Decision-making on Public Transportation Services Based on the Socio-economic, Psychological, and Environmental Concern Factors
Nattanin Ueasin1, *
Identifiers and Pagination:Year: 2020
First Page: 22
Last Page: 31
Publisher Id: TOTJ-14-22
Article History:Received Date: 01/10/2019
Revision Received Date: 25/02/2020
Acceptance Date: 27/02/2020
Electronic publication date: 21/04/2020
Collection year: 2020
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This research aims to study the socio-economic, psychological, and environmental factors which affect the decision making of passengers with regard to the use of public transportation route from Nong Khai province, which is considered as the border area of Thailand and Laos, to many destinations.
The study was initially carried out by collecting data from 450 passengers using two public transportation services and 200 passengers using private cars. An analysis was conducted by means of descriptive statistics, factor analysis and binary logistic regression.
The results showed that the status, age, domicile, self-satisfaction and accomplishment, safety of life and property, and self-consciousness and environmental conservation practice influenced the choice of both the alternatives.
To develop a service model, a variety of bus and van services must be provided to match the varied demand of passengers with different levels of purchasing power. It is also important to consider the impact of various factors that affect the public bus service selection, which may result in improved public transport systems. As a consequence, the well-being of border area citizens can be improved.
Nowadays, travelers have more choices of transportation than before as a result of enhanced technology and innovation. For these reasons, the number of people traveling has increased significantly and supports the economic development of rural areas like Nong Khai province, Thailand. Nong Khai is considered as an important northeastern border province of Thailand with territory adjacent to the neighboring country, Laos. Therefore, this place is a gateway to the Association of Southeast Asian Nations (ASEAN) as it is a route following. Asian Highway No. 12 (AH12) to Vientiane, the capital of Laos. In addition, there is a road from Nong Khai along the Mekong River running parallel to the Asian Highway No. 11 (AH11) in Laos . These roads can be used to travel to several countries in ASEAN and southern China with ease. Fig. (1) shows how AH11, AH 12 and AH15 are connected and how Nong Khai city (blue star) is an important city because if travelers want to enter into Thailand, they need to pass through Nong Khai. In this border area, the number of tourists has significantly increased from 2.07 to 5.04 million people between 2009 and 2015 . Moreover, the border trade between Thailand and Laos showed the second-highest trading value for Thailand, which was 16,674.71 million baht .
To harmonize with the geographic, economic, and tourism issues and to link AH routes 11, 12, 15 with two branch roads in the two countries, international bus and domestic bus services are provided to meet the increased demand for services. The major public transportation services are categorized as air-conditioned buses and vans. Based on survey data, Table 1 presents the information about bus and van service companies in Nong Khai and many important provinces in Thailand such as Bangkok, UdonThani, Chiang Mai, Phuket, Rayong etc. It is noted that the main destination of domestic bus services is Bangkok, the capital city of Thailand, which is a 620-kilometer journey while the most popular van service is to Udon Thani province, which is about 50-kilometers. Additionally, Udon Thani province is one of the three largest provinces in the northeastern region of Thailand. It has an international airport and is the starting point of the AH15 route to Vietnam. Moreover, AH12 and AH15 are in the East-West Corridor development project that aims to build transportation networks in the ASEAN peninsular countries . (Fig. 1).
The bus service data reveal that the number of companies and the number of trips per day slightly decreased between 2017 to 2019 on domestic routes. However, the number from Nong Khai province to Luang Prabang province, Laos increased. In addition, the most attractive tourist destinations in Laos are Vientiane, the capital city of Laos, and Luang Prabang, which is a United Nations Education, Scientific and Cultural Organization (UNESCO) world heritage town . These places have many tourist attractions such as: Pha That Luang, Morning market, Wat Xieng Thong etc. Therefore, the number of passengers for international buses is continuously increasing because of tourism. On the other hand, the number of van service companies increased between 2017 and 2019 because Nong Khai to Udon Thani is a short distance, has a high-frequency timetable, and fewer stop points than local buses. It is also easy to reach the Udon Thani international airport by paying a small additional fee.
