Rate Of Road Traffic Accidents Research Articles

Epidemic characteristics and trend analysis of major injuries deaths among children and adolescents in Jiangsu Province from 2012 to 2021

Objective: To understand the situation and epidemic characteristics of injury deaths among children aged 5 to 24 years in Jiangsu Province from 2012 to 2021 and the trend of annual changes. Methods: The main injury mortality data of children and adolescents was collected, and the crude and standardized mortality rates of road traffic accidents, drowning, suicide, and accidental falls among children and adolescents over a decade and the annual average percentage of change (AAPC) were calculated. The main injury mortality characteristics and trends of children and adolescents of different age groups and genders were analyzed. Results: The total number of injury deaths among 5 to 24 adolescents in Jiangsu Province was 16 052, with a standardized mortality rate of 9.58/100 000. There was no significant trend in the overall standardized mortality rate of injuries (AAPC=-3.450%, P=0.055). The standardized mortality rate of road traffic injuries among children and adolescents showed a decreasing trend over the past decade, with statistical significance (AAPC=-9.406%, P<0.001). The standardized suicide mortality rate showed an upward trend over the past decade, with statistical significance (AAPC=9.000%, P=0.001). The overall injury mortality rate showed an upward trend with age. Suicide rates in males and females were on the rise and both have statistical significance (AAPC=9.420% and AAPC=9.607%, both P<0.05). The standardized mortality rates of female traffic accidents, drowning, and male traffic accidents showed a decreasing trend and were statistically significant (AAPC for female traffic accidents=-7.364%, AAPC for female drowning=-5.352%, and AAPC for male traffic accidents=-10.242%, all P<0.05). The standardized mortality rate of urban and rural traffic accidents showed a decreasing trend and was statistically significant(AAPC=-7.899% and AAPC=-9.421%, both P<0.001). The standardized suicide mortality rate showed an upward trend and statistical significance (AAPC=11.009% and AAPC=7.528%, both P<0.05). Conclusions: The overall injury situation of children and adolescents in Jiangsu Province improved in the past decade from 2012 to 2021, but the suicide mortality rate was on the rise. It is necessary to focus on the mental health issues of this age group and to strengthen the prevention and control of suicide among children and adolescents, in Jiangsu.

Application of a time series to analyse the evaluation of road traffic accidents in Slovakia

Road traffic accidents represent an important part of the road traffic system. In many cases, they may lead not only to damage to vehicles and property, but also serious injuries or even death of road traffic participants. The prevalence of road traffic accidents and the related fatality rates in a country is a significant indicator of the maturity of that country and its inhabitants. The purpose of this article was to review the prevalence of road traffic accidents and fatality rates in the Slovak Republic over the period from 2009 to 2022. The analysis was conducted by applying basic statistical methods and a time series analysis (exponential smoothing method – ETS). The ETS is a method used for forecasting a time series univariate. The focus of this method is on three time series components (error E, trend T and seasonal S), while defining how the individual components interact. The results of the analysis indicated a positive, i.e. a falling trend in road traffic accident rates, according to the numbers of persons killed/injured in road traffic accidents in Slovakia. Compared to 2009, in 2020 there was a decrease in the number of road traffic accidents by almost 54%. In 2022, the number of fatalities decreased by almost 30% compared to those in 2009. The modelling also involved a forecast of the number of persons killed/injured in road traffic accidents for a period of 5 years.

Increasing the environmental safety of the motor transport complex by optimizing traffic on emergency road sections

The article presents the results of using microscopic simulation modeling to optimize traffic on emergency sections of highways. It is shown that these areas are characterized not only by high rates of road traffic accidents, but also by large volumes of pollutant emissions from vehicle exhaust gases due to high traffic intensity, congestion, and imperfect road infrastructure. Based on the results of field studies, a simulation model of an emergency-hazardous section of the urban road network was developed, the adequacy of the model was proven, and virtual experiments were conducted. An option has been proposed to improve the environmental and traffic situation on the site by improving the technical means of organizing traffic. When implemented, because of an increase in throughput, there is a noticeable reduction in mass emissions of pollutants and an increase in the safety factor.

