Severity Of Road Traffic Accidents Research Articles

Modeling Severity of Road Traffic Accident in Nigeria using Artificial Neural Network

In this study, an Artificial Neural Network (ANN) was used to model injury and fatality index in Nigeria with the aim to determine the effects of the number of GSM subscription (NGS) on the injury and fatality index in the country. Fifty-seven-year data from 1960-2016 comprising of Gross Domestic Product (GDP) per capita, population, NGS, the total number of traffic accidents, number of fatality and injury per year were used for developing the model. The result of the ANN implies that adding the NGS to the model has increased the model performance in both training and testing with a determination coefficient increasing by 18.7% and 2.5% in testing for fatality and injury index respectively. Comparing the performance of the ANN models and regression analysis shows the superiority of the ANN technique over the regression analysis for both injury and fatality index models. The goodness of fit of the model was further checked using t-test at 5% level of significance and the result proved the ANN approach as a powerful tool for modeling the severity of road traffic accident. Strict enforcement against the use of phone while driving will help reduce the accident severity caused as a result of phone usage while on wheels.

Using statistical modelling to analyze risk factors for severe and fatal road traffic accidents

Road traffic accidents (RTAs) are still frequent events in the UK with severe/fatal RTAs leading to significant morbidity and mortality. Therefore, this study aimed to explore clusters of risk factors which affect the severity of RTAs in the UK. A retrospective analysis of 76,334 driver-level records between 2005 and 2014 was conducted. Two methods were used: ‘partially constrained generalized logistic regression models’ and ‘classification and regression tree’ (CART) analysis in order to identify individual factors and combinations of risk factors relating to severity of accidents. Several established risk factors were confirmed which contribute to the severity of RTAs. Specific combinations of factors were identified which were more likely to lead to fatal accidents: the involvement of one older person, one or no cycles involved, speed limit over 40 mph in culmination with several other factors. This study reaffirmed risk factors relating to severity of RTAs in the UK, but also established combinations of risk factors which led to the most severe outcomes allowing for targeting of accident-prevention measures. In addition, this study demonstrates the use of CART analysis which can be used in wider public health evaluations where multiple risk factors are at play.

Road traffic accident severity analysis: A census-based study in China

Background: In China, despite the decrease in average road traffic fatalities per capita, the fatality rate and injury rate have been increasing until 2015. Purpose: This study aims to analyze the road traffic accident severity in China from a macro viewpoint and various aspects and illuminate several key causal factors. From these analyses, we propose possible countermeasures to reduce accident severity. Method: The severity of traffic accidents is measured by human damage (HD) and case fatality rate (CFR). Different categorizations of national road traffic census data are analyzed to evaluate the severity of different types of accidents and further to demonstrate the key factors that contribute to the increase in accident severity. Regional data from selected major municipalities and provinces are also compared with national traffic census data to verify data consistency. Results: From 2000 to 2016, the overall CFR and HD of road accidents in China have increased by 19.0% and 63.7%, respectively. In 2016, CFR of freight vehicles is 33.5% higher than average; late-night accidents are more fatal than those that occur at other periods. The speeding issue is severely becoming worse. In 2000, its CFR is only 5.3% higher than average, while in 2016, the number is 42.0%. Conclusion and practical implementation: A growing trend of accident severity was found to be contrasting to the decline of road traffic accidents. From the analysis of casual factors, it was confirmed that the release way of the impact energy and the protection worn by the victims are key variables contributing to the severity of road traffic accidents.

A classification and recognition model for the severity of road traffic accident

A classification and recognition method for the severity of road traffic accident based on rough set theory and support vector machine was proposed in this article. Rough set theory was used to calculate the importance of attributes in human, vehicle, road, environment, and accident. On the basis of importance ranking, the factors affecting the severity of accident were extracted. Then, with the general accident and major accident as two classification labels, the classification and recognition model of the severity of road traffic accident was established by using support vector machine. The results show that the model could improve the recognition accuracy and reduce the computational workload. Moreover, it has the good ability in classification and recognition as well as generalization compared with the model using support vector machine alone.

