Search

Existing studies often overlook the nuanced differences between various road classifications and their respective crash dynamics, hindering the development of targeted interventions to mitigate crash severity. To address this gap, this study investigates factors influencing the likelihood of fatality in road crashes across highways, collector roads, and local roads in Thailand using crash data from 2015 to 2021. Highways connect regions with high-speed traffic and large volumes, collector roads link smaller communities with lower traffic density but allow higher speeds, and local roads primarily pass through villages, with narrow pathways, two traffic lanes, and frequent motorcycle use. The study employs machine learning methodologies utilizing tree-based algorithms, including Decision Trees, Random Forest, Gradient Boosting, AdaBoost, Extra Trees, XGBoost, LightGBM, and CatBoost. The XGBoost model delivered superior performance for highways, while Gradient Boosting slightly outperformed XGBoost for local and collector roads. Both models consistently achieved a test accuracy of 0.70, with precision between 0.66 and 0.67, recall ranging from 0.59 to 0.61, and F1-scores from 0.58 to 0.61. The AUC values also consistently ranged from 0.59 to 0.61. SHAP values reveal key factors influencing fatality risk across road types, including speeding, gender disparities, driving under the influence of alcohol, inadequate lighting, and elderly drivers. Specific concerns include reversing on highways, collisions in poorly lit areas on collector roads, and helmet non-use on local roads. The findings support policy recommendations to address speeding, target male and older drivers, prevent reversing incidents, enhance lighting, and promote helmet use. This research deepens our understanding of factors affecting road crash severity and offers valuable insights for improving road safety across various environments.

This study aims to examine the differentiated impacts of frontage-lane, main-lane, and standard-lane motorcycle collisions on rider injury severity in Thailand. Using comprehensive crash data from 2016 to 2019, a random parameters model accommodating heterogeneity in means and variances is applied. Predictive comparisons between out-of-sample and within-sample predictions uncover differences between collision location sub-models. For frontage-lane collisions, severe or fatal injuries are positively associated with male riders, truck involvement, and speeding. In main-lane collisions, increased severe/fatal injury risk is associated with nighttime, holidays, weekends, single-motorcycle crashes, and collisions with trucks or pickups. In standard-lane collisions, positive severe/fatal injury associations include male riders, nighttime, non-peak hours, speeding, truck/pickup involvement, and pillion presence. Predictive comparisons show reorienting crashes to the frontage lane could reduce fatal injury probability by 0.1199 and 0.2233 versus the main and standard lanes respectively, preventing many fatalities. This research underscores accounting for unobserved heterogeneity and predictive simulation insights. The findings inform effective countermeasures and aid safety professionals, instructors, policymakers, law enforcement, and designers in motorcycle safety efforts.

This study explores the influence of Thai government policy perceptions on the adoption of electric vehicles (EVs). Transitioning to EVs is vital for reducing greenhouse gas emissions and combating climate change, aligning with global sustainability goals. This study addresses gaps in understanding how multidimensional perceptions of government policies influence EV adoption intentions in emerging markets, particularly in Thailand. A questionnaire was distributed to 3770 respondents across Thailand between January and March 2024. The survey assessed multiple dimensions of government policy, including commitment and efficiency, welfare, communication, policy effectiveness, and tax benefits. Using statistical techniques such as Exploratory Factor Analysis (EFA), second-order confirmatory factor analysis (CFA), and structural equation modeling (SEM), this study validated the constructs of government support perception and examined their influence on EV adoption intentions. The findings highlight that tangible government policies, particularly those improving EV infrastructure and providing clear regulatory support, alongside effective communication about these policies, significantly influence public willingness to adopt EVs. The results also emphasize the critical role of perceived government commitment and fiscal incentives in shaping consumer decisions. Based on these insights, this study recommends prioritizing the expansion of EV infrastructure, enhancing the visibility of government commitment, and improving direct financial incentives to accelerate EV adoption. These findings contribute to the growing body of knowledge on EV adoption in emerging markets and offer practical implications for policymakers seeking to promote sustainable transportation solutions.

