Rattanaporn Kasemsri

The expansion of the transport sector in Thailand has resulted in a continuous increase in greenhouse gas emissions and air pollution. Therefore, promoting the adoption of commercial electric vehicles (EVs) has become an important approach to mitigating environmental impacts and enhancing sustainability. This study integrates the TAM, TPB, and 7Ps frameworks to examine factors influencing the intention to adopt EVs among freight transport operators in Thailand. A total of 876 freight operators were surveyed, and the data were analyzed using a random parameters probit model with heterogeneity in means. The results indicate that environmental motivation, perceived safety, ease of use, reductions in operational costs, social benefits, dealership credibility, and perceived quality-of-life improvement positively influence the intention to adopt EVs. In contrast, gaps between EV attitudes and purchasing readiness, along with over-reliance on promotional and online channels, negatively affect EV adoption intention. Furthermore, perceptions of price appropriateness show heterogeneous effects across respondents, reflecting hidden costs and operational uncertainties. Based on these findings, the study proposes an integrated set of policy measures to support a sustainable transition toward EV adoption in the freight transport sector. These results provide useful guidance for policymakers and freight transport operators in developing strategies and policies that encourage the long-term adoption of electric vehicles in freight transportation.

Electric vehicle (EV) adoption remains critically low in Thailand despite government initiatives, with limited understanding of how adoption factors vary across different user segments. This study investigates the determinants of EV adoption intentions across three distinct groups—internal combustion engine (ICE) users, hybrid electric vehicle (HEV/PHEV) users, and current EV users—to develop targeted adoption strategies. Data were collected from 3794 Thai vehicle users through on-site administered questionnaires and analyzed using multi-group structural equation modeling, integrating the Technology Acceptance Model, Theory of Planned Behavior, and environmental psychology constructs. Results reveal significant differences in adoption pathways across groups: ICE users show the strongest sensitivity to perceived ease of use, indicating technology apprehension as the primary barrier; HEV/PHEV users demonstrate transitional characteristics with the highest experience-usefulness relationship, while current EV users exhibit stronger influence from environmental identity and social norms. All 14 hypotheses were supported, though with varying effect magnitudes across groups. Surprisingly, the attitude-intention relationship was consistently weak across all segments, suggesting unmeasured cultural or contextual factors. This study contributes the first empirical evidence of segmented adoption patterns in an emerging market, revealing a progression pathway from technology-focused concerns (ICE) through balanced considerations (HEV/PHEV) to identity-driven adoption (EV). Findings provide actionable insights for policymakers to design segment-specific interventions: technology familiarization for ICE users, transition facilitation for hybrid users, and community-building for EV users.

Hospital transport services represent a vital alternative for addressing inequities in access to medical care, particularly in countries where public transportation systems are inadequate, such as Thailand. This approach enables equitable and widespread access to healthcare services for residents in underserved areas. The objective of this study is to analyze the factors influencing the choice of hospital transport travel mode by comparing various machine learning algorithms. The findings reveal that the categorical boosting model outperformed the other models across all performance metrics. The model results indicate that waiting time, travel time, travel cost, and comfortability significantly influence the decision to use hospital transport services. Furthermore, demographic data analysis highlights critical factors such as age, gender, income, travel frequency, occupation, and time of travel, all of which significantly affect the choice of hospital transport service. To maximize the practical implications of this study, policy recommendations and implementation strategies are proposed to support decision-makers in promoting equitable travel options and eliminating barriers to fair access to healthcare services.

A key innovative strength of this study lies in the application of advanced statistical techniques to simultaneously analyze critical factors derived from the 7P framework, Activity-Based Tourism Models, and behavioral intentions within a single integrated model, leading to several novel findings.

Truck drivers play a crucial role in industrial development but face disproportionately high risks of traffic-related injuries and fatalities. These risks arise from complex traffic conditions, especially in truck-congested industrial zones, and economic pressures that encourage risky driving behaviors. This study investigates key factors influencing these behaviors among truck drivers in industrial zones using an integrated framework combining the Health Belief Model and Protection Motivation Theory, a novel approach in this context. A random parameter model was employed to account for unobserved heterogeneity in drivers’ responses. The results highlight several significant psychological factors: perceived susceptibility (when drivers perceive the risk of crashes while driving), perceived severity (when drivers feel that crashes will impact their work), perceived barriers (when truck drivers perceive that fastening seat belts causes discomfort and when they perceive safety equipment for vehicles as expensive and unaffordable), cues to action (when truck drivers encounter safe driving campaigns), and health motivation (when truck drivers prioritize adequate rest and relaxation). Additionally, the study identifies route familiarity as a random effect, revealing variations in how this factor influences behavior across individuals. The study provides practical, evidence-based policy recommendations aimed at reducing road injuries and fatalities among truck drivers, offering valuable insights for policymakers, transport authorities, and logistics stakeholders.

Amid globalization, tourism has increasingly emphasized health and well-being through sustainable, wellness-oriented services. Thailand has been recognized as having strong potential to become a regional hub for wellness tourism, supported by its efficient healthcare system and diverse attractions. This study aims to identify key indicators of wellness tourism based on holistic health principles and to examine their relationships with tourists’ intentions to use wellness services. Data were collected from 1200 wellness tourists in Thailand through stratified random sampling and analyzed using Exploratory Factor Analysis (EFA) and Structural Equation Modeling (SEM). The results revealed six significant wellness factors, with Environmental Wellness being the most influential. In addition, gender, income, and occupation were found to positively affect wellness tourism behavior. Attitude and subjective norms also significantly influenced tourists’ intentions to engage in wellness services. This study provides policy recommendations to assist tourism and public health agencies in promoting wellness tourism and enhancing health-focused travel experiences.

