Analysis of Thorax Injuries in 112 Death Cases Caused by Traffic Accidents

Objective: To explore the characteristics and mechanisms of serious injuries of chest caused by road traffic accidents. Methods: Totally 112 autopsy cases with chest injuries in the urban of Jingzhou road traffic accidents were collected. Systematic review and analysis of the general information, postmortem examinations and assessments of chest injury had carried out from Feb. 2016 to Mar. 2018. Results: Average age of the victims was 52.2 years and the ratio of male to female deaths was 2.39:1. The proportion of motor-cyclists and pedestrians increased significantly. The overwhelming majority of accident vehicles were motorcycles and bicycles. Fractures of ribs and pulmonary contusion were the most common injuries. Craniocerebral and abdominal injuries were the most common associated injuries. Conclusion: Fractures of ribs and pulmonary contusion were the most common features of fatal road traffic injuries, often associated with vitreoretinal damage and serious multiple damages. These features reflect the characteristics of great violence in traffic accidents, which provides the evidence of identification of violent injuries.

Classification of Emergency Responses to Fatal Traffic Accidents in Chinese Urban Areas

Fatal traffic accidents in urban areas can adversely affect the urban road traffic system and pose many challenges for urban traffic management.Therefore,it is necessary to first classify emergency responses to such accidents and then handle them quickly and correctly.The aim of this paper is to develop an evaluation index system and to use appropriate methods to investigate emergency-response classifications to fatal traffic accidents in Chinese urban areas.This study used a multilevel hierarchical structural model to determine emergency-response classification.In the model,accident attributes,urban road network vulnerability,and institutional resilience were used as classification criteria.Each evaluation indicator was selected according to importance ranking and independence screening and was given an interpretation and a quantitative criterion.The Fuzzy Delphi Method was used to rank the importance of the evaluation indices and the combined weight of each index was calculated using the G1 method.Finally,the case of a fatal traffic accident was used to validate the model.The results showed that the multilevel hierarchical structural model,Fuzzy Delphi Method,and G1 method can effectively address the problem of emergency-response classification.Because of its simplicity and adaptability,the approach presented here could be useful for decisionmakers and practitioners for determining emergency-response classifications.

Countermeasures for traffic accidents due to road conditions in China

Regarding the postulate of traffic infrastructure and vehicles, much attention should be given to the effect of road conditions on accidents. With large numbers of traffic accidents on Shenda Freeway, Liaoning Province, Harbin City and others in P. R. China, parameters and the effect of accidents caused by horizontal alignment, vertical alignment, cross section and intersection are studied systematically The disciplinary analysis of these effects are presented in this paper. The viewpoint is acknowledged that high sub grade and steep slopes are against traffic safety, which is common and ignored in high-usage highways in China. Design parameters of the current design criteria and the corresponding countermeasures are suggested for safety on our highways.

On the Time Series Forecasting of Road Traffic Accidents in Ondo State of Nigeria

This paper focuses on time series forecasting of monthly occurrence of fatal road accidents in Ondo State of Nigeria. Its aim, however, is to use time series analysis to analyze the data obtained from Federal Road Safety Corps （FRSC）, Ondo State Command; which was considered in two cases： the total cases reported （TCR） and the number of deaths resulted from accidents （NOD）. Various smoothing models for time series were used to analyze the two cases. Based on the models, predictions were made and the results show a steady increase as a result of long-term effects on road accidents for the two cases. It was found also that simple exponential smoothing model is the appropriate model for both TCR and NOD.

Spatial Analysis of Traffic Accidents in the City of Medina Using GIS

The general objective of this research is to determine how to use the spatial analysis of traffic accidents in Medina Menorah City through geographic information systems. This research aimed to identify, locate and define the sites where traffic accidents are concentrated and determine the need to apply specific safety standards to reduce accidents and identify their causes thereof. This current research applied the analytical descriptive approach for its relevance with this specific research. This research collected traffic accidents data from the Ministry of the Interior, Department of General Traffic. That data captured the hotspots accidents in Medina Menorah City. Some of the most important results of the study are as follows: many roads were selected as High Accident Location HAL, such as Central Ring Roads, King Faisal bin Abdul-Aziz Road, Prince Abdul Majid bin Abdul-Aziz Road, and King Abdulla bin Abdel-Aziz Road. The high-speed roads are heavily linked to the massive increase of traffic accident rates, and the increase in the street section length led to the soaring number of total accidents. The study recommended performing more studies and different highway safety studies to identify and locate accident patterns on road networks. Due to the fact that the accidents concentration is intensely focused on Medina City center and Prophet’s Mosque, it is a must to increase the number of public transportations to and from Prophet’s Mosque, particularly during the Hajj period, because of the fact that the visitors of Prophet’s Mosque is on the increase during the said period. This study can be applied in other cities because knowing the locations of traffic crash hotspots can provide us with valuable insights into the causes of accidents and this knowledge helps decision-makers to better assess the risk associated with accidents.

