Analysis of Traffic Accidents in Vietnam

Understanding the causes and solutions of road traffic accidents is important for developing road and action plans in a country. In Vietnam, awareness of traffic participants is the main cause of serious traffic accidents. In recent years, the number of road traffic accidents in Tuyen Quang province with deaths has decreased, but the number of accidents has increased significantly. The article uses data on traffic accidents in Tuyen Quang over the (2016-2023) has been analytically reviewed. From there, analyze accident characteristics and causes of traffic accidents in Tuyen Quang province, and propose solutions to improve traffic safety in Tuyen Quang, Vietnam. The findings can be information for managers and researchers interested in studying the province of Tuyen Quang, Vietnam road traffic safety. Additionally, the findings have led the government to achieve national targets in reducing the number of accidents and serious injuries.

Development of Accident Prediction Model under Mixed Traffic Conditions:A Case Study

Accident prediction models are developed under mixed traffic conditions.Two models have been developed.The first model is a city based traffic accident prediction model.City population and vehicle ownership are the two parameters used to develop the model.A case study of Surat city in Gujarat is taken up.Total accident occurred in the city are regressed with population and vehicle ownership.Second model is the urban arterial based accident prediction model.Past accident record of ring road of Surat city shows that there are eight different locations on a ten kilometer stretch of ring road where the accidents took place consistently.These locations are picked up as accident spots.As there are many contributing factors and causes to road accidents.A comprehensive study of road safety found that human error was the sole cause in 57%of all accidents and was a contributing factor in over 90%.Keeping this in view a new term driver-pedestrian index is used to develop this model.Regression function of Microsoft excel is used for model development.Both the models are checked with R-statistics and t-statistics further the models are validated by using statistical goodness of fit(chi square test).Hence these models can be used to predict number of accidents in future subjected to the same geometric standards.Keeping this in mind improvement measures can be taken up by the district authorities.

Child Victims of Road Traffic Accidents: Epidemiological and Clinical Aspects in the Medical and Surgical Emergency Department of the Donka National Hospital

Introduction: Children involved in road accidents most often suffer head or limb injuries. The severity of these injuries varies according to the age of the child, the type of road user and whether or not protective devices are used. The aim of this study was to carry out a census of admissions and to identify the epidemiological and clinical profile of children involved in road traffic accidents at Donka National Hospital. Methods: This is a descriptive cross-sectional study of children involved in traffic accidents over a 3-month period from September 1 to November 30, 2021. All children admitted to the emergency department for a traffic accident were included in the study. Results: During the study period, 435/530 children (82.1%) were victims of road accidents. Boys accounted for 54.94% of cases, compared with 45.06% for girls. The age group most affected was 12-17 years old (48.97%). October had the highest accident rate (36.4%). Pedestrians are most affected (52.87%). Bicycles and mopeds were involved in 59.77% of accidents. Head injury was the most frequent pathology (33.33%). The study showed that 12.64% of victims were referred to intensive care, pediatric surgery, etc. We recorded 8 cases of death (1.84%). Conclusion: This study highlighted the characteristics, incidence and risk factors for accidents in children admitted to emergency departments. Accident prevention, particularly serious accidents in children, requires more precise knowledge of the factors and circumstances leading to their occurrence.

Analysis of Traffic Accidents Based on the Integration Model

To enhance the safety of road traffic operations,this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics.Initially,the process involved data cleaning,transformation,and normalization.Subsequently,various classification models were constructed,including logistic regression,k-nearest neighbors,gradient boosting,decision trees,AdaBoost,and extra trees models.Evaluation metrics such as accuracy,precision,recall,F1 score,and Hamming loss were employed.Upon analysis,the passive-aggressive classifier model exhibited superior comprehensive indices compared to other models.Based on the model’s output results,an in-depth examination of the factors influencing traffic accidents was conducted.Additionally,measures and suggestions aimed at reducing the incidence of severe traffic accidents were presented.These findings served as a valuable reference for mitigating the occurrence of traffic accidents.

Overview of the identification of traffic accident-prone locations driven by big data

Effective identification of traffic accident-prone points can reduce accident risks and eliminate safety hazards.This paper first systematically compares the research in Chinese and foreign literature,and proposes three types of identification indicators,namely absolute,relative and comprehensive,according to different reference standards.According to the evaluation indicators and modelling methods,the current status of research and problems in identification theory and methods are systematically summarised in terms of mathematical statistics,cluster analysis,machine learning and conflict technology.The study shows that the foreign literature focuses on the innovation of data and indicators and changes from accident point safety management to road network safety management,while the research in Chinese literature focuses on the integration of multiple identification methods and theoretical innovation.Driven by big data,the identification of traffic accident-prone points has been further developed at the meso-micro scale.Morphological image processing methods are widely used,combined with GIS platforms,to accurately mine the spatial attributes and correlations of accidents.Also,considering the spatial and temporal distribution of accidents,the identification results are also transformed from regions to specific road sections and points to achieve more accurate identification.

