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To reduce the damage caused by road accidents, researchers have applied different techniques to explore correlated factors and develop efficient prediction models. The main purpose of this study is to use one statistical and two nonparametric data mining techniques, namely, logistic regression (LR), classification and regression tree (CART), and random forest (RF), to compare their prediction capability, identify the significant variables (identified by LR) and important variables (identified by CART or RF) that are strongly correlated with road accident severity, and distinguish the variables that have significant positive influence on prediction performance. In this study, three prediction performance evaluation measures, accuracy, sensitivity and specificity, are used to find the best integrated method which consists of the most effective prediction model and the input variables that have higher positive influence on accuracy, sensitivity and specificity.

Motor Vehicular Accidents claim about 1.2 million lives and injure more than 10 million people annually worldwide. The injuries caused by MVAs can be analyzed based on the type of injury, injury severity score, Glasgow Coma Scale and required blood transfusion. Methodology: A total number of 190 patients were included in this retrospective study from January 01, 2010 to December 31, 2015. The study aimed to determine the correlation between the type of injuries and GCS, ISS, and blood transfusions in the patients suffering from Motor Vehicle Accidents, who were presented to the Emergency Department at the King Khalid Hospital. All the data of the patients fulfilling the inclusion criteria were collected from the database at medical records department of the hospital. Results: Majority of the patients were adults Saudi male. 68.9% of the patients did not sustain shock, and 75.8% of patients did not require a blood transfusion. Patients with head, neck, chest, abdominal, internal organ, pelvic or spinal injuries conferred a statistically significant higher mean ISS. Patients with abdominal or internal organ injuries had a statistically significant higher mean units of blood transfused. GCS was seen to be lower in the head, neck, chest, abdominal, internal organ, spinal and other injuries. Conclusions: The study documents a significant correlation between the type of injury and GCS, ISS, and blood transfusion in victims of road traffic accident. Emergency physician and the caregivers should be more careful about the injuries associated with lower GCS. Patients sustaining injuries of certain parts related to high ISS (i.e., head, chest, abdominal, internal organ, pelvic) should be addressed on priority basis.

Background Road traffic incidents often have sudden and life-changing effects, including reduced productivity, and long-term dependence in daily life. Addressing long-term consequences (LTC) is therefore a key challenge in creating a resilient road transport system. The EU project ProtAct-Us aims to improve the collection of data on physical and cognitive outcomes, and to develop countermeasures and assessment tools that consider accident and injury statistics, including LTCs. Methods To identify use cases that frequently result in LTCs, an analysis was carried out using accident data from the MHH Accident Research Unit (295 cases) and the EU-funded REHABIL-AID project (60 cases). Both databases contain general accident information recorded at the time of the incident, as well as follow-up data on LTCs. This includes information related to pain, reduced mobility, physical, psychological, or social impairments. Results For different types of road users different factors result in higher rates of long-term consequences. For example, car occupants with LTC are often in frontal and rear—end impact scenarios with another car, having injured the thorax, head, neck and lower extremities. While cyclists with long-term consequences are often in single-vehicle accidents or in collisions at junctions with cars with frontal or front-lateral impacts. Here injuries to the upper or lower extremities were most common. Conclusion The analysis of accident data revealed that certain accident types and injured body regions are more likely to lead to long-term consequences. These are not always linked to injury severity and can also psychologically affect uninjured individuals. Based on this information the development of tools and measures will be conducted to reduce such outcomes. Key messages Persons with long-term consequences have specific accident characteristics that differ from those of severe injuries, and they are specific to the different types of road users. Key messages • Persons with long-term consequences have specific accident characteristics that differ from those of severe injuries. • Long-term consequences are specific to the different types of road users. Topic Long-term consequences, traffic accidents, injury outcome.

To undertake a reliable analysis of injury severity in road traffic accidents, a complete understanding of important attributes is essential. As a result of the shift from traditional statistical parametric procedures to computer-aided methods, machine learning approaches have become an important aspect in predicting the severity of road traffic injuries. The paper presents a hybrid feature selection-based machine learning classification approach for detecting significant attributes and predicting injury severity in single and multiple-vehicle accidents. To begin, we employed a Random Forests (RF) classifier in conjunction with an intrinsic wrapper-based feature selection approach called the Boruta Algorithm (BA) to find the relevant important attributes that determine injury severity. The influential attributes were then fed into a set of four classifiers to accurately predict injury severity (Naive Bayes (NB), K-Nearest Neighbor (K-NN), Binary Logistic Regression (BLR), and Extreme Gradient Boosting (XGBoost)). According to BA’s experimental investigation, the vehicle type was the most influential factor, followed by the month of the year, the driver’s age, and the alignment of the road segment. The driver’s gender, the presence of a median, and the presence of a shoulder were all found to be unimportant. According to classifier performance measures, XGBoost surpasses the other classifiers in terms of prediction performance. Using the specified attributes, the accuracy, Cohen’s Kappa, F1-Measure, and AUC-ROC values of the XGBoost were 82.10%, 0.607, 0.776, and 0.880 for single vehicle accidents and 79.52%, 0.569, 0.752, and 0.86 for multiple-vehicle accidents, respectively.