The number of passengers who traveled between Thailand and Laos increased from 4.37 million in 2008 to 5.58 million in 2018. The number of private cars that crossed the Thailand-Laos Friendship Bridge increased continuously (except in 2017) from 209,000 cars in 2008 to 1,109,460 cars in 2018 (Fig. 2). This situation was affected by many factors such as increasing tourism in both countries, increasing trade value, a sustainable infrastructure policy, ease of access and extended opening and closing times at the Thai-Laos Friendship Bridge [6, 7].
|Fig. (1). ASEAN highways no. 11,12 and 15 connectivity.|
|Number of Trips (time/day)||Timetable||Destination (Province)||Number of Trip Frequency Per Day||Timetable||Destination|
|1.Chaiyaphom Chong-Cha Reang||2||9.00, 11.00||Chaiyaphom||2||9.00, 11.00||Chaiyaphom|
|2.Chatkapong tour||1||19.00||Chiang Mai||Out of business|
|3.Transportation company||2||20.00, 20.20||Bangkok||2||20.00, 20.20||Bangkok|
|6||7.30, 9.30, 12.40, 14.30, 15.30, 18.00||Vientiane (Laos)||6||7.30, 9.30, 12.40, 14.30, 15.30, 18.00||Vientiane (Laos)|
|Services not yet enabled||3||7.30, 12.40, 15.30||Louang Prabang (Laos)|
|4.Sawatdee E-san||2||19.30, 20.00||Bangkok||1||19.30||Bangkok|
|6.Chad Chai tour||1||19.45||Bangkok||1||19.45||Bangkok|
|7.Or. Suesa tour||1||19.00||Bangkok||1||19.00||Bangkok|
|11.Sri Mong Kon||1||18.30||Kanchanaburi||1||18.30||Kanchanaburi|
|12.Rung Pra Sert tour||1||20.25||Bangkok||Out of business|
|13.Sa-ng Chai tour||3||7.30,9.00,11.00||Ubonratchatani||3||7.30,9.00,11.00||Ubonratchatani|
|1||8.30||Sri Sa ket||1||8.30||Sri Sa ket|
|14.Green bus||1||9.15||Chaing Rai (Mea Sai)||Out of business|
|15.Nakorn Chai air||4||10.00,19.45,20.00,20.30||Bangkok||3||10.00,19.45,20.30||Bangkok|
|1.Pet Tawee air||13||6.00-18.00||Udon Thani (Bus station)||13||6.00-18.00||Udon Thani (Bus station)|
|2.Kittipon Pa Roue||13||6.00-18.00||Udon Thani (Central)||13||6.00-18.00||Udon Thani (Central)|
|3.Chaiyaphom Chong Cha Reang||Services not yet enabled||13||6.00-18.00||Udon Thani (Central)|
After the Air Quality Index (AQI), PM2.5 became a matter of concern in Thailand from 2016. Nong Khai province also collected data on PM2.5. The data for Nong Khai (Thabo district point) revealed that AQI had a seasonal pattern and the whole-year average for 2018 was 37.2 μg/m3. This level is unhealthy for sensitive groups . One of the main pollution sources is the increasing use of cars in Nong Khai province. Moreover, because the international trade value of Thailand and Laos increased year-on-year, the number of trucks increased dramatically. Every day, long lines of trucks waited to cross the Thai-Laos Friendship Bridge, which wasted time and increased air pollution in this area. If passengers can be persuaded to use public transport services, it should help to reduce air pollution in this area. Therefore, the research question of this study is ‘what are the important factors that affect the decision making of passengers who travel by public transportation? With this information, the important factors can be identified, and ways to encourage passengers to use public transportation can be suggested. Therefore, this study aims to study the factors that affect decision-making on public transportation service usage in the border areas in Nong Khai. By conducting socio-economic, psychological and environmental analysis, guidelines can be suggested to improve the quality of public van and bus services in the Thailand-Laos border area.
|Fig. (2). Number of private cars that crossed the Thailand / Laos border in Nong Khai province .|
2. LITERATURE REVIEW
For studying modes of transportation, many studies have focused on stated and revealed preference. In general, the Stated Preference (SP) method is based on microeconomic theory, where respondents are required to state their preference in a hypothetical situation. A utility function is then estimated from the SP survey data taken from the respondents, and the behavior is predicted from the estimated utility function . Therefore, the SP survey is conducted to estimate the subsequent travel probability choice and the stated choice is a theoretical choice behavior-typically composed in a survey situation under one or more detailed theory. Most studies focused on new policies, new technologies or improvement of services such as [10-14] etc. However, SP still has weaknesses as the estimation is based on theoretical concepts.