Identification of Traffic Accident Causal Factors Through Dynamic Driving Behavior Analysis

The reliability expression of dynamic driving behavior is derived from the driving shaping behavioural model; and then, in accordance with the combination of computer simulation and mathematical expression of driving reliability, an approach for assessing the effect of driving erroneous actions on the dynamic performance of the driver vehicle system is presented. The analysis of driving erroneous actions in the driver vehicle system has been performed to show that the reliability during perception with variety widely could result in the incidents and/or accidents in traffic system.

A Deep Learning Based Sentiment Analytic Model for the Prediction of Traffic Accidents

The severity of traffic accidents is a serious global concern,particularly in developing nations.Knowing the main causes and contributing circumstances may reduce the severity of traffic accidents.There exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter,blogs and Facebook.Although such approaches are popular,there exists an issue of data management and low prediction accuracy.This article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory(XLNet-Bi-LSTM)to predict traffic collisions based on data collected from social media.Initially,a Tweet dataset has been formed by using an exhaustive keyword-based searching strategy.In the next phase,two different types of features named as individual tokens and pair tokens have been obtained by using POS tagging and association rule mining.The output of this phase has been forwarded to a three-layer deep learning model for final prediction.Numerous experiment has been performed to test the efficiency of the proposed XLNet-Bi-LSTM model.It has been shown that the proposed model achieved 94.2%prediction accuracy.

Analysis of Road Traffic Accident Costs in Sudan Using the Human Capital Method

In this study, we used the Human Capital (HC) accident analysis method, to determine the road traffic accident costs in Sudan in two successive years (2010 and 2011) with slight modifications to the recommended and known framework in the way it handles currently and future accident cost components. We evaluated and compared the significance and impact of the economic loss caused by road traffic accidents in Sudan using detailed information on road traffic accident casualties, classified by severity level, vehicle type, and other key parameters such as discount rates and medical and insurance information for Sudan in its entirety. The total cost of road traffic accidents in Sudan in 2010 was estimated at US $391 million, which represents 0.57% of the Gross Domestic Product (GDP), while in 2011 the cost was calculated to reach US $413 million, representing 0.62% of GDP. Findings show that the amount of accident costs is estimated to a certain extent at less than 1% of the total GDP of the country in the two estimation years, but we believe that the evaluation process used fulfilled the eligibility criteria of HC studies and that the produced values for Sudan are valid and reliable. Unit costs for each crash severity level were also estimated in the two years such as death, disability, serious injury, slight injury, and vehicle damage. Death or fatality was equal to US $38,932 and 39,508;disability was equal to US $43,113 and US $45,165;serious injury was equal to US $6963 and US $7596;slight injury was equal to US $2570 and US $3198 and vehicle damage only was equal to US $2268 and US $2579 in the assessment years 2010 and 2011, respectively.

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.

Sensitive Resource and Traffic Density Risk Analysis of Marine Spill Accidents Using Automated Identification System Big Data

This study developed a new methodology for analyzing the risk level of marine spill accidents from two perspectives,namely,marine traffic density and sensitive resources.Through a case study conducted in Busan,South Korea,detailed procedures of the methodology were proposed and its scalability was confirmed.To analyze the risk from a more detailed and microscopic viewpoint,vessel routes as hazard sources were delineated on the basis of automated identification system(AIS)big data.The outliers and errors of AIS big data were removed using the density-based spatial clustering of applications with noise algorithm,and a marine traffic density map was evaluated by combining all of the gridded routes.Vulnerability of marine environment was identified on the basis of the sensitive resource map constructed by the Korea Coast Guard in a similar manner to the National Oceanic and Atmospheric Administration environmental sensitivity index approach.In this study,aquaculture sites,water intake facilities of power plants,and beach/resort areas were selected as representative indicators for each category.The vulnerability values of neighboring cells decreased according to the Euclidean distance from the resource cells.Two resulting maps were aggregated to construct a final sensitive resource and traffic density(SRTD)risk analysis map of the Busan–Ulsan sea areas.We confirmed the effectiveness of SRTD risk analysis by comparing it with the actual marine spill accident records.Results show that all of the marine spill accidents in 2018 occurred within 2 km of high-risk cells(level 6 and above).Thus,if accident management and monitoring capabilities are concentrated on high-risk cells,which account for only 6.45%of the total study area,then it is expected that it will be possible to cope with most marine spill accidents effectively.

