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Jomnonkwao, Sajjakaj
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Civil Engineering, Building, Construction & Architecture

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Measuring Belt and Road Initiative Perceptions: A Comparative Analysis of Thai Border and Non-Border Regions Champahom, Thanapong; Chonsalasin, Dissakoon; Theerathitichaipa, Kestsirin; Jomnonkwao, Sajjakaj; Watcharamaisakul, Fareeda; Kasemsri, Rattanaporn; Ratanavaraha, Vatanavongs
Civil Engineering Journal Vol 10, No 12 (2024): December
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-12-010

Abstract

This study aims to analyze and compare perceptions of the Belt and Road Initiative (BRI) between border and non-border regions in Thailand, addressing a gap in understanding how geographic proximity influences BRI project views. Using a sample of 3,200 respondents, this study employed confirmatory factor analysis and measurement invariance techniques to examine perceptions across eight key constructs related to BRI impacts. The findings reveal significant structural differences in BRI perceptions between border and non-border regions. Non-border regions generally showed more consistently positive perceptions across all constructs, while border regions demonstrated more varied and nuanced views. Notable differences were observed in perceptions of economic benefits, logistics improvements, and social impacts. This study contributes to the field by providing a comprehensive comparative analysis of BRI perceptions across different geographical contexts within a single country, employing advanced statistical methods to ensure valid comparisons. The results suggest the need for tailored approaches to BRI implementation and communication in different regions, implementing inclusive policy-making processes, and establishing robust monitoring and evaluation systems to address the varied perceptions and potential impacts of BRI projects in Thailand. Doi: 10.28991/CEJ-2024-010-12-010 Full Text: PDF
Empirical Analysis of Risk Behavior in Truck Drivers Across Industrial Zones and Policy Recommendations Seefong, Manlika; Wisutwattanasak, Panuwat; Se, Chamroeun; Banyong, Chinnakrit; Theerathitichaipa, Kestsirin; Jomnonkwao, Sajjakaj; Champahom, Thanapong; Ratanavaraha, Vatanavongs; Kasemsri, Rattanaporn
Civil Engineering Journal Vol. 11 No. 10 (2025): October
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-10-010

Abstract

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.
A Comparative Study of a Series of Supervised Learning Models for Motorcycle Crash Injury Severity Prediction Sum, Sonita; Wisutwattanasak , Panuwat; Champahom, Thanapong; Jomnonkwao, Sajjakaj; Ratanavaraha, Vatanavongs
Civil Engineering Journal Vol. 11 No. 10 (2025): October
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-10-014

Abstract

Motorcycle crashes pose a major public health challenge in Thailand, where motorcyclists account for most traffic fatalities. This study aims to evaluate and compare the predictive performance of four supervised learning models—Decision Tree (DT), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)—for motorcycle crash injury severity using data from the Highway Accident Information Management System (2020–2022). After preprocessing, 36 explanatory variables covering roadway, environmental, accident causes, crash characteristics, and vehicle involvement were analyzed. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) and cost-sensitive learning were applied, and models were validated using train–test splits with cross-validation. The Random Forest model achieved the best performance with an AUC of 0.726, balanced accuracy of 0.649, and Matthews Correlation Coefficient (MCC) of 0.308, outperforming the other algorithms. SHapley Additive exPlanations (SHAP) were used to interpret the RF model, identifying nighttime crashes, large truck involvement, and roadway features (e.g., depressed medians and two-lane roads) as key predictors of severe outcomes. These insights suggest countermeasures such as improving nighttime safety, dedicating truck lanes, and designing safer medians. The novelty of this study lies in integrating model comparison, imbalance-aware metrics, and SHAP interpretability to provide actionable, context-specific policy recommendations for motorcycle safety in Thailand.
Measurement Invariance of Expectations Toward Sustainable Public Transport Service Quality Among Urban and Rural Older Adults Chantaratang, Anon; Chonsalasin, Dissakoon; Wisutwattanasak, Panuwat; Watcharamaisakul, Fareeda; Champahom, Thanapong; Ratanavaraha, Vatanavongs; Jomnonkwao, Sajjakaj
Civil Engineering Journal Vol. 11 No. 12 (2025): December
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-12-016

Abstract

This study examines measurement invariance of expectations toward sustainable public transport service quality between urban and rural older adults in Thailand. Using second-order confirmatory factor analysis, data were collected from 1,189 elderly respondents across Thailand's four major regions through face-to-face interviews. The measurement framework incorporated eleven service quality dimensions: nine traditional attributes (Vehicle, Bus Stop, Accessibility, Convenience, Information, Staff, Safety and Security, Reliability, and Affordability) and two extended dimensions (Older's Facilities and Post-Pandemic Prevention). Results demonstrated successful measurement invariance, confirming that the eleven-factor structure operates equivalently across urban and rural contexts. Universal priorities emerged for Convenience, Staff quality, and Reliability, while rural elderly showed elevated importance for Safety and Security. The validation of Older's Facilities and Post-Pandemic Prevention as distinct dimensions establishes empirical support for incorporating age-inclusive design and health protection measures as permanent components of sustainable transport planning, justifying unified national standards while accommodating regional variations for Thailand's aging population.
Co-Authors Banyong, Chinnakrit Champahom, Thanapong Chantaratang, Anon Chonsalasin, Dissakoon Kasemsri, Rattanaporn Ratanavaraha, Vatanavongs Se, Chamroeun Seefong, Manlika Sum, Sonita Theerathitichaipa, Kestsirin Watcharamaisakul, Fareeda Wisutwattanasak , Panuwat Wisutwattanasak, Panuwat