Examining factors affecting driver injury severity in speeding-related crashes: a comparative study across driver age groups

This paper investigates the factors influencing the severity of driver injuries in single-vehicle speeding-related crashes, by comparing different driver age groups. This study employed a random threshold random parameter hierarchical ordered probit model and analysed crash data from Thailand between 2012 and 2017. The findings showed that young drivers face a heightened fatality risk when speeding in passenger cars or pickup trucks, hinting at the role of inexperience and risk-taking behaviours. Old drivers exhibit an increased fatality risk when speeding, especially in rainy conditions, on flush median roads, and during evening peak hours, attributed to reduced reaction times and vulnerability to adverse weather. Both young and elderly drivers face escalated fatality risks when speeding on road segments lacking guardrails during adverse weather, with older drivers being particularly vulnerable in rainy conditions. All age groups show an elevated fatality risk when speeding on barrier median roads, underscoring the significant role of speeding, which increases crash impact and limits margins of error and manoeuvrability, thereby highlighting the need for safety measures focusing on driver behaviour. These findings underscore the critical imperative for interventions addressing not only driver conduct but also road infrastructure, collectively striving to curtail the severity of speeding-related crashes.

Modeling of single-vehicle and multi-vehicle truck-involved crashes injury severities: A comparative and temporal analysis in a developing country

Truck-involved crashes persist as a significant concern, yielding noteworthy human casualties and causing economic ramifications, particularly in developing countries. This paper aims to undertake a comprehensive analysis of the associated factors influencing injury severity in truck-involved crashes, with a particular emphasis on discerning variations between single-vehicle and multi-vehicle incidents, as well as accounting for heterogeneity and temporal stability. The data analysis involves a meticulous examination of crash data spanning the entirety of Thailand from 2017 to 2020. Employing three distinct levels of injury severities, namely PDO injury, moderate injury, and severe injury, the study employs a series of mixed logit models that account for unobserved heterogeneity in both means and variances. Results revealed significant instability in injury risk determinants over time among both single and multi-vehicle events. Aligning predictive assessments further spotlighted fluctuations in projected burdens across models and years - collectively underscoring the imperative to integrate temporal considerations into modeling and prevention. Several crash-type distinctions and priorities emerged. For single-truck events, key risks included roadway alignments and geometry, speeding, fatigue, and lighting conditions. However multi-truck collisions concentrated around exposure factors like highway traits, sightline limitations, and vulnerable road users. Ultimately, the technique permitted responsive countermeasure targeting and recalibration opportunities keyed to each crash form's evolving landscapes. While it is indeed noteworthy that several variables have exhibited instability in their effects, it is equally important to acknowledge the existence of certain variables that maintain a relative degree of temporal stability. This underscores their pivotal role in shaping the foundation of enduring strategies aimed at enhancing traffic safety in the long run. The multifaceted investigation constitutes an invaluable reference for diverse transportation stakeholders seeking to curb rising truck fatalities through evidence-based improvements in policy, engineering, usage protocols, and technologies. It provides a blueprint for nimble safety planning within complex modernizing road systems.

The intervention design to promote active travel mode among children and adolescents: A systematic review

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.

Examining the Impact of Service Quality on Passengers' Intentions to Utilize Rail Transport in the Post-Pandemic Era: An Integrated Approach of SERVQUAL and Health Belief Model

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.

Temporal instability and differences in injury severity between restrained and unrestrained drivers in speeding-related crashes

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.

Empirical examination of interdependent relationship between usage of seatbelt restraint system and driver-injury severity of single-vehicle crashes in Thailand using a joint econometric analysis

OBJECTIVES

The paper aims to examine the interdependent relationship between the usage of the seatbelt restraint system and severities of the driver-injury in single-vehicle crashes.

METHODS

This paper developed a comprehensive joint econometric structure - a joint random parameters binary probit-binary probit model - that allows for the simultaneous examination of injury severity of the driver in a crash, and taking into account the fact that seat belt use can be endogenous to the outcomes of driver injury. The developed model is tested using data on drivers-injury severities involved in single-vehicle crashes in Thailand from 2012-2017.

RESULTS

In terms of the interdependent relationship between seatbelt use status and driver-injury severities, the findings suggest that drivers who do not use seat belts may demonstrate more dangerous or aggressive driving behaviors (such as speeding), subsequently increasing their likelihood of involvement in severe or fatal crashes. Additionally, the result also shows that drivers who are involved in speeding-related crashes are less likely to wear a seatbelt and have a higher risk of sustaining severe and fatal injuries. The findings also reveal that in crashes, drivers who are young, or operating trucks are less likely to be wearing their seat belts. The study also indicates that severe and fatal crashes are associated with factors such as elderly drivers, alcohol involvement, unbelted drivers, fatigue, depressed medians, and barrier medians. Conversely, a crash in a U-turn area, driving a passenger car, pickup truck, or large truck, or colliding with a guardrail reduces the likelihood of severe and fatal injuries.