Application of GIS for Urban Traffic Accidents: A Critical Review

The analysis of huge data is a complex task that cannot be executed without a proper system. Geographic information systems (GISs) have been used by many transportation agencies and police departments to analyze and manage urban traffic accident (UTA) data and for decision making aimed at decreasing accident rates and increasing safety. The exact location of accidents and environmental characteristics must be analyzed as UTAs occur in specific locations with specific characteristics. ArcGIS software is the best choice for obtaining meaningful information and analysis results from UTAs in an observational time span. GIS technology is a fundamental element for investigating and evaluating the complex spatial relationship among different components and urban traffic accident is one of them. Micro or macro analysis of UTAs through the spatial prospective within the geographical environment and urban structure can make a deep micro understanding of UTAs patterns in addition to assisting in decision making. UTAs can be considered complex events that occur in two aspects which are spatial and temporal or space and time in other word. A GIS can integrate more than two different and unrelated databases. The evaluation among different spatial objects in a geographical environment and associated factors in urban structure which are included but not limited to land use category, road transportation network qualification, population density, etc., is one of the GIS specification. Traffic safety organizations and UTA researchers use GISs as a key technology to support their research and operational needs. In particular, GIS-T is an often-used GIS application used for planning and decision-making in transportation.

Fatality from Road Traffic Accident in Guinea: A Retrospective Descriptive Analysis

Objectives: Causes and risk factors that result in fatal road traffic accident have not been described at the national level in Guinea yet. The goal of this study is to explore the causes and risk factors related to fatal road traffic accident, identified most vulnerable road users, and inform the road traffic prevention policy in Guinea. Methods: We made a retrospective descriptive analysis based on national fatal road traffic accident data from the Department of Health Information at the Guinean Ministry of Health for year 2011. Results: In 2011, road traffic accident was responsible for an aggregate number of 1655 deaths with an overall death rate of 15.3 per 100,000 population. Male experienced more than twice the risk of death from road traffic accidents (21.9 deaths per 100,000 population) compared with female (9.0 deaths per 100,000 population). While taking the population as a whole, the highest death rate was found among the middle aged in 35 - 49 age group accounting for (29.7 deaths per 100,000 population), followed successively by young adults age group 25 - 34 years (24.6 deaths per 100,000 population), and the middle aged in 50 - 64 age group (22.9 deaths per 100,000 population). Principally, occupants, motorcyclists and pedestrians sustained considerable burden of deaths respectively (9.2;2.9;2.2 per 100,000 population). In re-gional setting, the highest death rate was found in Upper Guinea (19.5 per 100,000 population), followed by Forest Guinea (18.7 per 100,000 population) and Middle Guinea (16.8 per 100,000 population). A large proportion of male was killed as motorcyclist than female while high per-centage of female died as occupant than male for all age group. The regional distribution showed that when a remarkable number of occupant death were observed in Upper and Forest Guinea, more people died as pedestrian and pedal cyclist in Conakry. Conclusions: This study demonstrated that most of the deaths were among occupants, motorcyclists and pedestrians, and the productive workforce aged 25 - 49 years. It was found that majority of the deaths happened in Upper Guinea followed by Forest Guinea. Improvement of roads design, strict enforcement of road safety laws and raising the awareness of general public about the causes and risks factors of road traffic accident through various channels are highly required which will promote economic growth in the local communities and then help people escape the poverty trap.

Analysis of the Relationship between the Number of Traffic Accidents and the Traffic Flow &Section Location in Extra Long Tunnel

In order to study the influence of the traffic characteristics on traffic accidents in extra long tunnel, the main measurement indicators of traffic flow during the time of traffic accidents are matched with the accident information to form a data set of the number of traffic accidents and the hourly traffic flow of the accident. Vehicle ratio and the number of accidents are mainly used as the characteristic indicators of traffic flow. At the same time, the longitudinal distribution law of the average speed of traffic flow and the number of traffic accidents in the extra long tunnel is studied. Based on the superposition principle, the extra long tunnel is divided into 5 traffic safety zones. This paper analyzes the distribution of time, morphology, cause of accident, and other characteristics in different traffic safety zones, finding that the shape of traffic accidents in extra long tunnel is mainly rear-end collisions. Improper operation and illegal lane changes are the main causes of accidents.

Injury patterns of seniors in traffic accidents: A technical and medical analysis

AIM:To investigate the actual injury situation of seniors in traffic accidents and to evaluate the different injury patterns.METHODS:Injury data,environmental circumstances and crash circumstances of accidents were collected shortly after the accident event at the scene.With these data,a technical and medical analysis was performed,including Injury Severity Score,Abbreviated Injury Scale and Maximum Abbreviated Injury Scale.The method of data collection is named the German InDepth Accident Study and can be seen as representative.RESULTS:A total of 4430 injured seniors in traffic accidents were evaluated.The incidence of sustaining severe injuries to extremities,head and maxillofacial region was significantly higher in the group of elderly people compared to a younger age(P<0.05).The number of accident-related injuries was higher in the group of seniors compared to other groups.CONCLUSION:Seniors are more likely to be involved in traffic injuries and to sustain serious to severe injuries compared to other groups.