Factors Associated with Road Traffic Accidents in Kathmandu, Nepal: A Cross-Sectional Study

Background: Road traffic accidents (RTAs) are a leading and disproportionately prevalent cause of mortality and disability, particularly burdening Nepal, primarily within the Kathmandu Valley.&#x0D; Methods: This cross-sectional study was conducted between March 22nd and April 24th, 2021, involving 185 victims admitted to the emergency department of a private hospital. Respondents were selected using purposive sampling, and data were collected through face-to-face interviews. Socio-demographic characteristics, human factors, and environmental factors data were analyzed using descriptive statistics, and all statistical tests were conducted using SPSS version 20.&#x0D; Results: Among the 185 RTA victims, the majority (36.8%) fell within the 21-30 age groups. Male victims (55.7%) outnumbered females (44.3%). Students (36.8%) experienced the highest accident rates, with most incidents (21.1%) occurring on Saturdays and 38.4% taking place between 12:00pm and 6:00pm.&#x0D; Human Factors and RTA: Of the total victims, 80 (43.2%) were drivers, with 43.75% of them reporting the use of emergency brakes. Furthermore, 77.61% indicated that accidents were caused by pedestrians not using zebra crossings when crossing the road.&#x0D; Environmental Factors and RTA: Rainy weather was associated with the highest number of accidents, accounting for 36.2% of cases. Additionally, narrow routes (28.6%) and slippery roads (25.9%) contributed to a significant number of accidents.&#x0D; Conclusion: Nepal faces a high rate of road traffic accidents, with critical factors including victim age, vehicle types, speed, road and weather conditions, and traffic rule violations. Authorities must consider these factors to effectively control and prevent RTAs.

Application of an Effective Hierarchical Deep-Learning-Based Object Detection Model Integrated with Image-Processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes

Against the backdrop of rising road traffic accident rates, measures to prevent road traffic accidents have always been a pressing issue in Taiwan. Road traffic accidents are mostly caused by speeding and roadway obstacles, especially in the form of rockfalls, potholes, and car crashes (involving damaged cars and overturned cars). To address this, it was necessary to design a real-time detection system that could detect speed limit signs, rockfalls, potholes, and car crashes, which would alert drivers to make timely decisions in the event of an emergency, thereby preventing secondary car crashes. This system would also be useful for alerting the relevant authorities, enabling a rapid response to the situation. In this study, a hierarchical deep-learning-based object detection model is proposed based on You Only Look Once v7 (YOLOv7) and mask region-based convolutional neural network (Mask R-CNN) algorithms. In the first level, YOLOv7 identifies speed limit signs and rockfalls, potholes, and car crashes. In the second level, Mask R-CNN subdivides the speed limit signs into nine categories (30, 40, 50, 60, 70, 80, 90, 100, and 110 km/h). The images used in this study consisted of screen captures of dashcam footage as well as images obtained from the Tsinghua-Tencent 100K dataset, Google Street View, and Google Images searches. During model training, we employed Gaussian noise and image rotation to simulate poor weather conditions as well as obscured, slanted, or twisted objects. Canny edge detection was used to enhance the contours of the detected objects and accentuate their features. The combined use of these image-processing techniques effectively increased the quantity and variety of images in the training set. During model testing, we evaluated the model’s performance based on its mean average precision (mAP). The experimental results showed that the mAP of our proposed model was 8.6 percentage points higher than that of the YOLOv7 model—a significant improvement in the overall accuracy of the model. In addition, we tested the model using videos showing different scenarios that had not been used in the training process, finding the model to have a rapid response time and a lower overall mean error rate. To summarize, the proposed model is a good candidate for road safety detection.

Adult ADHD in the Republic of Ireland: the evolving response.

Historically, attention-deficit hyperactivity disorder (ADHD) was conceptualised as a disorder of childhood that gradually improved and diminished as individuals transitioned to adulthood. Over the past decade, several studies have been published describing a cohort of adolescents with a childhood diagnosis of ADHD experiencing a continuity of ADHD symptoms into adulthood. Untreated ADHD in adults is associated with personal relationship difficulties, educational and occupational underachievement, comorbid mental health problems, substance misuse, and increased rates of road traffic accidents and criminality. These result in an increased economic burden and broader public health challenges. This review outlines the current framework and stage of development of ADHD services for adults in the Republic of Ireland.