On the other hand, the Revealed Preference (RP) survey assesses passenger’s behavior when choosing the mode of transport at a particular time but does not include rare or nonexistent options like SP . Most studies focused on deeply understanding the choice behavior of passengers such as [15-17] etc. However, RP data cannot be analyzed in many research contexts.
Because SP and RP studies have both weaknesses and strengths, many studies employ both SP and RP by assuming that there is a trade-off between the significant attributes such as price, quality etc. [18-20]. RP data concerns recent choices of the mode of transportation and SP data concerns the choice between new services and old services. New services in these studies represent new topics that have been considered but have not been introduced in the places of study, such as carpooling systems, new business models, better services etc.
Based on previous studies related to the factors affecting public transport service preference, most investigations were conducted in capital cities or major cities and were both quantitative and qualitative [17, 21-24]. However, there have been few studies conducted in rural areas, especially in border areas like Nong Khai province.
Numerous studies principally used personal and socio economic variables to perform decision analysis [16, 25] for example, using personal and socio-economic data such as gender, age, education level, occupation, income, car in procession etc. Moreover, several studies employed a latent variable model [16, 19, 26] by employing three, five, or six-point rating scale questions in the questionnaire. However, every study employed different theoretical concepts to formulate questions based on various theories.
In addition, some studies included social and psychological factors in the analysis [19, 23, 27]. They used these factors to explain individual attitudes and behaviors to assess the influence of values on individual travel behaviors. Based on a study , the values in this study include security, hedonism, and power and  also social status and prestige, control or dominance over people and resources, pleasure, sensuous gratification for oneself, safety, harmony, and the stability of society.
It seems that a combination of SP and RP is a good choice in this study. However, this study does not include new transportation choices at present or in the near future and we collected detailed data of passengers at low cost. Moreover, the suggestions of each passenger who uses both public transportation and private cars were obtained to determine ways to encourage the use of public transportation in the future, whichrevealed that the preference survey is an appropriate method to adopt, using a simple technique like binary logistic regression analysis, which is presented in the methodology section.
3.1. Sample Size
The sample used in this study included 450 Thai and foreign passengers who use public transportation in Nong Khai province as an origin or terminal station, and 200 Thai and foreign passengers who use private cars in Nong Khai province as an origin, destination or transit. Only two types of public vehicles, buses and vane were included in this study. Questionnaires were initially examined by three experts and 30 passengers for each type of vehicle before conducting data collection.
3.2. Query Structure
In this study, data were collected by means of questionnaires in Thai, Lao, and English which were divided into three parts:
1. Socio-economics data about passengers:
- Gender (Male, Female).
- Age (passenger’s age)
- Level of education (primary school, secondary school, vocational certificate, diploma, bachelor or higher).
- Status (single, married, widowed or divorced).
- Occupation (student, government officer, private company, business owner, farmer, unemployed or retiree, other).
- Net income (passenger’s net income).
- Domicile (in the area of Nong Khai, out of Nong Khai).
- Nationality (Thai, Lao, other).
- Car or motorcycle ownership (yes/no).
- Possession of driving license (yes/no).
This information represents variables in the model’s specification except for car and driving license ownership. A nominal scale was employed for these variables, such as the male is zero, and the female is one. In contrast, ratio scale data was provided by the passengers, such as the age and income of the passenger .
2. Information on social, psychological and environmental concerns was obtained by noting passengers’ opinions on the importance of public transport by means of a six-point Likert-scale questionnaire (strongly agree, agree, slightly agree, slightly disagree, disagree, strongly disagree) which is divided into 11 sub-questions related to the basic human values  and we also studied some environmental concerns  by asking the opinions of passengers on the importance of the following issues:
- The stability of the passenger.