Wrong Way Driving (WWD) by motorcycle riders is a traffic violation that may cause crashes. It is frequently encountered in developing countries. The purpose of this study is to examine the factors influencing WWD among motorcycle riders. This study selected six sites in Khon Kaen city, Thailand, to examine motorcycle riders’ driving behavior, either WWD or law-obeying driving, such as when making a U-turn. The driving behaviors of 1,114 motorcycle riders were collected. The random parameter mixed logit model was applied to analyze the correlation between motorcycle riders’ WWD behavior and influencing variables. The results indicated that WWD influencing factors included gender, helmet wearing, time of day, motorcycle size, the presence of LED warning signs, control of U-turn median opening, and the ratio of the travel distances between the law-obeying route and the WWD route. Finally, this study proposed recommendations to reduce the occurrence of motorcycle riders’ WWD behavior.

Background: The automotive industry is pivotal in advancing sustainability, with electric vehicles (EVs) essential for reducing emissions and promoting cleaner transport. This study examines the determinants of EV adoption intentions in Thailand, integrating demographic and psychographic factors from Environmental psychology and innovation diffusion theory; Methods: Data from a structured questionnaire, administered to 4003 respondents at gas stations with EV charging facilities across Thailand, were analyzed using a Correlated Mixed-Ordered Probit Model with Heterogeneity in Means (CMOPMHM); Results: Findings indicate that younger adults, particularly those aged 25–34 years old and 45–54 years old, are more likely to adopt EVs, whereas conventional or hybrid vehicle owners are less inclined. Rural residency or travel also hinders adoption. Individuals with strong environmental values and openness to new technologies are more likely to adopt EVs; Conclusions: The proposed model quantified the relative importance of these factors and uncovered heterogeneity in user preferences, offering reliable and valuable insights for policymakers, EV manufacturers, and researchers. The study suggests targeted policies and enhanced charging infrastructure, especially in rural areas, and recommends leveraging environmental values and trialability through communication campaigns and test drive events. These insights can guide the development of targeted incentives, infrastructure expansion, communication strategies, and trialability programs to effectively promote wider EV adoption in Thailand and similar markets.

Background: Walking and cycling are examples of active travel modes or nonmotorized modes that rely on human physical power rather than fossil fuel consumption. In the context of short-distance journeys, active travel modes are advocated as feeder modes to reduce energy consumption and CO2 emissions. However, in Thailand and many other developing countries, these modes of transportation have not been widely adopted or effectively promoted. The absence of comprehensive campaigns and interventions to promote and facilitate the use of active travel modes has become a major barrier to their adoption, particularly among adolescents who will be future global citizens. Therefore, a campaign or intervention targeting adolescents is imperative to introduce and persuade them to adopt active travel modes. This study aims to provide guidelines for developing a robust intervention strategy to promote active travel modes among adolescents. Methods: This study performed a systematic review to achieve the research goal using a particular search and selection approach. The search strategy has focused on published studies in the English language since 2014 to highlight the most recent trends. The selection process focused on articles relevant to promoting active travel mode among children and adolescents (up to 18 years old) through intervention. Conclusions: A total of 16 studies were included. The findings reveal that successful interventions to promote active travel modes consist of an educational program and activities incorporating gamification to encourage their use. Furthermore, the intervention should last longer than one month to be effective.

Abstract Upon detecting a crash impact, the vehicle restraint system locks the driver in place. However, external factors such as speeding, crash mechanisms, roadway attributes, vehicle type, and the surrounding environment typically contribute to the driver being jostled within the vehicle. As a result, it is crucial to model unrestrained and restrained drivers separately to reveal the true impact of the restraint system and other factors on driver injury severities. This paper aims to explore the differences in factors affecting injury severity for seatbelt-restrained and unrestrained drivers involved in speeding-related crashes while accounting for temporal instability in the investigation. Utilizing crash data from Thailand between 2012 and 2017, mixed logit models with heterogeneity in means and variances were employed to account for multi-layered unobserved heterogeneity. For restrained drivers, the risk of fatal or severe crashes was positively associated with factors such as male drivers, alcohol influence, flush/barrier median roadways, sloped roadways, vans, running off the roadway without roadside guardrails, and nighttime on unlit or lit roads. For unrestrained drivers, the likelihood of fatal or severe injuries increased in crashes involving older drivers, alcohol influence, raised or depressed median roadways, four-lane roadways, passenger cars, running off the roadway without roadside guardrails, and crashes occurring in rainy conditions. The out-of-sample prediction simulation results are particularly significant, as they show the maximum safety benefits achievable solely by using a vehicle's seatbelt system. Likelihood ratio test and predictive comparison findings highlight the considerable combined impact of temporal instability and the non-transferability of restrained and unrestrained driver injury severities across the periods studied. This finding also demonstrates a potential reduction in severe and fatal injury rates by simply replicating restrained driver conditions. The findings should be of value to policymakers, decision-makers, and highway engineers when developing potential countermeasures to improve driver safety and reduce the frequency of severe and fatal speeding-related single-vehicle crashes.