The prediction of traffic accident severity is essential for traffic safety management and control. To achieve high prediction accuracy and model interpretability, we propose a hybrid model that integrates random forest (RF) and Bayesian optimization (BO). In the proposed model, BO-RF, RF is adopted as a basic predictive model and BO is used to tune the parameters of RF. Experimental results show that BO-RF achieves higher accuracy than conventional algorithms. Moreover, BO-RF provides interpretable results by relative importance and a partial dependence plot. We can identify important influential factors for traffic accident severity by relative importance. Further, we can investigate how the influential factors affect traffic accident severity by the partial dependence plot. These results provide insights to mitigate the severity of traffic accident consequences and contribute to the sustainable development of transportation.

Motorcycle crash severity is under-researched in Ghana. Thus, the probable risk factors and association between these factors and motorcycle crash severity outcomes is not known. Traditional statistical models have intrinsic assumptions and pre-defined correlations that, if flouted, can generate inaccurate results. In this study, machine learning based algorithms were employed to predict and classify motorcycle crash severity. Machine learning based techniques are non-parametric models without the presumption of relationships between endogenous and exogenous variables. The main aim of this research is to evaluate and compare different approaches to modeling motorcycle crash severity as well as investigating the effect of risk factors on the injury outcomes of motorcycle crashes. Motorcycle crash dataset between 2011 and 2015 was extracted from the National Road Traffic Crash Database at the Building and Road Research Institute (BRRI) in Ghana. The dataset was classified into four injury severity categories: fatal, hospitalized, injured, and damage-only. Three machine learning based models were developed: J48 Decision Tree Classifier, Random Forest (RF) and Instance-Based learning with parameter k (IBk) were employed to model the severity of injury in a motorcycle crash. These machine learning algorithms were validated using 10-fold cross-validation technique. The three machine learning based algorithms were compared with one another and the statistical model: multinomial logit model (MNLM). Also, the relative importance analysis of the attribute was conducted to determine the impact of these attributes on injury severity outcomes. The results of the study reveal that the predictions of machine learning algorithms are superior to the MNLM in accuracy and effectiveness, and the RF-based algorithms show the overall best agreement with the experimental data out of the three machine learning algorithms, for its global optimization and extrapolation ability. Location type, time of the crash, settlement type, collision partner, collision type, road separation, road surface type, the day of the week, and road shoulder condition were found as the critical determinants of motorcycle crash injury severity.

The paper investigates the dependences between levels of severity of road traffic accidents, accounting at the same time for spatial and temporal correlations. The study analyses road traffic accidents data at ward level in England over the period 2005–2013. We include in our model multivariate spatially structured and unstructured effects to capture the dependences between severities, within a Bayesian hierarchical formulation. We also include a temporal component to capture the time effects and we carry out an extensive model comparison. The results show important associations in both spatially structured and unstructured effects between severities, and a downward temporal trend is observed for low and high levels of severity. Maps of posterior accident rates indicate elevated risk within big cities for accidents of low severity and in suburban areas in the north and on the southern coast of England for accidents of high severity. The posterior probability of extreme rates is used to suggest the presence of hot spots in a public health perspective.

Background Road traffic crashes represent a major public health concern, so it is of significant importance to understand the factors associated with the increase in injury severity of its interveners when involved in a road crash. Determining such factors is essential to help decision-making in road safety management, improving road safety, and reducing the severity of future crashes. Artificial Intelligence is probably the most important topic of research nowadays. Machine learning is a very prominent and successful approach in modern AI and is used in the work to predict crash injury severity. Methods For this study, all police-reported road accidents involving at least one injured passenger car occupant that occurred in Portugal over twelve years, from 2010 to 2022, were considered. The information present in the data (a total of 117 variables) can be divided into 3 parts: accident information (50 variables), occupants’ information (43 variables), and a third part related to the other driver involved (24 variables), when this exists. The size of the crash database is very large containing about 6000 fatalities, 20000 severe injuries and 600000 slight injuries. A full exploratory analysis of the data was performed for all variables in the dataset. To begin with, the exploratory analysis was performed on the original dataset. Afterwards, the continuous variables were discretized, and considering the previous exploration analysis, some classes were grouped within the different variables. A new exploration analysis was performed on this new discretized and regrouped dataset. Results Different machine learning methods are investigated and discussed in this study, namely Decision Tree (DT), Ordered Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) . To compare the models’ performance the following metrics were used: sensitivity, specificity, precision. Key messages • AI with machine learning methods provides a suitable framework to build a proper predictive model, allowing researchers and practitioners to evaluate more accurately the risk factors of crashes. • Car accidents account for the majority of road victims in the world, and tools that can predict injuries are very important for taking effective prevention measures. Topic Injury prediction, Machine Learning, Traffic crashes.

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.

Every year, millions die in road accidents globally, imposing significant economic and humanitarian costs. While road traffic accidents are a major health concern, many developing countries, including Ethiopia, struggle to address this issue effectively. Ethiopia ranks second in East Africa for severe road traffic accidents, highlighting the need for improved injury reduction strategies. This study introduces a novel approach by chronologically identifying and prioritizing accident black spots in the studied area, Ethiopia. This method provides a valuable tool for transportation authorities and traffic police to target high-risk areas for immediate intervention. Focusing on the Dembecha-Injibara highway segment, the study employs both descriptive and inferential analyses, using the Zegeer method to calculate accident rates. It also uses factors of weight contributing to road traffic accidents and their severity to rank accident-prone areas. The findings reveal that areas near Finote Selam, Banja, and Burie are highly prone to severe accidents, with specific accident frequencies and priority values identified. Recommendations are offered to address these high-risk areas and mitigate severe traffic accidents in the study region.