- The security of the passenger.
- The happiness of the passenger’s life and friendships.
- The harmony of the passenger’s family.
- The satisfaction of the passenger’s life.
- The social reputation of the passenger.
- The social prestige of the passenger.
- The social status of the passenger.
- The passenger’s behavior in regard to reducing and reusing resources and goods.
- The passenger’s love of nature.
- The belief that fossil fuels are a cause of global warming.
For these 11 questions, a latent variable was used to interpret this information by using the factor analysis technique to reduce the number of significant factors in the model, as mentioned in the discussion of the model specifications. The measurement scale for the attitudes of passengers was a Likert scale where strongly agree, agree, and slightly agree have 6, 5, and 4 points, respectively, and strongly disagree, disagree, and slightly disagree have 1, 2, and 3 points, respectively.
3. Passengers were asked an open-ended question “What would make you use public transportation in the near future” and what would encourage them to use public transportation in the future. This question aimed to elicit information about what could be improved and how to encourage or push passengers who use private cars to use public transportation in the near future, and for passengers who have used public transportation to use this kind of service again.
3.3. Utility, Demand for Transportation, and Revealed Preference in Microeconomic Theory
To illustrate the microeconomic theory (Fig. 3) that is related to this study, budget constraint is included, which is at the core of economics. This focuses on how to manage limited resources, which in this case means the budget of consumers. Consumers choose the best service they can afford ; this consumer preference concept aims to study how consumers make such a selection. “Utility” is an economics concept that measures the benefits that a service user obtains based on the assumption that the consumer will maximize their utility by making a choice that attempts to maximize utility under a budget constraint, thereby combining the two theories. Moreover, the optimal choice of consumers depends on many factors such as the cost of transportation, consumer income, quality or service . The “demand for transportation” will be considered as derived demand in this study (for example, we want to go to shopping, so we need a car to drive to the department store).
However, it is often difficult to determine consumer preferences because, in real life, preferences are not directly observable. The demand of consumers is the key to the study because it reveals information about their preferences. We can determine consumer preferences by observing their real behavior or we can call it “revealed preference”. We can also study their decision-making process because after consumers rationally consider each choice of transportation, they make a decision under the best affordable concept, as previously mentioned.
3.4. Transportation Utility Function
From utility theory, we can express the utility function as follows:
Where is the utility of transportation mode i gained by passenger n. is the systematic component of the utility of transportation mode i gained by passenger n, and is the disturbance of utility of transportation mode i gained by passenger n [31, 32].
The utility function for public transportation and private cars can be constructed in several forms depending on the variables as mentioned in the query structure section, which are combined in the utility function. Therefore, any variable that plays a role in the selection of the travel pattern was used to explain the behavior of the passengers. The utility functions for travel by alternative passenger transportation are as follows:
Where and are linear parametric function, taking into account computable appearances of the particular mode of passenger n. β is the coefficient of variables in each transportation mode (If we denote as a vector of k unknown parameters). X is an independent variable for general information, socio-economics and latent variables of the particular mode that was applied .
Due to a large number of variables in this analysis, the reduction in specific variables by using the factor analysis method for the social and psychological variables was carried out to investigate the latent variables and to analyze the factors affecting preferences for public transport services. General passenger information and the latent variables for the social and psychological aspects were analyzed to determine the particular variables affecting decision-making on public transport services by means of binary logistic regression analysis.
3.5. Model Specification
The principle of the logit model assumes that the random component of utilities or is independently and identically distributed (IID). Therefore, the model estimates the proportion of passengers n choosing transportation mode i as follows:
When is the probability or proportion of passenger n will choose transportation mode i. is the utility function of the transportation mode i of passenger n, and i is any type of vehicle which is categorized as a member of all vehicles, including public transportation and private cars (Applied from ).