Road accidents cause a large number of deaths, especially in Thailand. When considered in depth, motorcycles account for the highest percentage of fatalities. According to Heinrich's Safety Triangle Model, a decrease in near misses will reduce the number of road accidents. There is still a lack of studies on risky behaviors contributing to near misses involving motorcycles. This study aims to comprehend the various factors that influence the frequency of near-miss experiences using a questionnaire on near-miss incidents. The contributing factors include road factors (e.g., road surface, number of traffic lanes, speed limit), environmental factors (e.g., driving at night), and driver factors (e.g., using a phone while driving). Of the 2002 respondents, a total of 1547 people have occasionally experienced a near-miss incident. A random parameter probit model (RPOP) was used for analyzing the relationship between the contributing factors and the near-miss frequency, and model statistics clearly confirm that RPOPs that import only significant variables are the most suitable models. The study found 14 factors that affect near-miss frequency, and there are 5 variables that are random parameters. Variables that increase the chance of a near-miss incident include driving at night (both with and without lights), roads with concrete road surfaces, and roads with unclear lane markings. This study provides policy recommendations for relevant agencies that were identified to reduce near-miss motorcycle accidents.

This study proposes a method for evaluating losses sustained from motorcycle crashes in Thailand, utilizing the willingness to pay (WTP) approach. Value of statistical injury and Value of statistical life were used to identify the value. Structural equation modeling (SEM) using TBP as a framework found that behavioral intention, attitudes, and perceived behavioral control influence motorcycle drivers' WTP in Thailand. The WTP for reducing fatalities from motorcycle crashes is US$4.41 to 4.98, and the WTP to mitigate injury risks is US$23.49 to 24.53. Additionally, the study calculates the value of statistical life (VOSL) to be in the range of US$0.055 to 0.062 million and the value of statistical injury (VOSI) to be between US$0.188 to 0.194 million. This study can be used to inform budget allocation and policy formulation to reduce the risks of motorcycle road crashes.

The COVID-19 epidemic has had a profound impact on tourism in Thailand and worldwide. As one of the top-ranked global tourist destinations, Thailand has experienced significant effects on its economy and the well-being of its inhabitants. The objective of this study is to investigate the variations in tourists' behavior during and after the COVID-19 epidemic using the Health Belief Model and Travel planned behavior theories. Invariance analysis was tested across groups. The analysis revealed that attitude, perceived susceptibility, perceived benefits, government support, and perceived control had the most positive impact on travel intentions during the 2019 coronavirus epidemic. In contrast, perceived barriers, perceived severity, and health motivation had significant negative influences. Concerning behavioral intention after the outbreak, all the factors were significantly associated positively. The most influential factors were perceived susceptibility, attitude, health motivation, perceived behavioral control, perceived benefits, perceived severity, perceived control, and government support, respectively. Furthermore, the models of both groups during and after the epidemic were significantly different. Therefore, this study provides valuable insights for policymakers to develop strategies to stimulate the economy and sustainable tourism in the future.

Introduction: This research aims to identify and understand the risk factors associated with injury severities in accidents occurring at highway–rail grade crossings in the context of the developing country, Thailand.Method: The mixed logit model was employed by analyzing crash data over 10 years, from 2012 to 2022.Results: The analysis revealed a number of significant factors associated with severe or fatal crashes. These included accidents that occurred between midnight and 6 a.m., collisions involving pickup cars or heavy trucks, roads with a “no overtaking zone” sign, intersections classified as Type B1 (defined by the presence of only warning posts and horizontal crossing barriers), and intersections without adequate traffic control devices. In contrast, hazard markers on pavements and traverse rumble strips were positively correlated with property damage only crashes, where no injuries were reported.Discussion: This study provides insights into contributory factors to accidents at highway–rail grade crossing. Based on these key findings, the study recommends increasing nighttime visibility at railway grade crossings, developing targeted education and training programs for pickup car and truck drivers, installing hazard markers and traverse rumble strips, and considering physical barriers, such as bollards or delineators, to discourage overtaking maneuvers near the railway crossing.