Risk factors and predictive model of cerebral edema after road traffic accidents-related traumatic brain injury

Purpose:Cerebral edema(CE)is the main secondary injury following traumatic brain injury(TBI)caused by road traffic accidents(RTAs).It is challenging to be predicted timely.In this study,we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries.Methods:This case-control study included 218 patients with TBI caused by RTAs.The cohort was divided into CE and non-CE groups,according to CT results within 7 days.Demographic data,imaging data,and clinical data were collected and analyzed.Quantitative variables that follow normal distribution were presented as mean±standard deviation,those that do not follow normal distribution were presented as median(Q1,Q3).Categorical variables were expressed as percentages.The Chi-square test and logistic regression analysis were used to identify risk factors for CE.Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries.The efficacy of the model was evaluated using the receiver operator characteristic curve.Results:According to the study,almost half(47.3%)of the patients were found to have CE.The risk factors associated with CE were bilateral frontal lobe contusion,unilateral frontal lobe contusion,cerebral contusion,subarachnoid hemorrhage,and abbreviated injury scale(AIS).The odds ratio values for these factors were 7.27(95%confidence interval(CI):2.08-25.42,p=0.002),2.85(95%CI:1.11-7.31,p=0.030),2.62(95%CI:1.12-6.13,p=0.027),2.44(95%CI:1.25-4.76,p=0.009),and 1.5(95%CI:1.10-2.04,p=0.009),respectively.We also observed that patients with mild/moderate TBI(AIS≤3)had a 50%probability of developing CE 19.7 h after injury(χ^(2)=13.82,adjusted R2=0.51),while patients with severe TBI(AIS>3)developed CE after 12.5 h(χ^(2)=18.48,adjusted R2=0.54).Finally,we conducted a receiver operator characteristic curve analysis of CE time,which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI,respectively.Conclusion:Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury.Specifically,those with more severe injuries experienced an earlier onset of CE.These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.

Cause analysis of expressway traffic accidents based on accident statistics of Hunan provincial expressway in 2012-2018

Based on the 2012–2018 accident statistics on the Hunan Provincial Expressway in China,this paper analyses the proportion to which various factors contribute to accidents,as well as the trends over time.From 2012 to 2018,the total number of general procedure handling accidents(i.e.major accidents)increased first and then decreased,while the number of accidents and deaths per 1000 kilometres decreased year by year.The total number of simple procedure handling accidents(i.e.minor accidents)increased year by year,but the number of accidents per 1000 kilometres remained relatively stable.Speeding,fatigued driving,improper driving operation,failure to maintain a safe distance from the preceding vehicle and pedestrians entering the expressway are the main causes of general procedure handling accidents.Failure to maintain a safe distance from the preceding vehicle as required and improper driving operation accounted for approximately 2/3 of the simple procedure handling accidents.In view of the accidents caused by these factors,how to use law enforcement,education,science and technology and facilities construction to further reduce the occurrence of expressway traffic accidents is discussed.

A Workable Solution for Reducing the Large Number of Vehicle and Pedestrian Accidents Occurring on a Yellow Light

Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.

Traffic accidents of autonomous vehicles based on knowledge mapping:A review

As a characteristic representative of the new generation of vehicles,autonomous driving is expected to improve people's driving experience.The study on traffic accident of autonomous vehicles(AVs)helps provide suggestions for autonomous driving safety from multiple disciplines and perspectives,and provide support for formulating traffic accident treatment schemes.Knowledge mapping,as a cutting-edge research method in bibliometrics,scientifically and objectively displays the relevant research status using visual means.This paper uses CiteSpace 6.1.r3 to analyze 5068 related literature on the Web of Science database from 1991 to 2022 and finds out major thematic clusters,important documents and representative journals according to citation frequency.The results show that research on traffic accidents involving AVs focuses on accident preventing technologies,including how to avoid collisions,track lane-position,and enhance vehicle-toeverything(V2X)communication.This paper extracts the mean research topics and key points involved in the field and illustrates journals related to AVs and traffic accidents,which provides guidance for subsequent researchers to carry out in-depth research and contribute their papers.Popular journals are in disciplines of mathematics,systems,computer,economics,and social science.This paper also suggests scholars to consider the aspects of scene reconstruction,cause analysis,and injury of vulnerable road users,so as to investigate traffic accidents and put forward effective treatment schemes to reduce AV accidents,and ultimately improve road safety.