For the transportation mode choice model, a binary logistic model is employed, which provides alternatives to vehicles for public transport services (public transportation is the focused group by giving value 1) or private cars (using private cars as a reference group by giving value 0).
|Fig. (3). The path of microeconomics theory applied from .|
4.1. Validity and Reliability
The results of the content validity analysis revealed that the IOC value was between 0.5 and 0.9, indicating that the questionnaire was accurate and comprehensive with regard to all aspects and objectives of the study.
The questionnaire’s reliability was tested by means of the Cronbach Alpha coefficient. The result was 0.825, which indicates that this survey possesses a high level of reliability.
4.2. Personal Information of Respondents
The study was conducted by collecting general information from passengers who traveled from/to Nong Khai province as the origin, destination or in transit, as shown in Table 2
According to Table 2, more females used public transportation services than males, with an average age of 23.6-year-old, who were younger than the passengers traveled by private cars. Passengers who were single had a higher percentage of traveling by public transportation with more number of students. The passengers with a low income had more percentage to travel by public transportation and passengers who were living in Nong Khai province also had more percentage to use public transportation as well. Most of all the respondents owned motorcycles, while only 39.8 percent of the respondents used public transportation owned cars.
4.3. Results of Factor Analysis
Due to the number of variables used in the analyses, in particular, the social, psychological and environmental variables (11 variables), the analysis of element factors is crucial to remove the non-significant variables. The latent social, psychological and environmental variables were determined to analyze the factors that affected the selection of the mode of public transport service. The results are summarized in Table 3 as follows.
|–||–||Public Transportation||Private Car|
|General Information of Passengers (Variable)||Details||Number of Passengers||Percentage||Number of Passengers||Percentage|
|Destination (DT)||Udon Thani||356||79.1||79||39.5|
|Other provinces or Laos||94||20.9||121||60.5|
|Age||under 20 years old||97||21.6||0||0|
|21-30 years old||174||38.7||48||24|
|31-40 years old||86||19.1||112||56|
|over 41 years old||93||20.7||40||20|
|Educational Level (EL)||Primary School||58||12.9||5||2.5|
|Higher Secondary School or Vocational Certificate||168||37.3||65||32.5|
|Diploma or High vocational certificate||36||8.0||35||17.5|
|Bachelor degree or higher||128||28.4||73||36.5|
|Windowed or Divorced||19||4.2||5||2.5|
|Government officer or State enterprise officer||45||10.0||36||18|
|Private company officer||115||25.6||39||19.5|
|Unemployed or Retiree||16||3.6||3||1.5|
|Net Monthly income (NI)||Less than 4,000 baht||156||34.7||37||18.5|
|More than 10,001 baht||98||21.8||56||28|
|Domicile (DO)||In the area of Nong Khai||266||59.1||49||24.5|
|Out of Nong Khai||184||40.9||151||75.5|
|Car Ownership (CO)||Yes||179||39.8||200||100|
|Automobile driving license in possession (AD)||N/A||20||4.4||0||0|
|Motorcycle ownership (MO)||Yes||353||78.4||125||62.5|
|Motorcycle driving license in possession (MD)||N/A||20||4.4||0||0|
|Life with stability||.792|
|Life with security||.811|
|Couple life and friendship with happiness||.813|
|Family with harmony||.738|
|Proper social status||.801|
|Reduce and reuse resources||.817|
|Being a nature lover||.766|
|Fuel is a cause of global warming||.734|
|Percentage of variance||46.875||11.007||9.047|
According to Table 3, it was found that the Kaiser-Meyer-Olkin (KMO) test for sampling adequacy was equal to 0.891. Consequently, this high value was considered suitable for factor analysis. The Chi-square was 2851.207 (test of sphericity) at the significance level of 0.000, implying that some variables areinterlinked.
Composition no.1 consisted of five variables: life with stability, life with security, couple life and friendship with happiness, and family with harmony and life satisfaction. The Eigenvalue was equal to 5.941, with loading factors between .618 and .813. This composition was called Safety in life and property (SLP).
Composition no. 2 consisted of three variables: social reputation, social prestige and proper social status. The Eigenvalue was equal to 1.501, with loading factors between .796 and .811. This composition was called Self-satisfaction and accomplishment (SSA).