Background: Scholars have indicated differences in the attitudes of urban and non-urban populations, especially after the COVID-19 outbreak, which extend to their needs and expectations regarding rail transport development. The aim of this study is to enhance the quality of train services in the post-pandemic era, and multigroup analysis will be applied to achieve the difference in area context. Methods: The research data were collected from rail transport users throughout Thailand, consisting of 665 urban and 935 rural users. The questionnaires primarily focused on user expectations regarding rail service quality and travel conditions in the post-pandemic landscape using multigroup confirmatory factor analysis (MCFA). Results: The results unveiled significant variations in user trends and needs across different contexts and areas. In urban settings, there was a notably higher overall service expectation compared to rural areas. Specifically, urban users prioritized factors such as accessibility and service empathy, whereas rural rail users placed greater emphasis on staff quality and reasonable pricing. Conclusions: These findings furnish rail transport service agencies with valuable insights and guidance for comprehending their users’ needs. They can develop appropriate organizational strategies, service quality enhancements, and policy adjustments tailored to the unique demands of urban and rural areas in the post-pandemic era, thereby ensuring sustainability. Additionally, the methodology of multigroup analysis served as a significant scientific contribution; this showed that the statistical analysis of different area contexts in the study should not be ignored.

Preventing near-miss incidents is considered a proactive measure, as it aims to prevent events that have a risk of resulting in accidents. This is regarded as a vital component of building a sustainable and secure society within communities. In the present day, low- and middle-income countries (LMICs) often experience the highest fatality rates from motorcycle accidents, which frequently involve mixed traffic scenarios with other vehicles. The distinct physical characteristics and environmental conditions of roads in urban and rural areas significantly contribute to different riding behaviors. Therefore, the objective of this study is to develop a behavioral model related to near-miss incidents among motorcycle riders in both urban and rural regions using multi-group structural equation modeling (SEM). Data collected from six Thai regions via adapted MRBQ assessed control errors, violations, and safety equipment use in a sample of 2002 riders (1066 urban, 936 rural). Through parameter invariance testing, differences in factor loadings, intercepts, and structural paths were identified between urban and rural areas. All three of these factors significantly influenced near-miss incidents among motorcycle riders in both urban and rural areas. The policy recommendations resulting from this study can contribute to enhancing safety measures for motorcycle riders.

Statistical data indicate that trucks are more prone to rear-end crashes, making this an area of concern. The objective of this study is to create a model that analyzes the factors influencing the frequency of rear-end crashes involving trucks (TIRC). To achieve this, researchers identified the most appropriate model as Spatial Zero-Inflated Negative Binomial Regression (SZINB). This model takes into account spatial correlation, which plays a significant role in the occurrences of TIRC on different road segments supervised by each highway ward. The estimation of parameters in the SZINB model has led to key findings that shed light on the factors contributing to a higher likelihood of TIRC. These findings include the increased probability of TIRC on curved roads compared to straight ones, roads that feature open middle islands, six lanes per direction, a slope, right-of-way shoulder width, pavement type, lane width, and a post speed limit. Based on these key findings, this study developed policy recommendations and sample measures aimed at reducing the frequency of TIRC. Implementing measures such as improving the road design on curved sections, optimizing middle islands, and enhancing traffic management on wider roads can help mitigate the risk of crashes involving trucks.

Thailand was classified as a middle-income country and ranked second highest in terms of road traffic fatality rate in the world in 2015. By 2018, this ranking went up to ninth in world which may be because of various earnest safety policies implementation, supporting road safety research and establishing a road safety directing center. However, crash fatality rate has considerably remained high until recent year, indicating a clear need for further related research. Considering severity of the crashes, the majority of fatal crashes involved the motorcycle road user. Therefore, motorcycle crashes are important issues and should be considered to mitigate fatality due to immoderate proportion of motorcycle road user and motorcyclist fatality. This study aims to identify factors that influence the severity of motorcycle accidents on Thailand's arterial roads by employing ordered logistic regression and multiple correspondence analysis. The results demonstrated that although both analyses were relatively different, they provided similar results. Age, road lanes, and helmet wearing were significant factors that influenced the severity of motorcycle accidents. The results could serve as reference for planning strategies or organizing campaigns to reduce and prevent death owing to road traffic accidents, which may enhance the overall image of road traffic safety in Thailand.