Predictive Analysis of Road Traffic Accidents in Katsina State, Nigeria Using Machine Learning Algorithms: A Study on Factors and Mitigation Strategies

This study aims to explore the use of machine learning algorithms in predicting the likelihood of road traffic accidents in Katsina State, Nigeria, with the goal of reducing the high rate of road traffic accidents and associated loss of lives and properties. The study collects and analyzes data on road traffic accidents in Katsina State, including the number of accidents, location, time of day, and the type of vehicles involved. Machine learning algorithms such as Decision Trees, Random Forest, and K-Nearest Neighbors, are trained using the data to predict the likelihood of road traffic accidents. The study also identifies the factors that contribute to road traffic accidents in Katsina State by using techniques such as feature selection and correlation analysis, to identify the most important variables. The findings can be used by stakeholders, including the government, law enforcement agencies, and road safety organizations, to develop and implement effective strategies to reducing road traffic accidents in Katsina State. The study utilizes data collected from the Federal Road Safety Corps database in Katsina, Nigeria, and employs data cleaning and feature selection techniques to improve data quality. The Random Forest algorithm achieved the predictive value as 85% while K-Nearest Neighbors (KNN) and Decision Tree algorithms yielded 17% and 42% respectively.

Comparison of random forest and support vector machine regression models for forecasting road accidents

One of the world’s concerns today is the rate of road traffic accidents (RTA). The overwhelming majority of these accidents occur in low and middle-income countries. RTA are one of the leading causes of death in Rwanda. RTA prediction is crucial for both transportation management and intelligent transportation systems (ITS) development. This paper adopted the use of two modelling techniques, Random forest (RF) and Support vector machine (SVM) for short-term road accident forecasting. The data used to evaluate the models was obtained from the Police. The lower error indices of MAE, MSE, RMSE and higher coefficient of determination (R2) were accuracy measures in comparing the models. The RF model performed better than the SVM model as it revealed higher R2=0.91 compared to the SVM model that was with R2=0.86. Machine learning methods are promising tools for the prediction of road accidents. Prediction positively influences safety enhancements and regulation formulation to prevent future accidents. The appropriate prediction method would help policymakers and healthcare providers adjust their contributions to the accident management process.

Increasing rate of road traffic accidents in Sub Saharan Africa, an issue of negligence or carelessness? Case study, Cameroon

Objective: Road traffic injuries (RTIs) have over the years been on the rise with most casualties coming from the African region, even though they possess and utilize less cars than the developed countries. The purpose of this paper was to highlight the government's and civilians' negligence and carelessness in reducing the incidence of RTIs in Africa, using Cameroon as a case study. Methods of Review: This paper examined various literature searches from Science Direct, PubMed, World Health Organization official reports, Ministry of Public Health Cameroon reports, United Nations Population Fund (UNFPA), Centre for Disease Control and Prevention (CDC), and Scopus. Results: The burden of RTIs is not only felt medically but economically as most victims are often left to cater for their injuries from their personal expenses with little or no government assistance. Although there is a significant lack of data on the exact number of deaths related to road traffic accidents due to poor reporting systems, a variety of factors account for this including; unequitable resource distribution, lack of well-trained paramedics, mismanaged funds, and improper, control, and management of existing road traffic policies. Conclusion: The steady rise in RTIs in Cameroon has cut short the lives of most young people, adversely affected Quality of Life, increased economic burden, we are confident that the best approach to reducing the fatality rate from road accidents is by reviewing and amending policies governing transport systems in Cameroon.

Philosophy and Meanings of the Information Entropy Analysis of Road Safety: Case Study of Russian Cities

This article is devoted to the study of the entropic orderliness of road safety systems of various dimensionalities. The author’s methodology for quantitative assessment of the quality of the road safety systems is based on the use of information entropy analysis, the essence of which is to assess the significance (or “weights”) of various information-technological stages of the road traffic accident rate formation process. The main emphasis in this paper is on the philosophical interpretation of the results of entropic evaluation of the orderliness of urban road safety systems. The article aimed to philosophically understand the reasons for the diversity in the results of assessing the entropy of road safety (RS) in Russian cities. Within the framework of this goal, the results of the analysis of the state of the issue, ideological approaches and methods for assessing the relative entropy of urban road safety systems were presented. The study was based on analyzing statistics that characterize the processes of the formation of road traffic accidents in Russian cities classified into three groups based on population size. The experimental results obtained were explained from the point of view of human psychology. Rather, results were explained from the perspective of human psychology. The final results of the study once again illustrated the objectivity of Hegel’s dialectical laws and, perhaps, once again shattered illusions about the possibility of achieving high levels of road safety in cities by building rigid systems to regulate the actions of traffic participants. In the author’s opinion, the results of the presented philosophical analysis will be useful to managers specializing in the management of complex systems (not only transport but also other fields) to comprehend the contradictions of the complex nature of humans and the paradoxes of their behavior when their freedom of action is restricted through external control.

