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PurposeThis work aims to study factors, such as driver characteristics, environmental conditions, and vehicle characteristics, that affect different crash types with a special focus on distraction parameters. For this purpose, distraction factors are divided into five groups: cellphone usage, cognitive distractions, passengers distracting the driver, outside events attracting the driver’s attention, and in-vehicle activities.MethodsTaking the crashes that occurred in the USA into account, the crash types are divided into two main groups, single-vehicle crashes and two-vehicle crashes. Since there were different crash types (alternatives) in the dataset and the probable correlation in the unobserved error term, the Nested Logit model is developed.ResultsThe results of model illustrate that all of the aforementioned distraction-related factors increase the probability of run-off-road crashes, collision with a fixed object, and rear-end crashes. Cognitive distraction increases the probability of collision with a pedestrian. Distractions caused by passengers or out-of-vehicle events increase the probability of sideswipe crashes.ConclusionBy examining how a factor affects multiple crash type outcomes, it is possible to devise countermeasures, improvements to roadway geometry, and traffic control strategies, while minimizing unintended consequences. The results should be of value in the design of educational programs and propose road safety improvement techniques.

Road traffic crashes remain a significant concern for public safety and transport systems, and addressing their adverse effects forms a foundation for safety planning and policy development. This study presents a hierarchical hybrid framework that combines signal decomposition techniques, including Variational Mode Decomposition (VMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), with deep learning models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), and WaveNet. The framework uses daily vehicle–vehicle crash data from Yinzhou District, Ningbo City. Among all configurations, the VMD-GRU model produced the best results, with MAE = 2.960, RMSE = 3.750, and R 2  = 0.984, which reflects its ability to capture complex temporal structures. In contrast, the CEEMDAN-TCN model showed the weakest performance, with MAE = 14.559, RMSE = 19.481, and R 2  = 0.609. Furthermore, the Wilcoxon signed-rank test confirmed that the performance of VMD-GRU differs significantly from all other models at the 5% significance level. Residual analysis indicates that VMD-GRU maintains low prediction errors and aligns more closely with actual vehicle–vehicle crash values over time. This framework provides traffic authorities with a tool to identify shifts in crash patterns, make timely policy decisions, and allocate safety resources with greater precision.

The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

This study aimed to determine how built environments affect pedestrian–vehicle collisions. The study examined pedestrian–vehicular crashes that occurred between 2013 and 2015 in Seoul, Korea, by comparing and analyzing different effects of the built environment on pedestrian–vehicle crashes. Specifically, the study analyzed built environment attributes, land use environment, housing types, road environment, and traffic characteristics to determine how these factors affect the severity of pedestrian injury. The results of the statistical analysis appear to infer that the built environment attributes had dissimilar impacts on pedestrian collisions, depending on the injury severity. In general, both incapacitating and non-incapacitating injuries appear to be more likely to be caused by the built environment than fatal and possible injuries. These results highlight the need to consider injury severity when implementing more effective interventions and strategies for ensuring pedestrian safety. However, because of the small sample size, an expanded research project regarding this issue should be considered, as it would contribute to the development and implementation of effective policies and interventions for pedestrian safety in Korea. This study therefore offers practical information regarding the development of such an expanded study to inform future traffic safety policies in Seoul to establish a “safe walking city.”

Abstract With the iteration of vehicle brand design, the applicability of the current Mobile Progressive Deformable Barrier (MPDB) scheme has declined. The Advanced Chinese Mobile Progressive Deformable Barrier (AC-MPDB) is introduced, which incorporates segmented Z-directional design and increased mass distribution to better align with the Chinese vehicle front-end structure. This study utilizes Camry and Rogue models to design an MPDB-to-vehicle and a vehicle-to-vehicle simulation matrix. The compatibility of the barrier schemes, Euro-NCAP MPDB (E-MPDB) and AC-MPDB, is evaluated using energy equivalent speed, occupant load criteria, peak force, and acceleration. The simulation results indicate that, compared to E-MPDB, the AC-MPDB barrier scheme exhibits excessive stiffness for medium-sized sedans with smaller mass but demonstrates better compatibility with SUVs. Further analysis of the target vehicle's longitudinal beam deformation animation and pedal intrusion reveals that AC-MPDB achieves superior vehicle reproducibility, particularly for SUVs.