CONCLUSIONS

Neglecting the hidden endogenous effect in statistical analyses could result in an overestimation of the impact of seat belt usage on crash-injury outcomes. The findings of this study can provide valuable insights for relevant authorities aiming to improve driver safety.

Correlated random parameters model with heterogeneity in means for analysis of factors affecting the perceived value of road accidents and travel time

Road safety funding and management have become important issues in improving the quality of life of road users and there is evidence of a difference in driving behavior and the factor of road use between urban and rural areas, which is, in turn, reflected in different road safety evaluations. The purpose of this study is to assess the financial losses caused by road accidents on Thailand's highways and the related factors empirically, deploying the willingness-to-pay (WTP) approach. Data were obtained from 640 urban and 960 rural car drivers using a stated choice questionnaire and face-to-face interviews. This study used Correlated Random Parameters Binary Logit with Heterogeneity in Means (CRPBLHM) approach to analyze factors affecting WTP. According to the results, the value of a statistical life and injury for urban drivers was 1.63 times higher than that for rural drivers, and the value of travel time reduction per hour for urban drivers is ∼1.14 times higher than that found for rural drivers. Furthermore, the results of the CRPBLHM model reported that there are significant differences between urban and rural drivers' safety intentions and WTP. In the urban model, it was found that driving behavior (ticket, seatbelts, and speed) and weekday trips significantly increase the WTP, whereas household size and gender are unobserved characteristics among drivers. The rural model revealed that a driver's education (bachelor's and master's degrees held), and a necessary trip were significantly associated with drivers' valuation of safety; moreover, it was found that household size, sole earner status, own accident, in possession of a doctoral degree, and being young were significant in acting as unobserved characteristics. The results demonstrated differences in the value of road safety and unobserved heterogeneity among drivers, which influence risk perception and valuation with reference to the area context. Relevant agencies can use the results as a guideline for budget allocation and practical policy-related road safety management.

Temporal Instability of Motorcycle Crash Fatalities on Local Roadways: A Random Parameters Approach with Heterogeneity in Means and Variances

Motorcycle accidents can impede sustainable development due to the high fatality rate associated with motorcycle riders, particularly in developing countries. Although there has been extensive research conducted on motorcycle accidents on highways, there is a limited understanding of the factors contributing to accidents involving the most commonly used motorcycles on local roads. This study aimed to identify the root causes of fatal motorcycle accidents on local roads. The contributing factors consist of four groups: rider characteristics, maneuvers prior to the crash, temporal and environmental characteristics, and road characteristics. The study employed random parameters logit models with unobserved heterogeneity in means and variances while also incorporating the temporal instability principle. The results revealed that the data related to motorcycle accidents on local roads between 2018 and 2020 exhibited temporal variation. Numerous variables were discovered to influence the means and variances of the unobserved factors that were identified as random parameters. Male riders, riders over 50 years old, foreign riders, and accidents that occurred at night with inadequate lighting were identified as the primary factors that increased the risk of fatalities. This paper presents a clear policy recommendation aimed at organizations and identifies the relevant stakeholders, including the Department of Land Transport, traffic police, local government organizations, and academic groups.

Motorcyclist injury severity analysis: a comparison of Artificial Neural Networks and random parameter model with heterogeneity in means and variances

In Thailand, the motorcyclist mortality rate is steadily on the rise and remains a serious concern for highway administrators and burden on both economic and local people. Using motorcycle-crash data in Thailand from 2016 to 2019, this study empirically employed and compared the Artificial Neural Networks (ANN) model and random parameters binary probit model with heterogeneity in means and variances (RPBPHM) to explore the effects of a wide range of associated risk characteristics on the severity outcomes of the motorcyclist. Study results revealed that probabilities of injury or fatal crash increase for crashes that involve male riders, riding with pillion, speeding, improper overtaking, riders under influence of alcohol, fatigue riders, undivided road and so on. The probability of non-injury crash increases for crashes on main or frontage traffic lane, four-lane road, concrete road, during rain, involving collision with other motorcycles, rear-end crashes, sideswipe crashes, single-motorcycle crashes and crashes within urban areas. The RPBPHM models were found to outperform the ANN model (quadratic support vector machine) in all performance metrics. The findings could potentially assist policymaker, safety professionals, practitioners, trainers, government agencies or highway designers in future planning and serve as guidance for mitigation policies directed at safety improvement for motorcyclists.