GIS-based spatial analysis of urban traffic accidents： Case study in Mashhad, Iran

There is a growing concern in traffic accident rate in recent years. Using Mashhad city （Iran second populous city） traffic accident records as case study, this paper applied the combi- nation of geo-information technology and spatial-statistical analysis to bring out the influence of spatial factors in their formation. The aim of the study is to examine 4 clustering analyses to have a better understanding of traffic accidents patterns in complex urban network. In order to deploy the clustering technique in urban roads, 9331 point features for inner city traffic accidents during 12 months have been registered according to their x and y location in geographic information system （GIS）. The mentioned areas were analyzed by kernel density estimation （KDE） using ARCMAP and two other analyses using SANET 4th edition software so that the results of network analysis can be compared with traditional KDE method. In addi- tion, this research introduces five classifications for determining the eventfulness of the under study area based on standard deviation and to make priority in creating security in the area. The nearest neighbor and K-function output analysis consist of four curves and regarding the fact that for all fatal, injury and property damage only crashes, the observed value curve is above the 5% confidence interval. Accidents in the study region are more clustered than ex- pected by random chance. The importance of this study is to use GIS as a management system for accident analysis by combination of spatial-statistical methods.

Clinical analysis of 28 children suffering from intracranial hematoma and multiple injuries following traffic accidents

Objective: To evaluate the result of diagnosis and treatment of intracranial hematoma and multiple injuries caused by road traffic accidents. Methods: Twenty-eight patients, aged from 1 to 14 years, receiving craniotomy and other surgical treatments were retrospectively reviewed. Results: Among the 28 cases, 23 cured with the recovery rate of (82.3)%, 2 had a sequel of moderate disability, and 3 died from severe brain injury, hemorrhagic shock, and other visceral complications. The clinical sympotoms and signs were severe and perplexing. The major characters included: severe head injury, usually combined by multiple injuries, and easy of access to missed diagnosis and misdiagnosis. Conclusions: The occurrence of infection is high after traffic accidents as a result of depression of humoral and cellular immunity, long-term bed rest, and fractures of limbs. Hence, on the basis of maintaining vital signs, the management of primary wound is essential to reduce infection and underlying death. In addition to the management of brain injury, concurrent injuries should also be highlighted so as to reach a good result for their patients.

Weather and traffic accidents in Hong Kong

The influence of rainfall and thermal stress in accident occurrence for the period 1985 through 1994 in Hong Kong was investigated. The existence of seasonal variations and weekly cycle was also examined. Daity accident and meteorological data were obtained from the Transport Department and the Hong Kong Observatory respectively. Utilizing regression analyses and ANOVA, it is discovered that seasonal variation was not important in contributing traffic accidents, and Saturday peak was not ascertained. A non linear relationship between accident occurrence and various rain conditions was found. Accident numbers increased till rain exceeded 50.0 mm. High traffic volume and low speed urban driving in Hong Kong can be the major cause of traffic accidents on rainy days. The impact of heat stress on accident occurrence was found to be statistically significant.

Hospital-based emergency department visits in children with motor vehicle traffic accidents: estimates from the nationwide emergency department sample

Background:The purpose of this study is to provide nationally representative estimates of children visiting hospital-based emergency departments(ED)for motor vechicle traffic accidents(MVTA)in the United States during the year of 2008.Methods:Nationwide Emergency Department Sample for 2008 was used.All pediatric(age<18 years)ED visits with external cause for injury ICD-9-diagnostic codes for MVTA were selected.Outcomes examined included discharge status following ED visit and presence of concomitant injuries.Descriptive statistics was used to summarize the estimates.Results:Totally 604027 hospital-based ED visits occurred in the United States among children(age≤18 years)due to MVTA.Following an ED visit,91%were discharged routinely,while 6%were admitted as inpatients into the same hospital.A total of 928 children died in the ED.A total of 34004 ED visits required inpatient admission into the same hospital and 768 patients died during hospitalization.Mean charge per ED visit was$1887 and total ED charges across the United States were close to$970 million.Among those admitted into the same hospital following ED visit(n=34004),the mean hospitalization charge was$53726 and total hospitalization charge across the entire United States were$1.8 billion.Conclusions:Study findings illustrate the burden associated with pediatric ED visits due to MVTA.Close to$970 million of hospital charges were incurred by children who made an ED visit due to a MVTA during 2008 and about$1.8 billion was incurred among those hospitalized following an ED visit.

