• Energy Research

Due to the COVID-19 epidemic, ordering food online has become very popular. This study used a structural equation model to analyze the indicators that influence the decision to order food through a food-delivery platform. The theory of planned behavior and the technology acceptance model were both used, along with a new factor, the task–technology fit (TTF) model, to study platform suitability. Data were collected using a questionnaire given to a group of 1320 consumers. The results showed that attitudes toward on-line delivery most significantly affected the behavioral intentions of the consumers, followed by subjective norms. Among attitudes, perceived ease of use was the most significant, followed by perceived usefulness and trust. The study’s results revealed that TTF had the most significant impact on perceived ease of use, followed by perceived usefulness. This means that, if a food-ordering platform is deemed appropriate, consumers will continue to use it, and business sustainability will be enhanced.

The risk of accidents is a danger in public transport that could lead to threats to property, the environment, and the lives and health of people. In particular, parents are keenly aware of and concerned about the safety of school trips. Thus, this study aims to examine the factors that influence the perceptions of parents about the safety of the school trips of children. The study recruited 750 respondents from Northeast Thailand. Data were obtained from responses to a self-report questionnaire. The model consisted of six factors, namely, transportation satisfaction, infrastructure, information, the safe behavior of drivers, the safety of the transportation systems, and the safety policy. The results of the confirmatory factor analysis indicated that all six factors were in accordance with the empirical data (model fit statistic: χ2 = 758.098, df = 276, χ2/df = 2.747, CFI = 0.962, TLI = 0.955, SRMR = 0.038, RMSEA = 0.048). The results can serve as a reference for developing guidelines and recommending policies for the management and the implementation of safe school trips for students.

Abstract Daily human activities have caused severe impacts on global warming. Such human activities, in particular travel and freight transportation, generate massive emissions of greenhouse gases (GHGs), e.g., carbon dioxide (CO2). Hence, the aim of this study was to predict the amount of CO2 emissions from energy use in Thailand's transportation sector as well as related factors, thus providing a substantial benefit to determine policies for reducing GHG emissions and its impacts. In this study, 5 independent variables, namely the size of the population, gross domestic product (GDP), and the number of small, medium and large-sized registered vehicles, were considered in the forecasting of the CO2 amount released from transportation energy consumption using 4 techniques: log-linear regression, path analysis, time series, and curve estimation. According to the findings, the time series exemplified the minimum mean absolute percent error (MAPE=5.388), followed by the log-linear regression model (MAPE=6.379). The results, based on a path analysis model, indicated the significant effects of the large-sized registered vehicle numbers, GDP, and population on the amount of CO2 emissions. With the CO2 emission forecast, the maximum predicted value was 225.33 million tons by 2030 using curve estimation (cubic), and the minimum predicted value was 91.68 million tons using log-linear regression.

In 2018, 19,931 people were killed in road accidents in Thailand. Thus, reduction in the number of accidents is urgently required. To provide a master plan for reducing the number of accidents, future forecast data are required. Thus, we aimed to identify the appropriate forecasting method. We considered four methods in this study: Time-series analysis, curve estimation, regression analysis, and path analysis. The data used in the analysis included death rate per 100,000 population, gross domestic product (GDP), the number of registered vehicles (motorcycles, cars, and trucks), and energy consumption of the transportation sector. The results show that the best three models, based on the mean absolute percentage error (MAPE), are the multiple linear regression model 3, time-series with exponential smoothing, and path analysis, with MAPE values of 6.4%, 8.1%, and 8.4%, respectively.

Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.

There is a significant need to change people’s travel mode from personal cars to public rail, because rail transport is a more environmentally friendly travel mode. Over the past decade, the number of rail passengers has reduced because of service quality problems. Thus, this study aims to propose guidelines for precise service quality (SQ) improvements of intercity rail services in Thailand. Data were collected from 615 train passengers by distributing questionnaires at train stations in six provinces, covering all regions of Thailand. Cluster analysis (CA), factor analysis (FA), and importance-performance analysis (IPA) were applied in this research, which were used based on gap analysis. As a result of CA and FA, the 45 quality indicators were grouped into four factors, namely, vehicles, staff, services, and infrastructures/stations. The FA results seem more appropriate than those of CA in terms of providing factor loadings that indicate the importance of each indicator. The results of IPA show that the seven indicators that were analyzed fell into the “concentrate here” quadrant. To summarize the current policy, the factor most in need of rapid improvement in order to increase the quality of the intercity rail service in Thailand is that of the train car variables group; on the other hand, the main strength of the current services relates to the services provided by staff.

Industrial waste disposal in a cement kiln is an operation that includes waste disposal as well as the conversion of waste into renewable energy, which is a cement industry in many countries. This research studied business factors related to the intention to use co-processing industrial waste disposal service in cement kilns by surveying the data with questionnaires from 1251 customers nationwide. The objectives of this research were to study the relationship of business factors by using structural equation modeling to analyze factors influencing the selection of industrial waste disposal service in cement kilns. The study results found that customer attitude towards the following factors, including perceived ease of use, perceived usefulness, disposal price, service provider location, promotion, people, and a service provider’s infrastructure, influenced intention to use the service. The variables that customers gave importance to were the industrial waste disposal with zero wastes to landfill and the use of industrial waste relevant to the circular economy by using the industrial waste, which has a quality of renewable fuel in cement kiln as the renewable fuel of the cement furnace. According to the research results, service providers in cement kilns can potentially plan service strategies to achieve sustainability for further business operations in a highly competitive market.

The motorcycle is one of the important modes of transport for Thai people in all provinces due to its convenience and ability to access all areas and cover short distances, which is especially convenient for rural people. However, according to the accident record, it was found that the motorcycle was the vehicle causing the highest amount of accidents, and helmet wearing could save lives and reduce the level of severe injuries. In this regard, the objective of this study was to study and develop a model of factors that affected helmet use behavior using structural equation modeling (SEM) based on the Health Belief Model (HBM). Further, this study compared urban and rural models, so as to suggest suitable guidelines for the promotion of helmet use in the study areas. The sample comprised 801 motorcycle users divided into 401 urban residents and 400 rural residents. From the parameter invariance testing in the two areas, a chi-square difference test found differences in the factor loading, intercepts, and structural paths between urban and rural societies.