Obstructive sleep apnea and excessive daytime sleepiness among commercial motor vehicle drivers in Lusaka, Zambia.

Obstructive sleep apnea (OSA) is a risk factor for a major public health problem, car crashes, due to excessive daytime sleepiness (EDS). Commercial vehicle driving (CVD) is a hazardous occupation, having a high fatality rate worldwide. There have been no studies on EDS and OSA in Zambia despite the high rate of annual road traffic accidents (RTAs). We aim to determine the prevalence of EDS and OSA risk among CVDs in Lusaka, Zambia, to assess the impact of OSA on high RTA rates. This was a cross-sectional study. The STOP BANG questionnaire and the Epworth Sleepiness Scale were used. Consecutive sampling of drivers was done who were divided into low and high risk of OSA (HROSA). The risk factors associated with OSA in the bivariate analyses were subjected to a multivariate logistic regression model. One hundred thirty-six drivers participated in the study (all male) with a mean age of 48 ± 5 years. The prevalence of HROSA was 22.8% out of whom 67.7% also had a EDS. Only 9.6% of the total cohort had EDS without HROSA. Using Fisher's exact test, HROSA was significantly associated with older age (> 50 years, P < .001), obesity (body mass index >30, P < .001), neck circumference of > 40 cm (P = .032), and hypertension (P < .001). Snoring and EDS were significantly associated with RTAs (P < .0001 and P = .007, respectively). High risk of OSA and EDS are common among CMV drivers in Zambia and underdiagnosed. The risk factors for OSA are amenable to preventive interventions. Simpamba K, May JL, Waghat A, Attarian H, Mateyo K. Obstructive sleep apnea and excessive daytime sleepiness among commercial motor vehicle drivers in Lusaka, Zambia. J Clin Sleep Med. 2023;19(7):1191-1198.

Information and Entropy Aspects of the Specifics of Regional Road Traffic Accident Rate in Russia

The aim of this research is to study the specifics of the road accident rate formation processes in regions of the Russian Federation (2021) using information-entropic analysis. The typical research approaches (correlation-regression, factorial analyses, simulation modelling, etc.) do not always allow us to identify its specificity. It is impossible to evaluate the quality of the researched process’s structure using these methods. However, this knowledge is required to understand the distinctions between high-quality road safety management and its opposite. In order to achieve the goal of the research methodology based on the use of the classical approaches of C. Shannon, the quantitative value of information entropy H was elaborated. The key components of this method are the modelling of the cause-and-effect chain of road accident rate formation and the consideration of the relative significances of individual blocks of the process in achieving the final result. During the research the required statistical data were collected and the structure of the road accident rate formation process in 82 regions of the Russian Federation in the format “Population P—Fleet of vehicles NVh—Road Traffic Accidents NRA—RTA Victims NV—Fatality Cases ND” was analyzed. The fact that the structure of the road accident rate formation process is extremely specific in different Russian regions was shown. Exactly this specificity forms the degree of ambiguity in the state of Russian regional road safety provision systems in terms of the probability of death in road accidents. The main conclusion of this research is that information-entropic analysis can be successfully used to assess the structural quality of road safety systems.

Characteristics of road traffic accident types and casualties in Guangzhou, China, from 2007 to 2020: A retrospective cohort study based on the general population

IntroductionThis study aimed to explore the trend and main influencing factors of road traffic accidents in Guangzhou, China, from 2007 to 2020 and to provide a reference and guidance for government decision-making. MethodsA retrospective cohort study was used to describe road traffic accidents in Guangzhou. According to the population types, all people with road traffic accidents were divided into migrant workers and the control population. We divided road users, administrative districts, motorcycle types and injury levels into subgroups to investigate the characteristics of road traffic accidents in Guangzhou. The road traffic accident data were derived from the Guangzhou Public Security Traffic Management Integrated System. ResultsThe incidence rate of road traffic accidents per 10,000 vehicles in Guangzhou decreased from 36.55 in 2007 to 10.07 in 2012, remained relatively stable at 9.47 in 2017, and finally rose to 11.12 in 2020. The injury rate showed the same trend as the incidence rate, while the mortality rate gradually decreased from 14.21 in 2007 to 5.19 in 2020. Vulnerable road users such as motorized two-to-three-wheeler drivers and migrant workers were casualties in more than 80% of the cases. The proportion of casualties involving mopeds and electric bicycles increased rapidly after 2018. Motor vehicle drivers frequently caused road traffic accidents and were most often uninjured. ConclusionRoad safety in Guangzhou has shown a clear trend of improvement, but casualties are uneven across administrative districts. More attention should be given to motorized two-to-three-wheelers, migrant workers, and road traffic violations by uninjured individuals.