In developing nations, motorcycles are a ubiquitous mode of transportation on local road networks, contributing to a substantial frequency of traffic accidents and fatalities. These occurrences exhibit significant discrepancies between urban and rural road environments. Specifically, urban roads exhibit greater traffic density and more robust law enforcement presence compared to their rural counterparts, factors that invariably influence vehicular speeds. Consequently, these variations in speed are associated with the adoption of riskier behaviors by motorists, with a pronounced impact on Young Adult Motorcyclists (YAMs) who demonstrate a heightened propensity for engaging in perilous activities, such as operating a motorcycle while under the influence or executing excessively close maneuvers. This study seeks to examine the factors that influence injury severity among YAM on local roads by contrasting urban and rural roadways, taking into account the different roadway contexts. Data from motorcycle crashes on local roads in Thailand between 2018 and 2020 were analyzed using the Random Parameter Logit Models with Unobserved Heterogeneity in Means and Variances (RPLMV) approach to investigate the relationship between contributing factors and YAM injury severity. The findings revealed several critical insights into the factors influencing the severity of motorcycle accidents, particularly among YAM on local urban roads. It is evident that a confluence of factors, including the absence of a valid driver’s license, exceeding designated speed limits, and the involvement of male drivers, significantly elevates the probability of fatal outcomes in these accidents. Our analysis has also unveiled intriguing patterns in nighttime accidents involving motorcycles, where those with functioning vehicle lights and those occurring under the cover of darkness without proper illumination share notable similarities in terms of severity. Furthermore, our research has emphasized that accidents transpiring outside city confines during nighttime hours with adequate lighting exhibit a negligible impact on higher crash severity. The key findings advocate for the development and implementation of targeted policy recommendations and countermeasures to alleviate the severity of accidents involving YAM. This includes a proposition to bolster law enforcement efforts, particularly in regions beyond city borders, and to enforce strict adherence to regulations concerning driver’s license verifications. Our study offers a crucial foundation for future research and policy development aimed at improving road safety and reducing the severity of motorcycle accidents, with the ultimate goal of safeguarding the lives of YAM.

The COVID-19 pandemic has significantly disrupted railway transportation in developing countries, resulting in reduced passenger demand and economic activity. As a result of the pandemic effect, there is an increased focus on health and safety among potential passengers. To address this issue, the present study aimed to investigate the fundamental factors that contribute to railway transportation service quality (SERVQUAL) and the intention to use intercity train services in Thailand using the health belief model (HBM), with 1600 passenger participants using structural equation modeling (SEM). The findings demonstrated that train operators’ service standards remain vital to passengers, and the HBM had a statistically significant impact on shaping passengers’ intentions to use train services after an epidemic. These results can inform rail agencies and health authorities when developing policies and strategies to prioritize both business and passenger safety on intercity trains.

Machine learning currently holds a vital position in predicting collision severity. Identifying factors associated with heightened risks of injury and fatalities aids in enhancing road safety measures and management. Presently, Thailand faces considerable challenges with respect to road traffic accidents. These challenges are particularly acute in industrial zones, where they contribute to a rise in injuries and fatalities. The mixture of heavy traffic, comprising both trucks and non-trucks, significantly amplifies the risk of accidents. This situation, hence, generates profound concerns for road safety in Thailand. Consequently, discerning the factors that influence the severity of injuries and fatalities becomes pivotal for formulating effective road safety policies and measures. This study is specifically aimed at predicting the factors contributing to the severity of accidents involving truck and non-truck collisions in industrial zones. It considers a variety of aspects, including roadway characteristics, underlying assumptions of cause, crash characteristics, and weather conditions. Due to the fact that accident data is big data with specific characteristics and complexity, with the employment of machine learning in tandem with the Multi-variate Adaptive Regression Splines technique, we can make precise predictions to identify the factors influencing the severity of collision outcomes. The analysis demonstrates that various factors augment the severity of accidents involving trucks. These include darting in front of a vehicle, head-on collisions, and pedestrian collisions. Conversely, for non-truck related collisions, the significant factors that heighten severity are tailgating, running signs/signals, angle collisions, head-on collisions, overtaking collisions, pedestrian collisions, obstruction collisions, and collisions during overcast conditions. These findings illuminate the significant factors influencing the severity of accidents involving trucks and non-trucks. Such insights provide invaluable information for developing targeted road safety measures and policies, thereby contributing to the mitigation of injuries and fatalities.