Geographic analysis of road accident severity index in Nigeria

ABSTRACTBefore 2030, deaths from road traffic accidents (RTAs) will surpass cerebrovascular disease, tuberculosis, and HIV/AIDS. Yet, there is little knowledge on the geographic distribution of RTA severity in Nigeria. Accident Severity Index is the proportion of deaths that result from a road accident. This study analysed the geographic pattern of RTA severity based on the data retrieved from Federal Road Safety Corps (FRSC). The study predicted a two-year data from a historic road accident data using exponential smoothing technique. To determine spatial autocorrelation, global and local indicators of spatial association were implemented in a geographic information system. Results show significant clusters of high RTA severity among states in the northeast and the northwest of Nigeria. Hence, the findings are discussed from two perspectives: Road traffic law compliance and poor emergency response. Conclusion, the severity of RTA is high in the northern states of Nigeria, hence, RTA remains a public health concern.

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%–16%) more accuracy than without using clustering, while the percentage split achieves (2%–5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

A Space–Time Multivariate Bayesian Model to Analyse Road Traffic Accidents by Severity

SummaryThe 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.

Risk factors for road traffic accidents with head injury in Aligarh

Background: Road traffic accidents (RTAs) have become a growing public health and development problem. RTA is the most common cause of head injury in 15–45 years of age group. There are number of host and environmental factors which determine the occurrence of RTAs. Objective: To study the risk factors related to RTA with head injury and to study the relationship between risk factors and severity of head injury. Materials and Methods: A hospital-based cross-sectional study was carried out in casualty and neurosurgery ward, JNMCH, AMU, Aligarh. All patients of RTAs with head injury in the age group of 15–45 years who have registered themselves in casualty were included in the study. Purposive sampling was used for patient inclusion in the study. Study period was 1 year from August 2010 to July 2011. Data were analyzed with SPSS 20.0 and χ2-test was applied to assess the relationship of severity of head injury with risk factors. Result: Significant deterioration in severity of RTAs was noted whenever road accident took place over inadequate roads or in poor visibility. Vehicles functioning inadequately at the time of accident also led to significant increase in proportion of moderately and severely injured patients. Excessive speed noted in one-fifth of accidents and consumption of alcohol prior to accident also led to significant increase in moderate-to-severe accidents. Conclusion: Road conditions and poor visibility are identified environmental risk factors for RTAs. Vehicular status along with rising speed and alcohol intoxication are important risk factors.

Formalization of characteristics of interaction of vehicles and pedestrians in conflict areas in the crossroad territory

Based on the analysis of statistics of road traffic accidents in Ukraine at unregulated crossroads, the task on improving the methods for assessing the safety of unregulated crossroads taking into account qualitative accident rate indicators that characterize the severity of road traffic accidents was set in the paper. According to the analysis of the interaction between vehicles and pedestrians, the concepts of conflict area of interaction between traffic and pedestrian flows in the crossroad territory were introduced. Five specific interaction areas were identified. For the corresponding conflict areas in the crossroad territory, formalization of the specified interaction between vehicles and pedestrians at the level of the characteristics of the interaction process in time within the day was held. It was proposed to reflect the interaction using the law of conservation of energy. In this regard, energy characteristics of interaction between vehicles and pedestrians in the corresponding conflict areas were synthesized by the proposed five types of interaction between traffic and pedestrian flows.

Ensuring Operators-Drivers Transport of Agricultural Machinery Safety in the Context of Sustainable Development

Abstract The number and severity of road traffic accidents in the Russian Federation is not reduced from year to year. It is established that one of the common causes of traffic accidents is sleep at the wheel, it is especially typical for operators-drivers of transport agricultural machinery. According to the experimental data were derived mathematical dependences of operator reaction time of the driver at the beginning and end of the work shift by his age, which proved that with increasing age operator reaction time in an emergency increases

Femoral fractures in road traffic accidents

The purpose of this study is to describe and analyze the severity of road traffic accidents in which a femoral fracture was identified, comparatively, in ante and post-mortem cases. For this purpose we have conducted a retrospective study, using the archives of the National Institute of Legal Medicine from which were selected 82 consecutive cases of clinical medical- legal expertises and 100 cases of autopsy (AR) reports for road traffic accidents. Femoral fractures were identified in 34 cases, of which 12 were in clinical cases and 22 in necropsy cases. Femoral shaft fractures were significantly associated with a lethal outcome, whilst fractures of the proximal extremity were significantly associated with a non-fatal outcome. Femoral fractures have a tendency to occur at younger ages compared with the cases in which such a lesion was not identified. Conclusions. Femoral shaft fractures were more frequent in younger males and fractures of the proximal extremity in older female patients. The presence of a femoral fracture (especially if it is involved the femoral shaft) is associated with an increased risk for an