Composition no.3 consisted of three variables: reduce and reuse resources, being a nature lover, and the belief that gasoline is a cause of global warming. The Eigenvalue was equal to 1.232, with loading factors between.734 and. 817. This composition was called Self-consciousness and environmental conservation practice (SEC).
4.4. Results of the Analysis of Factors Affecting Decision-making on Public Transport Service Usage
After obtaining data on the latent variables from the analysis of social, psychological and environmental factors, which consisted of three variables (SLP, SSA, SEC), the factors were analyzed that affected decision-making preferences by applying 10 passengers’ general information variables (DT, SE, AG, EL, ST, OC, NI, DO, MO, MD) to conduct the analysis. To perform investigation, the Forward Selection (Wald) method - Binary Logistic Regression was adopted, given that passengers using private cars and public transportation were 0 and 1, respectively. The results of the study are shown in Table 4.
|ST (2)||Windowed or Divorced||1.347||1.091||1.523||1||0.217||3.844|
|AG||Age of traveler||-0.207||0.090||5.293||1||0.021**||0.813|
|DO (1)||Domicile of traveler is out of Nong Khai||1.171||0.317||13.639||1||0.000***||3.226|
|SSA||Self-satisfaction and accomplishment||-0.469||0.166||7.948||1||0.005**||0.625|
|SLP||Safety in life and property||-0.539||0.138||15.385||1||0.000***||0.583|
|SEC||Self-consciousness and environmental conservation practice||0.283||0.112||7.438||1||0.006***||1.514|
|Predicted||Predictive ability of model||73.2|
|-2Log likelihood||Concordance of data and model||424.132|
|Cox&Snell R square||Ability to explain dependent variable from independent variable||0.291|
|Nagelkerke R square||0.389|
|Hosmer&Lemeshow test significance||Goodness of fit test model||0.915|
According to Table 4, it can be concluded that single passengers tended to use public transportation more than married people by 8.199 times, with statistical significance.
As the passenger’s age increased, there is a tendency to decrease public transportation use at 18.7 percent (1-0.813)*100) with statistical significance.
Passengers who live outside Nong Khai province were more likely to use public transportation than passengers who were domiciled in Nong Khai province by 3.226 times, with statistical significance.
For passengers who took self-satisfaction and accomplishment into account, it could be inferred that in every elevated level of priority for this variable, the likelihood of public transportation service usage decreased by 37.5 percent (1-0.625)*100).
Passengers who took safety of life and property into account, it could be inferred that in every elevated level of priority for this variable, the likelihood of public transportation service usage decreased by 41.7 percent.
Passengers who took self-consciousness and environmental conservation practice into account, it could be inferred that in every elevated level of priority for this variable, the likelihood of public transportation service usage increased by 1.514 times.
With regard to the overall forecasting model results, the actual results when compared to the results obtained from the forecasting process showed accuracy of 73.2 percent, which can be considered suitable to perform prediction at a high accuracy level.
The results of Cox & Snell and Nagelkerke R2 were 0.291 and 0.389, respectively, indicating the ability to be independent of the dependent variables, which included status, age of traveler, the domicile of traveler, SSA, SLP and SEC. The percentage of the factors affecting decision-making on public transport services was 29.1 and 38.9, respectively.
The results of the Hosmer and Lemeshow test revealed that the statistical significance was 0.915, which is greater than 0.05, indicating that the logistic regression model was functionally appropriate.
The results revealed that most respondents who would use public transportation service were unmarried, 21-30 years old, women, with high school education level / vocational certificate and were students with less than 4,000 baht per month income and had been living in Nong Khai. In addition, 83.1%, 12.2%, and 4.7% were Thai, Lao and other passengers who used Thai public transport, respectively.