Among several approaches to analyzing crash research, the use of machine learning and econometric analysis has found potential in the analysis. This study aims to empirically examine factors influencing the single-vehicle crash for personal cars and trucks using decision trees (DT) and mixed binary logit with heterogeneity in means and variances (RPBLHMV) and compare model accuracy. The data in this study were obtained from the Department of Highway during 2011–2017, and the results indicated that the RPBLHMV was superior due to its higher overall prediction accuracy, sensitivity, and specificity values when compared to the DT model. According to the RPBLHMV results, car models showed that injury severity was associated with driver gender, seat belt, mount the island, defect equipment, and safety equipment. For the truck model, it was found that crashes located at intersections or medians, mounts on the island, and safety equipment have a significant influence on injury severity. DT results also showed that running off-road and hitting safety equipment can reduce the risk of death for car and truck drivers. This finding can illustrate the difference causing the dependent variable in each model. The RPBLHMV showed the ability to capture random parameters and unobserved heterogeneity. But DT can be easily used to provide variable importance and show which factor has the most significance by sequencing. Each model has advantages and disadvantages. The study findings can give relevant authorities choices for measures and policy improvement based on two analysis methods in accordance with their policy design. Therefore, whether advocating road safety or improving policy measures, the use of appropriate methods can increase operational efficiency.

Road medians play an important role in the design of four or more lanes highways with high traffic volume and the large number of accidents. This study focused on effect of factors (such as traffic volume, road characteristics, land use and road median types) on the number of accidents. The data was collected from nationwide highway, and further used to build the models by using Generalized linear with Negative binomial model distribution classified by road median types including raised-, depressed-, flush-and barrier median. The result found that the increase in traffic volume and truck ratio are positively associated with more number of accidents in all four road median model. The finding also recommends that raised-, depressed-and flush median should be implemented within agriculture and rural area in order to decrease crashes frequency. The contributions of this study could be used as guideline regarding highways median type implementation decision-making to enhance road safety in Thailand.

This research aims to study the factors affecting elder’s transportation mode selection by considering 4 transportation types including bicycle or motorcycle, car and public transportation (which includes bus, taxi, and public motorcycle) in accordance with common transportation types in Thailand. The methodology used for analysis was a multinomial logistic regression of which independent variable was nominal scale and using a car to be the base model compared to the elderly people’s change of transportation mode for result interpretation. The study results concluded the common characteristics of aging mobility that travel frequency is less than once a week, travel time is the morning, and most of activities is shopping. The private car transportation should be improved for the convenience. In addition, motorcycle and bike lanes should be developed for serving aging people due to their less income, and travel time use which is less than 30 minutes. Furthermore, public transportation should be also enhanced to accommodate their travel activities such as going to the hospital, and shopping.

The aim of this study was to identify factors such as: roadway operational characteristic, crash characteristic, surrounded environment, vehicle type, driver information, severity level of driver, and temporal information, affecting driver injury severity involving in single-vehicle run off road accident occurred on 2-lanes highways and 4-lanes highway using Multinomial Logit model. The analyses used secondary data obtained from police accident record (extracted from Highway Accident Information Management System (HAIMS)).The variables were found to increase chance of fatality are driver older than 55-year-old, driver under influence of alcohol, drowsiness driver, run off road on straight and curve, accident on highways with depressed median and accident on concrete pavement. The variables were found to mitigate severity are adult driver 25-35-year-old, using seat belt, accident on highway with raised median and hit fixed object accident. The contributions of this study were drawn: Thailand related authorities such as Department of Highway or Royal Thai Police should emphasize their effort on education campaigns on road safety for all road users, especially old drivers, enforce the law on drunk driving and seatbelt; and for road design perspective: monitor and build roadside safety features such as safety barrier alongside the highway particularly run off road black spot and curve roads. The study also mentions the safety benefit of asphalt pavement over concrete pavement and safety planner should consider implementing raised median in urban area for safety purpose.