A novel prediction model of traffic accidents based on big data

The occurrence of traffic accidents is regular in probability distribution.Using big data mining method to predict traffic accidents is conducive to taking measures to prevent or reduce traffic accidents in advance.In recent years,prediction methods of traffic accidents used by researchers have some problems,such as low calculation accuracy.Therefore,a prediction model of traffic accidents based on joint probability density feature extraction of big data is proposed in this paper.First,a function of big data joint probability distribution for traffic accidents is established.Second,establishing big data distributed database model of traffic accidents with the statistical analysis method in order to mine the association rules characteristic quantity reflecting the law of traffic accidents,and then extracting the joint probability density feature of big data for traffic accident probability distribution.According to the result of feature extraction,adaptive functional and directivity are predicted,and then the regularity prediction of traffic accidents is realized based on the result of association directional clustering,so as to optimize the design of the prediction model of traffic accidents based on big data.Simulation results show that in predicting traffic accidents,the model in this paper has advantages of relatively high accuracy,relatively good confidence and stable prediction result.

Analysis of risk factors contributing to road traffic accidents in a tertiary care hospital. A hospital based cross-sectional study

Purpose: Road traffic accidents (RTAs) are a public health issue and cost a lot to individuals, families, communities and nations. Trauma care systems in India are at a nascent stage of development. There is gross disparity between trauma services available in various parts of the country. Rural area in India has inefficient services for trauma care, due to the varied topography, financial constraints, and lack of appropriate health infrastructure. The present study is to study the trends of occurrence of RTA cases by month, week and time of accident occurrence as well as to research the types of vehicle involved in accidents and other various risk factors related to them.Methods: During 1st January 2017 to 31st December 2017, a hospital-based and cross-sectional study of RTA victims was conducted. The patients were admitted in emergency department of Uttar Pradesh University of Medical Sciences, Saifai, Etawah, when stabilized, they were shifted to the orthopaedics and surgery ward.Results: In the study, 654 road accident victims were included, of which the majority were males (77.5%) and the most of them belonged to rural (67%). RTA victims according to the month of occurrence majority were found in January (12.5%) and evening was time of a day with maximum accidents (32.1%). Mortality cases of RTA victims based on type of road user and it shows decreasing trend of mortality of motor-cyclists (54.2%) followed by pedestrian (25.1%).Conclusion: There should be control over people driving vehicles under the influence of alcohol and drivers over-speeding and rash driving on urban roads as well as rural village roads.

Evaluation of differences in injury patterns according to seat position in trauma victims survived traffic accidents

Purpose： Investigation of injury patterns epidemiology among car occupants may help to develop different therapeutic approach according to the seat position. The aim of the study was to evaluate and compare differences in the incidence of serious injuries, between occupants in different locations in private cars. Methods： A retrospective study including trauma patients who were involved in motor vehicle accidents and admitted alive to 20 hospitals （6 level I trauma centers and 14 level II trauma centers）. We examined the incidence of injures with abbreviated injury score 3 and more, and compared their occurrence be- tween seat locations. Results： The study included 28,653 trauma patients, drivers account for 60.8% （17,417）. Front passenger mortality was 0.47% higher than in drivers. Rear seat passengers were at greater risk （10.26%） for trau- matic brain injuries than front seat passengers （7.48%） and drivers （7.01%）. Drivers are less likely to suffer from serious abdominal injuries （3.84%） compared to the passengers （front passengers - 5.91%, rear passengers - 5.46%）. Conclusion： Out of victims who arrived alive to the hospital, highest mortality was found in front seat passengers. The rate of serious chest injuries was higher as well. Rear seat passengers are at greater risk for serious traumatic brain injuries. All passengers have a greater incidence of abdominal injuries. These findings need to be addressed in order to develop ＂customized＂ therapeutic policy in trauma victims.