The influence of neighborhood safety and built environment on childhood obesity: isolated and combined effect of contextual factors.

This study aims to analyze the isolated and combined effect of objective measures concerning neighborhood safety, food, and physical activity environments on students' obesity. This is a cross-sectional study conducted with 9- and 10-year-old children enrolled in the municipal education network of a Brazilian metropolis. Environment objective measures comprised neighborhood unsafety (annual criminality and road traffic accident rates), availability of public parks and spaces for physical activity practicing, and index of establishments that predominantly sell ultra-processed food. Euclidean buffers of 1,000m around the children's house were used as eligible geographic units. This study adopted the Principal Component Analysis and Generalized Estimation Equation models. Stratified analyses were conducted based on neighborhood unsafety and on child's family income. In total, 717 students were assessed, 12.2% of them were children with obesity. The latent variable of the obesogenic environment (deduced by environment unsafety rates and the index of establishments that predominantly sell ultra-processed food) was a risk factor for obesity in children with lower socioeconomic levels (OR = 2.37; 95%CI: 1.06-5.19). Public parks and spaces for physical activity practicing were protective factors against childhood obesity only in locations recording the lowest environment unsafety rates (OR = 0.30; 95%CI: 0.09-0.94). Based on our findings, social conditions change the effect of the environment on childhood obesity, reinforcing the relevance of inter-sectoral policies and strategies against this condition.

Simulation of rates of traffic accidents involving unmanned ground vehicles within a transportation system for the ‘smart city’

This article presents an approach to simulation of the rates of road traffic accidents involving unmanned ground vehicles within a multi-agent intelligent transportation system for the ‘smart city.’ A new simulation model of an intelligent transport system has been developed which makes it possible to significantly reduce the number of potential road traffic accidents (TAs) and implements the concept of socially-centered management of the urban economy. The software implementation of such a large-scale agent-based model was carried out using the FLAME GPU framework, which allows us to effectively parallelize the agents’ behaviour logic and consider their individual decision-making systems when modelling the spacial dynamics of an ensemble of unmanned ground vehicles (UGVs) interacting with other road users: the usual manned ground vehicles (MGVs), unexpected obstacles (e.g., pedestrians, etc.). Various scenarios of such agents’ behaviour in an intelligent transportation system are studied, including the occurrence of an accident under certain conditions (e.g., under the high speed and traffic intensity of UGVs, etc.) and various configurations of the digital road network (DRN). We determine the parameter values that provide for the individual decision-making system of UGVs remaining stable with respect to the characteristics of the external environment (including in extreme situations), ensuring the safety of other road users on the scale of the ‘smart city.’

Exploration of Distracted Road Users in Road Traffic Accidents in South Africa

Distracted road users contribute to high rate of road traffic accidents globally and it is also prevalent in South Africa where many road users lose their lives on the road due to distraction. The inability of road users to adhere to the rules of the road with regard to the use of cell phones, driving while impaired, walking while impaired and all other means of distraction while on the road is a major problem in South Africa. Road users should be focused on the road and follow the rules of the road and avoid other aspects which could derail their journey and lead up to fatality as it is the case on South African roads. This article focused on distracted road users who contribute to high rate of road traffic accidents in South Africa. The article is based on a qualitatively rooted methodology including a wide range of primary and secondary sources. The article further draws on the findings from the world on road safety and formulate steps to road traffic accidents as a result of being distracted on the road. The research based key steps leading to prevention of road traffic accidents as a result of being distracted on the road in South Africa.

