Thanapong Champahom

(1) Background: Motorcyclists remain disproportionately represented in road-traffic fatalities and serious injuries worldwide, yet the behavioral factors associated with their crash involvement are still incompletely understood. (2) Methods: This study integrates several established behavioral theories—Human Information Processing (HIP), Reason’s Generic Error-Modelling System (GEMS), the Theory of Planned Behavior (TPB), and Protection Motivation Theory (PMT)—into a single mixed-theory framework in order to examine simultaneously how risk behavior and protective behavior are associated with self-reported motorcycle crash involvement. A cross-sectional survey was administered to 2910 active motorcyclists using a Modified Motorcycle Rider Behavior Questionnaire (MRBQ) to capture four dimensions of risk behavior. (3) Results: A second-order confirmatory factor analysis (CFA) confirmed that the four risk dimensions load onto a single higher-order motorcyclist risk behavior construct, and the full measurement model demonstrated good reliability, convergent validity, and discriminant validity. Structural equation modeling (SEM) showed excellent fit. Motorcyclist risk behavior was positively and significantly associated with crash involvement, whereas protective behavior was negatively associated with it; because protective equipment mainly reduces injury severity rather than preventing crashes, this inverse relationship is interpreted as an indirect association rather than a direct reduction in crash occurrence, and both hypotheses were supported. (4) Conclusions: The findings support the value of integrating error-based and motivation-based theories when modeling motorcyclist safety and highlight the need for generationally tailored interventions that simultaneously reduce risky riding and promote consistent protective behavior.

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.

Crashes involving freight and commercial vehicles impose substantial human and economic costs, yet most severity studies pool vehicle types or focus exclusively on heavy trucks, masking class-specific risk mechanisms. This study estimates separate Random Parameters Binary Logit models with heterogeneity in means and variances for three vehicle categories—heavy-duty multi-axle trucks (n = 6512), two-axle trucks (n = 2656), and light-duty pickup trucks (n = 23,477)—using 32,645 crash records from Thailand’s national highway network (May 2022–December 2024). Pairwise transferability tests rejected parameter transferability, with four of six comparisons exceeding the 97 percent confidence level (three of these above 99 percent; χ² = 85.38 to 240.01), confirming that disaggregate estimation is statistically warranted. Three core findings emerge: First, although barrier medians, cut-in-front maneuvers, and sideswipe crashes affect severity in consistent directions across all vehicle types, their magnitudes differ sharply: the protective effect of barrier medians is nearly six times larger for two-axle trucks (ME = −0.160) compared to heavy-duty trucks (ME = −0.028). Second, several determinants are class-specific: dark unlit conditions elevate severity only for two-axle trucks (ME = 0.128), flush medians only for heavy-duty trucks (ME = 0.040), and raised medians only for light-duty pickups (ME = 0.042). Third, no random parameter is common to all three models. Pooled models, therefore, impose misleading homogeneity assumptions; vehicle-type-specific estimation is essential for targeted safety policy.

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.