The status and age of passengers were found to be related to decision making on public transportation especially for bus services (Fig. 4). Discounted tickets should be offered for passengers who travel as a family. Moreover, baby and elderly facilities should be provided, such as priority seats, magnifying glasses, facilities inside the toilet to handle babies, toys, and children’s books . to encourage more passengers to travel with their family by bus. These suggestions are related to the cost of installation facilities in vehicles that businesses may take into consideration. However, based on the answers of passengers in the open-ended question in part three, businesses can also make extra money and attract new groups of passengers by offering baby wipes, disposable diapers for babies and the elderly, on the bus as well.
|Fig (4). Show interior and exterior of public bus and van service in Nong Khai province bus station.|
Safety of life and property was also found to be a concern of passengers. Based on data from the Thailand Road Accidents Data Center for Road Safety Culture (Thai RSC) , the number of accidents from 2010 to 2019 in Nong Khai province was 12,886 (1,289 person per year) which is a low rate compared to the average for the whole country (3,646 person per province per year). Therefore, the safety of overland transportation should be a major concern in the government policy to enhance the safety of public and private transportation as:
- Encourage businesses to have not only compulsory motor insurance but also voluntary motor insurance for public transportation services with optional additional fees for passengers.
- Encourage drivers to enhance the safety of passengers by driving the bus or van more carefully. Drivers can also make announcements about safety issues.
- Encourage the use of VIP public transportation vehicles with improved active and passive safety systems in vehicles for optional extra fees.
Based on the answers of passengers in part three of the questionnaire, active safety systems such as Tire Pressure Monitoring Systems (TPMS), Driver Monitoring Systems (DMS), Blind-Spot Detection (BSD) and passive safety systems such as whiplash protection systems, airbags etc. can be improved. These improvements should be focused on van services because vans are smaller than buses (Fig. 4) and the chance of serious accidents is greater. This type of van should be promoted with extra charges for enhanced safety.
The self-consciousness and environmental conservation factors depend on the feelings of individual passengers. However, environmental problems are a global issue so public awareness of such issues should be raised. In summary, the development of public transportation must take into account various factors that affect the passenger’s demand. Bus and van services must offer services that reflect the various types of passengers’ demands and reflect the different levels of affordability .
CONSENT FOR PUBLICATION
AVAILABILITY OF DATA AND MATERIALS
CONFLICT OF INTEREST
The author declares no conflict of interest, financial or otherwise.
This study was supported by Research and Technology Transfer Affairs at Khon Kaen University. Moreover, I would also like to extend my special thanks to the Indo-China Country International Trade and Economic Research Sector for their encouragement and support for this study.
|||Asian highway network in member countries. Accessed on 9 September 2019. Available online: https://www.unescap.org/ our-work/transport/asian-highway/database.|
|||Foreigner visitor arrivals to Nong Khai province Thailand from 2009 to 2015. Accessed on 19 October 2018. Available online: http://service.nso.go.th/ nso/web/statseries/statseries23.html|
|||The summary of Thailand and neighbor countries trade situation (Jan-Jun 2018). Accessed on 15 August 2018. Available online: http://www.dft.go.th/Portals/3/.|
|||Nonthapot S, Dinh TN, Thuan YT. The process of land passenger transportation by international bus crossing Lao and Vietnamese border gates. Open Transplant J 2019; 13: 1-10.
|||Town of Luang Prabang. Accessed on 11 January 2020. Available online: https://whc.unesco.org/en/list/479.|
|||ASEAN Connectivity. Accessed on 12 January 2020. Available online: https://asean.org/asean/asean-connectivity-2/.|
|||Number of private car that across Thai-Laos Friendship Bride. Accessed on 14 January 2020. Available online: http://www.dannongkhai.com/.|
|||Nong Khai air quality index (AQI). Accessed on 14 January 2020 Available online: https://www.airvisual.com/thailand/nong-khai|
|||Fujii S, Garling T. Application of attitude theory for improved predictive accuracy of stated preference methods in travel demand analysis. Transp Res Part A Policy Pract 2003; 37: 398-402.
|||Anwar AHMM, Yang J. Examining the effect of transport policy on model shift from private car to public bus. Procedia Eng 2017; 180: 1413-22.
|||Lee C, Ran B, Yang F, Loh W-Y. A Hybrid tree approach to modelling alternate route choice behavior with online information. J Intell Transp Syst 2010; 14(4): 209-19. [https://doi.org/10.1080/15472450.2010.516229].