Black Spot Determination of Traffic Accident Locations and Its Spatial Association Characteristic Analysis Based on GIS

In general, the location of traffic accidents is described as an address with text, so they are difficult to display on the map. The paper discusses how to utilize the geocoding technology and VRS-GPS positioning technology to record the traffic accidents with Geo-spatial information. Based on the spatial relationship between traffic accidents and road network elements, two-way association relationship is defined by spatial relationship computation. Then the paper presents the method which takes the potential of reducing accidents as an index to extract the black spots. Finally, in the discussion, the association relationship between black spots and traffic attributes is used to analyze the factors that resulting in traffic accidents.

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.

Increasing Road Traffic Throughput through Dynamic Traffic Accident Risk Mitigation

The introduction of vehicular ad-hoc networks (VANETs) leads to the possibility to re-evaluate many traditional functions and views of road traffic networks. The ability for vehicles and infrastructure to communicate and collaborate will enable many novel solutions for problems as diverse as collision avoidance and traffic management with the view of reducing traffic congestion, increasing the effectiveness of logistics systems etc. In this paper we introduce a novel framework that utilises VANET information to share information about risk factors among road occupants and infrastructure. We introduce the concept of risk limits as a means of traffic accident risk mitigation, whereby vehicles need to adjust their behaviour to maintain a given level of risk. We discuss determination of risk values and detail this process using the NSW traffic accident database. We show how the effects on risk of particular vehicular behaviours such as speed and headway can be calculated and use these results to modify vehicle behaviour in real time to maintain a predefined risk limit. Experiments are carried out using the Paramics Microsimulator. Our results show that it is possible to reduce the accident rate among vehicles while at the same time increasing road network throughput by exploiting the variation in risk between vehicles.

Nursing students'knowledge,willingness,and attitudes toward the first aid behavior as bystanders in traffic accident trauma:A cross-sectional survey

Objectives:The purpose of the study was to investigate the nursing students'levels of the knowledge,willingness,and attitudes toward first aid behavior as bystanders in road traffic accident and the related factors.Methods:A total of 475 nursing students were recruited by convenience choosing in Tianjin University of Traditional Chinese Medicine.The nursing students'self-efficacy,core self-evaluation,knowledge,willingness and attitudes toward first aid behavior as bystanders in traffic accidents were investigated with a self-designed questionnaire.Results:The scores of knowledge,willingness,and attitudes toward first aid behavior in traffic accident trauma were 7.51±1.93,15.54±5.03,and 7.73±1.56,respectively.Students who once gained training of first aid showed lower levels of attitude toward first aid behavior than those untrained(t=-2.345,P=0.019).It was found that self-efficacy was correlated to the knowledge(r=0.150,P<0.001),willingness(r=0.182,P<0.004)and attitudes toward behavior of the first aid(r=0.371,P<0.001)among nursing students.Core self-evaluation was correlated to knowledge(r=0.193,P<0.001)and attitudes toward behavior of the first aid(r=0.199,P<0.001).Conclusions:The first aid knowledge among nursing students was not satisfactory.The study suggested that an unsustainable short first-aid training program may bring negative effects.Countermeasures should be taken to ensure both quality and continuity of first aid training.Meanwhile,more attention should be paid to improving the self-efficacy and core self-evaluation of the nursing students.

Epidemiological Pattern of Injuries Resulting from Road Traffic Accidents in Khartoum, Sudan

Background: Traffic accidents are increasingly becoming a threat to public health and national development in many developing countries. Sudan isn’t an exception, the number of vehicles is rapidly increasing while the road’s widths remain as same as before, so many accidents took place, and serious losses and damages to both human beings and vehicles may occur. This study was aimed at identifying the epidemiological pattern of injuries and determinants that contribute to road traffic accidents (RTA) in Khartoum Teaching Hospital Sudan. Methodology: Hospital based descriptive study was conducted in Khartoum Teaching Hospital. 150 road traffic victims who attended the emergency room or were admitted to the hospital from February-May 2012 and fulfilled the inclusion criteria were included in the study. A pre-test standardized administered questionnaire was used for data collection. Results: Males were more affected (85%);laborers were the highest among the victims (43%). Sixty three of RTA causes were related to the drivers. Conclusion: Urgent intervention is needed through the involvement of many sectors and specialties. There is an urgent need to setup a database to collect, store and analyze information related to road traffic accidents. This will help the authorities to plan strategically for appropriate interventions across the country.

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.