Analysis of driver-injury severity: a comparison between speeding and non-speeding driving crash accounting for temporal and unobserved effects

This paper aims to investigate the differences between temporal stability of factors influencing driver-injury severities in crashes involving speeding and non-speeding driving using a six-year (2012-2017) crash data in Thailand. With two possible driver injury severity outcomes (no/minor and severe/fatal), random parameter binary logit models, that allow for heterogeneity in means and variances, were estimated to fully account for unobserved heterogeneities (i.e., allow crash-level factors to vary across crashes and to influence random parameter distribution). While most factors were unstable over time, speeding crash models result showed that stable factors decreasing probability of severe/fatal injury were restraint, van, passenger car, pickup truck, running-off-road on straight and hitting guardrail and mounting traffic island; whereas stable factor increasing probability of severe/fatal injury were central/eastern/southern regions. In non-speeding driving crash model, stable factors decreasing probability of severe/fatal injury were restraint, truck, and running-off-road on straight and hitting guardrail; whereas stable factors increasing probability of severe and fatal injury were under influence of alcohol and van. The findings of this research could potentially be utilized to improve highway safety and facilitate the development of more effective crash injury mitigation policies. Practical-related recommendation based on the results is also provided.

Empirical comparison of the effects of urban and rural crashes on motorcyclist injury severities: A correlated random parameters ordered probit approach with heterogeneity in means

In Thailand in 2016, more than 70% of all deaths due to road accidents were motorcyclist deaths. This study uses a correlated random parameters ordered probit model with heterogeneity in means (CRPOPHM) to obtain insight into differences in the significant factors determining the severity of motorcyclist injury between motorcycle crashes in urban and rural roadways, using data on motorcycle crashes in Thailand from 2016 to 2019. Using a rating system for injury severity level from minor injury to severe injury and to fatal injury, a wide range of potential risk factors are considered, including rider characteristics and actions, roadway characteristics, environmental and temporal characteristics, and crash characteristics. The findings indicate that, although some factors are significant in both urban and rural models, factors such as male rider, illegally overtaking, drowsiness, four-lane or wider highway, flush or depressed median, road on slope, weekend, nighttime with light, crash with van or minibus, and rear-ending or sideswiping crash, are significant only in the rural model, whereas the factors barrier median, occurring between 18:00 and 23:59, and striking a passenger car are statistically significant in only the urban model. These findings further suggests that difference in effect of unobserved characteristics could be seen in different crash locations, and splitting the model estimation between both location types could be done to develop effective guidance for policies to mitigate the severity of motorcyclist injuries. In addition, practical policy-related recommendations drawn from the results of the analysis are provided. With respect to methodology, the proposed CRPOPHM method outperforms lower-ordered models in terms of statistical fit and captures unobserved heterogeneity to a greater extent.

Hierarchical binary logit model to compare driver injury severity in single-vehicle crash based on age-groups

Most of the previous single-vehicle crash analysis studies ignored the effect of road-segments level at higher plan that could probably be unobserved heterogeneity and vary among crash-level factor from one road-segment to next and possibly could lead to a potential biased estimated result. This study developed a hierarchical binary logit model which have the ability to account for both unobserved heterogeneity and correlation within road-segment, to investigate and compare the impact of significant factors influencing fatal single-vehicle crash between young, mid-age and old driver model. A seven-years from 2011 to 2017 crash data, Department of Highway (DOH), Thailand were used in this study. The Intra-Class-Correlation values indicate the importance of road-segment level that 10.1%, 12.2% and 12.8% of the total variation were accounted by random effect from road-segment heterogeneity for young, mid-age and old driver model, respectively. The estimated result of this study shows that influence of alcohol and fatigue increase risk of fatal crash among young and old driver, seatbelt-usage reduce risk of being fatal among mid-age and old driver, roadside safety feature (guardrail) significantly reduce fatality risk among young and mid-age driver, and night time driving without light increase probability of fatal crash for mid-age driver. This study recommends the need to enforce the law on driver under influence of alcohol and seatbelt usage, educational campaign on driving, and installation of guardrail on curve road.

Applying hierarchical logistic models to compare urban and rural roadway modeling of severity of rear-end vehicular crashes

A rear-end crash is a widely studied type of road accident. The road area at the crash scene is a factor that significantly affects the crash severity from rear-end collisions. These road areas may be classified as urban or rural and evince obvious differences such as speed limits, number of intersections, vehicle types, etc. However, no study comparing rear-end crashes occurring in urban and rural areas has yet been conducted. Therefore, the present investigation focused on the comparison of diverse factors affecting the likelihood of rear-end crash severities in the two types of roadways. Additionally, hierarchical logistic models grounded in a spatial basis concept were applied by determining varying parameter estimations with regard to road segments. Additionally, the study compared coefficients with multilevel correlation model and those without multilevel correlation. Four models were established as a result. The data used for the study pertained to rear-end crashes occurring on Thai highways between 2011 and 2015. The results of the data analysis revealed that the model parameters for both urban and rural areas are in the same direction with the larger number of significant parameter values present in the rural rear-end crash model. The significant variables in both the urban and rural road segment models are the seat belt use, and the time of the incident. To conclude, the present study is useful because it provides another perspective of rear-end crashes to encourage policy makers to apply decisions that favor rules that assure safety.