Evaluation of the status of the pre-hospital trauma care in road traffic accidents in Kancheepuram district of Tamil Nadu

Objective:To evaluate the status of the pre-hospital trauma care in a road traffic accidents in a rural area of Kancheepuram district.Methods:A cross-sectional study of two months duration(June and July 2014)was conducted in the tertiary care hospital of a medical college,and its affiliated urban/rural health centers.Universal sampling was used and all road accident victims were enrolled as study participants.The required information was obtained with the help of a semi-structured questionnaire.Ethical clearance was obtained before the start of the study.SPSS version 18 was used for data entry and statistical analysis.Descriptive statistics were calculated for all the variables.Results:A total of 80(77.7%)study subjects were from the productive age group(15-45 years).Most of the accidents were reported at night time[43(41.7%)],on weekends[59(56.5%)],and involved two-wheelers[81(78.6%)].In addition,69 cases(67%)of the victims were not aware of the existence of emergency ambulance services,while only 6(5.8%)of the victims were brought to the hospital in an emergency ambulance.Conclusions:The study findings clearly suggest that the quality of the pre-hospital trauma care for road traffic accident victims in a rural area of Kancheepuram district lacks on multiple dimensions and there is an immense need to improve and strengthen the range of services to save the lives of the victims.

论《牛津高中英语》Project板块的教学——以Traffic Accidents and Road Safety

教学目标:Project板块是《牛津高中英语》中非常重要的一个组成部分,分为A、B两个部分,A部分一般是一些阅读材料,数量一篇到两篇,与课题活动有关,B部分就是课题活动。将A、B有效结合起来,可以提高学生的综合素质。Project板块的教学目标主要是让学生开口去说,并且动手去做,主要是以B部分为主,A部分是为B部分的顺利实施奠定基础。英语老师要围绕这个教学目标。

Time analysis of fatal traffic accidents in Fars Province of Iran

Objective： To analyze the time factor in road traffic accidents （RTAs） in Fats Province of Iran. Methods： This study was conducted in Fars Province, Iran from November 22, 2009 to November 21,2011. Victims＇ information consisted of age, sex, death toll involving dri- vers or passengers of cars, motorcycles and pedestrians, and site of injury etc. Accidents were analyzed in relation to hour of the day, season of the year, lighting condition in- cluding sunrise, sunset, daytime and nighttime. Results： Atotal of 3 642 deaths （78.3% were males, and the ratio of males to females was about 3.6：1） were studied regarding their autopsy records. There was a steady in- crease in fatal accidents occurring at midnight to 15：59. The risk of being involved in a fatal traffic accident was higherfor those injured between 4：00 to 7：59 than at other times （OR=2.13, 95% C11.85-2.44）. The greatest number of fatal RTAs took place in summer. Mortalities due to RTA during spring and summer were more pronounced at 20：00 to 23：59 and midnight to 3：59, whereas mortalities in fall and winter were more pronounced from 12：00 to 15：59. Conclusion： The high mortality rate ofRTAis a major public health problem in Fars Province. Our results indicate that the time is an important factor which contributes to road traffic deaths.

Educational level and age as contributing factors to road traffic accidents

Objective： This research analyzes data on road traffic accidents （RTA） in Fars province, whose roads are among the highly dangerous ones in Iran. It inves- tigates educational level and age involved in RTA in order to discover patterns that can prevent or decrease accidents. Methods： This research made use of data visualization techniques to find hidden patterns. The data included mor- tality rate related to RTA in Fars province and were obtained from Fars Forensic Medicine Registry covering a period of 1 year from March 21, 2010 to March 21, 2011. All data were analyzed using SPSS 11.5. The results were reported as de- scriptive indices such as frequency （percentage）. The Chi- square test was applied to the data concerning educational level and age. P value less than 0.05 was considered significant. Results： In the mentioned period, 1 831 people were killed, out of whom un/lowly educated people （69.6%） ac- counted for the highest mortality rate. The significant rela- tionship between educational level and mortality rate was found （X2=275.98, P〈0.0001）. Also three was a significant association between age and mortality rate （X2=371.20, P〈0.0001）. Young people （age between 20 and 29 years） contribute to higher RTA mortality rate compared with other age groups. Conclusion： The educational level and age are signifi- cantly correlated to mortality rate. The youth and un/lowly educated people suffer more fatal RTA.