Speeding drivers’ attitudes and perceptions of speed cameras in rural England

There is evidence that excessive speed leads to an increased frequency and severity of road traffic accidents, but it is not clear how speeds may be reduced. To increase understanding of why drivers exceed the speed limits, the views of a sample of road users who had been prosecuted for exceeding the speed limit in the rural county of Norfolk England were sought. Respondents were categorised into a four group driver typology comprising conformers (those who report they never exceed limits) deterred drivers (those put off speeding by the presence of cameras), manipulators (those who slow only at camera locations) and defiers (those who exceed limits regardless of cameras), and the consistency of opinions was compared between the groups. Speeding was perceived as widespread and normal, and many drivers resented camera enforcement. Indeed, some respondents considered that cameras in themselves caused dangerous driving. For many drivers, the prosecution experience resulted in distress, anger and anti-camera sentiments, predominantly because they expressed the belief that they were more skilled than other drivers. It was also apparent that many respondents displayed a lack of awareness of the link between speed and collisions. Conformers were the least likely to state that the prosecution had deterred them from further speeding behaviour, possibly because they perceived themselves as already law-abiding. The deterred drivers were most likely to express intentions to avoid further speeding and their speeding incident was found to be most likely to be accidental. Manipulators and defiers tended to report that they had deliberately chosen to infringe the speed limits. Manipulators often acknowledged that their style of driving was dangerous; however, they failed to link this to their own behaviour. It was evident that many defiers and manipulators did not perceive speeding as a serious traffic law violation. Possible implications for road safety initiatives are discussed and recommendations are given for specifically targeting different driver types.

Phillip Maloney Trauma Lecture: Pediatric Maxillofacial Trauma

In the first issue of The Journal of Oral Surgery (1943), a paper entitled “Fractures of the Facial Bones in Children” by C. W. Waldron, S. G. Balkin, and R. G. Peterson was published. In the ensuing years, there have been significant advances in prevention, diagnosis, and treatment of craniomaxillofacial (CMF) injuries. Legislation (speed limits, alcohol restriction, use of helmets, shoulder and seat restraints), improved road construction, and vehicle safety modifications (safety glass, padded dash boards, stronger frames, collapsible steering columns, airbags) have led to a decrease in the incidence and severity of road traffic accidents (RTA) in the United States. There has also been a corresponding decrease in the incidence and severity of CMF injuries, particularly in children below the age of ten years.

Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea

Increasing amount of road traffic in 1990s has drawn much attention in Korea due to its influence on safety problems. Various types of data analyses are done in order to analyze the relationship between the severity of road traffic accident and driving environmental factors based on traffic accident records. Accurate results of such accident data analysis can provide crucial information for road accident prevention policy. In this paper, we use various algorithms to improve the accuracy of individual classifiers for two categories of severity of road traffic accident. Individual classifiers used are neural network and decision tree. Mainly three different approaches are applied: classifier fusion based on the Dempster–Shafer algorithm, the Bayesian procedure and logistic model; data ensemble fusion based on arcing and bagging; and clustering based on the k-means algorithm. Our empirical study results indicate that a clustering based classification algorithm works best for road traffic accident classification in Korea.

Pattern recognition for road traffic accident severity in Korea

An increasing number of road traffic accidents (RTA) in Korea has emerged as being harmful both for the economy and for safety. An accurately estimated classification model for several severity types of RTA as a function of related factors provides crucial information for the prevention of potential accidents. Here, three data-mining techniques (neural network, logistic regression, decision tree) are used to select a set of influential factors and to build up classification models for accident severity. The three approaches are then compared in terms of classification accuracy. The finding is that accuracy does not differ significantly for each model and that the protective device is the most important factor in the accident severity variation.

Pattern recognition for road traffic accident severity in Korea

An increasing number of road traffic accidents (RTA) in Korea has emerged as being harmful both for the economy and for safety. An accurately estimated classification model for several severity types of RTA as a function of related factors provides crucial information for the prevention of potential accidents. Here, three data-mining techniques (neural network, logistic regression, decision tree) are used to select a set of influential factors and to build up classification models for accident severity. The three approaches are then compared in terms of classification accuracy. The finding is that accuracy does not differ significantly for each model and that the protective device is the most important factor in the accident severity variation.