The Belt and Road Initiative’s infrastructure development faces significant challenges in understanding and addressing the divergent perceptions between urban and rural populations, particularly regarding high-speed rail projects. This study investigates the behavioral intentions and sustainability perspectives regarding the China–Laos High-Speed Rail Service among Thai people, with particular focus on urban–rural differences. While the China–Laos railway became operational in December 2021, it is important to note that the high-speed rail extension into Thailand is not yet in operation and remains in the planning and development stage. Using survey data from 2866 respondents (1301 urban and 1565 rural) across 22 Thai provinces, this study employs structural equation modeling to examine relationships between perceived benefits, service quality, cultural factors, emotional aspects, and behavioral intentions. The findings reveal significant urban–rural disparities in infrastructure acceptance patterns. Urban residents demonstrate stronger relationships between perceived benefits and attitudes (β = 0.260) compared to rural residents (β = 0.170), while rural populations show substantially stronger responses to cultural factors (β = 0.365 vs. β = 0.309). Service quality more strongly influences behavioral intentions in rural areas (β = 0.154 vs. β = 0.138), suggesting varying priorities across geographical contexts. The study recommends implementing differentiated development strategies that address these urban–rural differences, including culturally sensitive rural engagement approaches and comprehensive service quality management systems. This research contributes to infrastructure development literature by empirically validating spatial heterogeneity in acceptance factors, extending theoretical frameworks on sustainability perceptions, and providing evidence-based guidance for managing urban–rural disparities in major infrastructure projects.

This study investigates the impact of high-speed rail (HSR) on Thailand’s public transportation market and evaluates the effectiveness of machine learning techniques in predicting travel mode choices. A stated preference survey was conducted with 3200 respondents across 16 provinces, simulating travel scenarios involving buses, trains, airplanes, and HSR. The dataset, consisting of 38,400 observations, was analyzed using the CatBoost model and the multinomial logit (MNL) model. CatBoost demonstrated superior predictive performance, achieving an accuracy of 0.853 and an AUC of 0.948, compared to MNL’s accuracy of 0.749 and AUC of 0.879. Shapley additive explanations (SHAP) analysis identified key factors influencing travel behavior, including cost, service frequency, waiting time, travel time, and station access time. The results predict that HSR will capture 88.91% of the intercity travel market, significantly reducing market shares for buses (4.76%), trains (5.11%), and airplanes (1.22%). The findings highlight the transformative role of HSR in reshaping travel patterns and offer policy insights for optimizing pricing, service frequency, and accessibility. Machine learning enhances predictive accuracy and enables a deeper understanding of mode choice behavior, providing a robust analytical framework for transportation planning.

Despite the considerable efforts to address traffic crashes, overspeeding in industrial zones remains a primary cause in Thailand. In order to effectively against this challenge (overspeeding), the deep-rooted factors influencing speeding behaviors, particularly drivers’ risky behaviors, must be understood. Thus, this study employs the theory of planned behavior (TPB) and the framework comprising three basic Es (Education, Engineering, and Enforcement) and additional Es (Emergency response), i.e., the 3Es + Es framework, to examine these deep-rooted factors while considering the riders’ sociodemographic data. Additionally, we performed structural equation modeling to investigate the factors influencing speeding behaviors, with key findings revealing that Engineering factors significantly account for overspeeding. Conversely, we revealed that attitude, subjective norm, and perceived behavioral control (which are essential TPB components) significantly influence riders’ intentions to exhibit safe behavior, resulting in reduced speeding. Additionally, our examination of latent factors based on riders’ sociodemographic data revealed that age, marital status, income, riding experience, crash history, and traffic tickets are significant factors that determine speeding habits. Specifically, we observed that single riders and those with less than five years of riding experience were less likely to exhibit safe riding behaviors. Overall, our findings would benefit Thailand’s road-safety authorities, as we specifically proposed appropriate policies and empirical guidelines for Thailand’s industrial zones, which are prone to high crash rates. This could effectively reduce speeding among motorcycle riders and mitigate traffic crashes.

This study aimed to identify and compare the risk factors associated with motorcycle crash severity during both daytime and nighttime, for single and multivehicle incidents in Thailand using 2021–2024 data. The research employed the XGBoost (Extreme Gradient Boosting) method for statistical analysis and extensively examined the temporal instability of risk factors. The results highlight the importance of features impacting the injury severity of roadway collisions across various conditions. For single motorcycle crashes, the key risk factors included speeding, early morning incidents, off-road events, and long holidays. In multivehicle crashes, rear-end collisions, interactions with large vehicles, and collisions involving other motorcycles or passenger cars were linked to increased injury severity. The findings indicate that the important factors associated with motorcyclist injury severity in roadway crashes vary depending on the type of crash and time of day. These insights are valuable for policymakers and relevant authorities in developing targeted interventions to enhance road safety and mitigate the incidence of severe and fatal motorcycle crashes.