Vehicle safety systems have substantially decreased motor vehicle crash-related injuries and fatalities, but injuries to the lumbar spine still have been reported. Experimental and computational analyses of upright and, particularly, reclined occupants in frontal crashes have shown that the lumbar spine can be subjected to simultaneous and out-of-phase combined axial compression and flexion loading. Lumbar spine failure tolerance in combined compression-flexion has not been widely explored in the literature. Therefore, the goal of this study was to measure the failure tolerance of the lumbar spine in combined compression and flexion. Forty lumbar spine segments with three vertebrae (one unconstrained) and two intervertebral discs (both unconstrained) were pre-loaded with axial compression (2200N, 3300N, or 4500N) and then subjected to rotation-controlled dynamic flexion bending until failure. Clinically relevant middle vertebra fractures were observed in twenty-one of the specimens, including compression and burst fractures. The remaining nineteen specimens experienced failure at the potting-grip interface. Failure tolerance varied within the sample and were categorized by the appropriate data censoring, with clinically relevant middle vertebrae fractures characterized as uncensored or left-censored and potting-grip fractures characterized as right-censored. Average failure force and moment were 3290N (range: 1580N to 5042N) and 51Nm (range: 0Nm to 156 Nm) for uncensored data, 3686N (range: 3145N to 4112N) and 0Nm for left-censored data, and 3470N (range: 2138N to 5062N) and 101Nm (range: 27Nm to 182Nm) for right-censored data. These data can be used to develop and improve injury prediction tools for lumbar spine fractures and further research in future safety systems.

Understanding the major determinants of crash fatalities continues to be an important topic of investigation for safety researchers. Regression models using a vast number of explanatory variables are often used which result in a huge array of specifications. Results often vary among studies based on size of estimated coefficients and significance levels. To address this, we explore both significance and model sturdiness in regression models using Leamer’s s-values. This Bayesian technique allows us to address estimation uncertainty and model ambiguity over all possible subset regressions so as to evaluate the effect of key variables which we focus on as contributors to crash fatalities. These include cell phone use, fleet modernization, suicidal behavior, alcohol use, and speed limits.

Animal–vehicle collisions (AVCs) can result in devastating injuries to both humans and animals. Despite significant advances in crash prediction models, there is still a significant gap when it comes to injury severity prediction models in AVCs, especially concerning small animals. It is no secret that large mammals can pose a significant threat to road safety; however, researchers tend to overlook the impact of domestic and small animals wandering along the roads. In this study, STATS19 road safety data was used containing any type of live animal, and a radial basis function (RBF) model was used to predict different severities of injury regardless of whether the animal was hit, or not. As a means of better understanding the factors contributing to severities, regression trees were used to identify and retain only the most useful predictors, removing the less useful ones. A comparison was made between the performance of the trees across a range of severity classes, and the model-fitting results were discussed. Initially, the study was unable to generate satisfactory predictions, but the optimization of the key predictors and the combination of severity classes significantly improved their accuracy. Research findings revealed factors contributing to the severities, which were discussed accordingly. Particular attention was drawn to the pressing safety issue posed by animals crossing A-class single carriageways in rural site clusters. Animals being present on those carriageways without direct vehicular contact significantly contributed to the severity of the injuries sustained. Although the majority of contributing factors were related to human behavior, no evidence of road safety education, training, or publicity interventions specifically targeting AVCs was found in the literature.

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000–10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Motor vehicle crashes remain a leading cause of injury and death in adolescents, with teen drivers three times more likely to be in a fatal crash when compared to adults. One potential contributing risk factor is the ongoing development of executive functioning with maturation of the frontal lobe through adolescence and into early adulthood. Atypical development resulting in poor or impaired executive functioning (as in Attention-Deficit/Hyperactivity Disorder) has been associated with risky driving and crash outcomes. However, executive function broadly encompasses a number of capacities and domains (e.g., working memory, inhibition, set-shifting). In this review, we examine the role of various executive function sub-processes in adolescent driver behavior and crash rates. We summarize the state of methods for measuring executive control and driving outcomes and highlight the great heterogeneity in tools with seemingly contradictory findings. Lastly, we offer some suggestions for improved methods and practical ways to compensate for the effects of poor executive function (such as in-vehicle assisted driving devices). Given the key role that executive function plays in safe driving, this review points to an urgent need for systematic research to inform development of more effective training and interventions for safe driving among adolescents.