This paper evaluates the perceived bus service quality by passengers in Mauritius. Structural equation modeling (SEM) was used to identify the unobserved latent variables relevant to public passenger bus service and reveal the relationships between these latent variables and customer satisfaction. The data were obtained from a customer satisfaction survey collected from 501 participants in Mauritius and three latent variables that described the expected service quality characteristics, namely Vehicle, Management, and Driver. The SEM results revealed that the most significant expectation was the quality of the vehicle that included the cleanliness and vehicle body. The second was driver and crews and the personal attributes of the bus service crew. The third anticipation was the overall management of the service. The findings of this research can provide crucial information for public transport bus operators to enable them not only to retain their current customers but also attract new customers.

The public transportation service quality continues to be one of the challenges for authorities and transportation stakeholders around the world. In this study, the researchers confront thischallenge by identifying key attributes that affect the users’ perceptions on bus service quality in Phnom Penh City. Questionnaire surveys were collected from bus users to measure their perceptions of the bus service quality. After that, data were analyzed using Factor analysis. Twenty-four quality attributes were analyzed utilizing exploratory factor analysis (EFA).This has led to theconclusion that five main factors affecting the perceptions of users regarding the quality of bus services. Theyare Bus Stop Facilities, Bus Services, Driver Attitude, Bus Capacity, as well as VehicleCondition.To check whether factor structure is acceptable, confirmatory factor analysis (CFA) was applied. In this context, the high factor loading of CFA means those attributes had forceful beneficial effectiveness on city bus service quality. The results of this study will help the authorities andinvolved stakeholders gaina depth of understanding of the underlying problemsofcity bus service and consequently will enhance the city bus service quality.

Objective. Among crash types on Thai highways, rear-end crashes have been found to cause the largest number of fatalities. This study aims to find ways to decrease rear-end crashes and fatal rear-end crashes. Methods. Classification and regression tree (CART) was used to analyze the complicated relationship of variables of big data. The analysis was conducted by creating two models: (1) a model which indicates the causes of rear-end crashes by applying Quasi-Induced Exposure to at-fault driver characteristics; (2) a determined model which studies fatal crashes. Results. Predictor variables in the model of at-fault and not-at-fault drivers found that driver age is most significant, followed by number of lanes and median opening area. For the mode of fatality, the use of safety equipment was found to be of most importance. Conclusion. The model results can be used to develop guidelines for public awareness programs for motorists and to propose policy changes to the Department of Highway in order to reduce the severity of rear-end crashes. Moreover, this paper discusses the variables that may result in both the perspective of rear-end crash number and the fatality rate of rear-end crashes as strategies in future research.

Countries that are still developing experience significant disparities in access to railway services, as these nations also grapple with societal inequality issues that remain unaddressed. In developed countries, railway transportation systems serve as the primary mode of transportation for both passengers and goods. However, in recent years, studies on disparities in developed countries have increased, while literature concerning developing countries remains scarce. Therefore, this study takes place in Thailand, a developing country facing significant population disparities. The objective is to examine factors contributing to these disparities in access to railway transportation systems across cities, using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) to identify user disparities. The sample group comprises 1,252 randomly selected railway users from various regions in Thailand, obtained through Stratified Random Sampling. The results reveal seven dimensions of disparities: cultural, spatial, societal, political, knowledge-based, economic, and environmental. The CFA results also highlight cultural disparities as a significant factor in explaining access disparities among railway users. These findings can inform relevant organizations, aiding them in better understanding the actual needs of railway users and aligning railway development plans accordingly. Ultimately, this contributes to policy development aimed at reducing access disparities and fostering a more equitable society. [ABSTRACT FROM AUTHOR]

Regarding punctuality and highly safe mode lead, rail transports become popular. Currently, in several countries traveling by train has been widely employed, resulting in the development in various aspects and motivating people to use more rail transports. Despite a long train service operation in Thailand, the number of passengers continually and evidently decreased every year. The aforementioned issue may be caused by insufficient improvement and development of railway stations. Therefore, the factors that potentially influence railway stations' service perception should be refined in order to provide effective strategies and policies to increase rail transport's passengers. In this study, 370 trains users with 18 indicators were collected to seek insight into the factors that influenced service quality perception. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied in this study. The finding reveals that indicators are classified into 3 factors, namely, Service, Safety and Facilities, and Information factor. These 3 factors had an ability to significantly measure service quality perception toward railway stations. In addition, safety and facilities were found as the major factor affecting service quality perception with ß=0.988. The findings may be used as a potential guideline for attracting passengers' demand, developing strategies and policies to build better service quality including facilities lead to enhance the good image of railway stations in Thailand. [ABSTRACT FROM AUTHOR]