latent variable Linkage to Private Vehicle (LPV) indicator “Prefer traveling personal space (LPV3)” with the significant value of 10,514 > 1,96.
Figure 2. Path Diagram (Mandatory Activities)
Figure 3. Path Diagram (Non-Mandatory Activities)