Investigation into the Understanding of Traffic Signs, Symbols and Safety Rules among Drivers in Southern Nigeria

Aims: This study examines the understanding of traffic signs and symbols as a safety measure towards accident reduction among drivers in Southern Nigeria. It investigated the understanding of traffic signs and symbols by drivers in Nigeria with regards to their personal characteristics such as age, educational background, and driving experience and also determined the major factors influencing the high rate of road traffic accidents in Nigeria.&#x0D; Study Design: Investigation into the level of knowledge of drivers in understanding traffic signs and symbols in Southern Nigeria.&#x0D; Place and Duration of Study: This study was conducted in Southern Nigeria, from September, 2017 to December, 2017.&#x0D; Methodology: 600 questionnaires were administered within the various city parks in Anambra, Enugu, Delta, and Lagos states but 476 of the respondents participated. A total of 27 symbols, which includes ten regulatory signs, nine warning signs, three traffic signals lights, and five road pavement markings were investigated. Miscellaneous questions for driving under special conditions, mechanical factors, and general guides were also assessed among the drivers. The use of Statistical Package for the Social Science (SPSS), as a tool was employed to analyze the data obtained from the respondents.&#x0D; Results: The analysis showed that there is a low understanding of traffic signs and symbols by drivers in Southern Nigeria. The overall average percentage of drivers who correctly understood the regulatory signs was 59%, warning signs was 64.2%, and pavement markings was 31.8%. The average knowledge percentage of drivers for driving under special conditions, speed/mechanical factor, and general guides are 43%, 53.66%, and 46.4% respectively. From the statistical -analysis of the findings, there is significance prove that age, driving experience, and educational background play an important role in drivers’ understanding of traffic signs and symbols.&#x0D; Conclusion: It is recommended that drivers are properly trained and sensitized on the correct meaning of traffic signs and symbols. The body concerned for issuing a driver’s license should carefully test the drivers’ knowledge before issuing a driver’s license and also traffic law court should be established for the prosecution of defaulters.

Verification analysis of relationship between driving failure probability and traffic accident rate

To enable the reliability theory to be further applied in roadway geometry design and safety evaluation, it is necessary to explore and establish the relationship between the failure index in reliability theory and the traffic accident rate. Based on the collected data, the risk ranking-based verification method and regression prediction model-based verification method considering the probability of driving failure were used to verify the relationship between the probability of driving failure and traffic accident rate. The Spearman’s rank correlation coefficient was calculated and the results show that there is a moderate correlation between the driving failure probability of the selected road segment and the traffic accident rate. Four regression prediction models for both two-lane and four-lane roads were established considering the probability of driving failure and four significant variables including segment length, annual average daily traffic volume, speed limit, and curve radius. The results show that the established regression prediction models can fit accident data well. This research verified and established the relationship between the probability of driving failure and the road traffic accident rate and pointed out that the traffic accident rate has a positive correlation with the probability of driving failure.

Visual Requirements and its Relation with Road Traffic Accident in Saudi Arabia

Introduction: Driving is the act of operating and moving vehicles for which it is critical for drivers to recognize road signs, pedestrians, and animals when on the road. In Saudi Arabia it is estimated that there are over 1 million car accidents per year in which almost 80% are due to vision issues. The aim of the study is to assess the need of visual test and its relation with RTA. Material and methods: This study has followed cross sectional study design conducted among driving license holders aged (17-60) who could be both the resident and non-resident of Saudi Arabia. A structured questionnaire was used to collect data for 550 calculated samples using the online platform. The data was analyzed using excel-13 and SPSS-21. T-test was used to compare vision with correction and number of accidents; vision without correction and number of accidents whereas ANOVA and Post-Hoc test was used to compare the number of accidents among different age groups. Results: This study involves the information collection among 550 respondents who had received the driving license under the Government of Saudi Arabia. Among the respondents, majority (33%) of them were from the age group 17 to 27. The prevalence of vision problem among respondents was 45% in which 72% had their visual correction. All the respondents who did not had visual correction has faced any sort of road traffic accident as the reverberation of denial towards need of visual correction. The study also performed comparative analysis to see the various relations among the driver's visual status and the road traffic accidents, were more number of accidents were recorded among respondents around 17-27 years of age. The study also reflected that there was no statistically significant difference in mean number of accidents between any other age group. Conclusion: Saudi Arabia has one of the highest accidents rate in the world by over 1 million accidents in 2016. This study has significantly highlighted the risk age group and people's denial towards visual correction leading to increase in rate of road traffic accident. So there is need of strictly implementing compulsory vision checkup with medical professional and revised standard visual requirement for obtaining driving license. Keywords: Vision, Visual standards, Road Traffic Accidents, Driving license

Mortality due to road traffic injuries in older adults in the Republic of Ecuador between 1990 and 2018: a descriptive study

Mortality due to road traffic injuries in older adults in the Republic of Ecuador between 1990 and 2018: a descriptive study