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.

The purposes of this research are (1) to create a motorcycle riding behavior measurement model for a Thai population by Motorcycle Rider Behavior Questionnaire (MRBQ) modification with exploratory factor analysis (EFA); (2) to verify the measurement model by second-order confirmatory factor analysis (second-order CFA); and (3) to define the guidelines of the self-assessment report for Thai people in terms of riding motorcycles. Collected data were distributed among four areas: metro-municipalities, municipalities, district municipalities, and non-municipalities from five regions. The sample consisted of 1516 motorcycle riders who were at least 20 years old. Of these riders, 91.4% had motorcycle riding licenses, 84.4% had over five years of experience in motorcycle riding, 75.5% used a motorcycle to go to work/study, and 82.1% used a helmet sometimes. Exploratory factor analysis (EFA) and second-order confirmatory factor analysis (second-order CFA) were used for measurement model creation. The results presented 26 indicators that were confirmed to compose the motorcycle riding behavior of Thai people at a statistical significance level of α = 0.05; these were separated into four factors, namely, traffic error, control error, stunts, and safety equipment. The results of this MRBQ study can inform future study of the motorcycle riding behavior of Thai people.

Road accidents are caused by humans, vehicles, and road environments. Human attitudes affect behavioral changes and can lead to unsafe riding behavior. The sex of an individual is a key factor that affects their riding behavior. We aimed to use structural equation modeling (SEM) by analyzing the multi-group SEM between men and women and applying the theory of planned behavior (TPB) and the locus of control (LC) theory. The data used in the research were collected from all over Thailand, consisting of 1516 motorcycle riders (903 men and 613 women) aged over 20 years. A self-administered questionnaire was designed for data collection of the riding behavior using the Motorcycle Rider Behavior Questionnaire (MRBQ), including traffic errors, control errors, stunt frequency, and safety equipment. We found that riding behaviors between men and women were significantly different in both theories. For men, TPB showed that the main factors that highly influenced motorcycle riding behavior (MRB) were the attitudes based on health motivation (AHM) and perceived behavior control (PC); for women, AHM produced a stronger effect than in men. However, for the subjective norms (SN) factor, we found no direct effect on MRB, but did find an indirect effect through the attitudes based on severity (ASE) in both sexes. Particularly for women, the indirect influence value of the SN factor was higher. For women, the LC showed that internal factors had more influence than external factors. The same was found for men, but the effect in women was significantly stronger. We found that sex significantly affected the MRB. Therefore, policies must be implemented that address each group specifically as their attitudes and behaviors are different.

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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.