|||Hansson J. F. pettersson, H. Svensson, and A. Wretstand, “Preference in regional public transport: A literature review. Eur Trans Res Rev 2019; 11(38) [https://doi.org/10.1186/s12544-019-0374-4].|
|||Jin F, An K, Yao E. Mode choice analysis in urban transport with shared battery electric vehicles: A stated-preference case study in Beijing, China. Transp Res Part A Policy Pract 2020; 133: 95-108.
|||Dell’Olio L, Ibeas A, Dominguez A, Gonzalez F. Passenger preference analysis: Light rail transit or bus versus car. Transport 2012; 27(3): 276-85.
|||Broach J, Dill J, Gliebe J. Where do cyclist ride? A route choice model developed with revealed preference GPS data. Transp Res Part A Policy Pract 2012; 46: 1730-40.
|||Malokin A, Circella G, Mokhtarian PL. How do activity conducted while commuting influence mode choice? Using revealed preference models to inform public transportation advantage and autonomous vehicle scenarios. Transp Res Part A Policy Pract 2019; 124: 82-114.
|||Chauhan V, Suman HK, Bolia NB. Binary logistic model for estimation of mode shift into Delhi Metro. Open Trans J 2016; 10: 124-36.
|||Yan X, Levine J, Zhao X. Integrating ridesourcing service with public transit: An evaluation of traveler responses combining revealed and state preference data. Transp Res, Part C Emerg Technol 2019; 105: 683-96.
|||Tahmasseby S, Kattan L, Barbour B. Propensity to participate in a peer-to-peer social-network-based carpooling system. J Adv Transp 2016; 50: 240-54.
|||Cherchi E, Ortuzar JDD. Mixed RP/SP models incorporating interaction effects. Transportation 2002; 29: 371-95.
|||Wu W, Hong J. Does public transit improvement affect commuting behavior in Beijing China? A spatial multilevel approach. Transp Res Part D Transp Environ 2017; 52: 471-9.
|||Kaewkluengklom R, Satiennam W, Jaensirisak S, Satiennam T. Influence of psychological factors on mode choice behavior: Case study of BRT in Khon Kaen City, Thailand. Transp Res Procedia 2017; 25: 5072-82.
|||Jaensirisak S, Ongkittikul S. Value of time and service quality for bus travel in Bangkok: valuation and policy implications. TDRI Quarterly Review 2015; 30(4): 4-12.|
|||Beirao G, Cabral JAS. Understanding attitudes towards public transportation and private car: A qualitative study. Transp Policy 2007; 14: 478-89.
|||Nguyen-Phuoc DQ, Amoh-Gymah R, Tran AT, Phan CT. Mode choice among university students to school in Danang, Vietnam. Travel Behav Soc 2018; 13: 1-10.
|||Paulssen M, Temme D, Vij A, Walker JL. Value, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice. Transportation 2014; 41: 873-88.
|||Lierop DV, El-Geneidy A. Is having a positive image of public transit associated with travel satisfaction and continued transit usage? An exploratory study of bus transit. Public Transp (Berl) 2018; 10(2): 241-56.
|||Homer PM, Kahle LR. A structural equation test of the value-attitude-behavior hierarchy. J Pers Soc Psychol 1988; 54(4): 638-46.
|||Bouscasse H, Joly I, Bonnel P. How does environmental concern influence mode choice habits? A mediation analysis. Transp Res Part D Transp Environ 2018; 59: 205-22.
|||Varian HR. Intermediate Microeconomics: A Modern Approach 2003.|
|||Ben-Akiva M, Lerman SR. Discrete Choice Analysis: Theory and Application to Travel Demand 1985.|
|||Siridhara S, Paonoi W, Meeyai S, Ratanavaraha V. Container transportation mode choice analysis with a binary logit model case study: Northeastern Thailand. Engineering and Applied Science Research 2019; 46(3): 256-66.|
|||The accidental data of Thailand. Accessed on 14 January 2020 Available online: http://www.thairsc.com/|