Types Of Traffic Accidents Research Articles

A Study on Disabling Injuries Prediction of Taiwan Occupational Disaster with Grey Rolling Model

In order to protect the safety and health of laborers and to achieve the goal of zero occupational accidents at work, the study takes the top three industries with the highest number of laborers inspections from 2010 to 2019, namely, construction, manufacturing, wholesale, and retail as the research object. Using three major indicators of disability injury including Disabling Frequency Rate, Disabling Severity Rate, and Frequency Severity Indicator as parameters, it applies grey theory to establish a GM (1,1) rolling forecast model. It further predicts the trend of disability injuries from 2020 to 2025. Based on the optimized GM (1,1) rolling model, the results show that there has the highest accuracy rate in the prediction of Disabling Frequency Rate (accuracy is 95.235% in K7) in construction. Disabling Severity Rate and Frequency Severity Indicator are both in wholesale and retail industries (accuracy is 97.044% in K6 and accuracy is 99.906% in K5). Therefore, Disabling Severity Rate has an upward trend, which is due to the common type of traffic accidents in the wholesale and retail industry. The study further proposes that relevant actual disaster cases could be the training materials and strengthen the communication in education to improve workers’ safety awareness for occupational disaster prevention.

Characteristics of Road Traffic Accident Types, Casualties, and Population in Guangzhou, China from 2007 to 2020: A Retrospective Study

Characteristics of Road Traffic Accident Types, Casualties, and Population in Guangzhou, China from 2007 to 2020: A Retrospective Study

Expert Analysis of Visibility from the Drivers Seat of М1 Vehicles

The total number of automobiles in Russia currently equals 55 mln., 45 mln. of which are passenger cars. Although the number of traffic accident is going down year by year, the problem remains very urgent, the number of people killed in such accidents is over 16 th., and the number of injured — 190 th. One of the most common types of traffic accidents is accidents with pedestrians, which happen through the fault of drivers or the pedestrians themselves. The conducted analysis made it possible to establish the influence of visibility from the driver’s seat in modern vehicles of М1 category on this type of traffic accidents. The analysis of visibility from the driver’s seat, specifically determining the angles of blind spots formed by the pillars of front windows, was done for one of the flagship Japanese models Toyota Land Cruiser 200. Research showed that the angle of blind spots formed by the front pillars of the bodies of a number of modern cars can exceed the value set by the Russian state standard (GOST) by 30 %. It is recommended to conduct an investigative experiment to assess the visibility from the driver’s seat when examining the circumstances of road accidents with pedestrians.

Variance-based global sensitivity analysis for rear-end crash investigation using deep learning

Traffic accidents are rare events with inconsistent spatial and temporal dimensions; thus, accident injury severity (INJ-S) analysis faces a significant challenge in its classification and data stability. While classical statistical models have limitations in accurately modeling INJ-S, advanced machine learning methods have no apparent equations to prioritize/analyze different contributing factors to predict INJ-S levels. Also, the intercorrelations among the input factors could make the results of a typical sensitivity analysis misleading. Rear-end accidents constitute the most frequent type of traffic accidents; and therefore, their associated INJ-S need more insight investigations. To resolve all these issues, this study presents a sophisticated approach based on a deep learning paradigm combined with a Variance-Based Globa1 Sensitivity Analysis (VB/GSA). The methodology proposes a deep residual neural networks structure that utilizes residual shortcuts (i.e., connections), unlike other neural network architectures. The connections allow the DRNNs to bypass a few layers in the deep network architecture, circumventing the regular training with high accuracy problems. The Monte Carlo simulation with the aid of the trained DRNNs model was conducted to investigate the impact of each explanatory factor on the INJ-S levels based on the VB/GSA. The developed methodology was used to analyze all rear-end accidents in North Carolina from 2010 to 2017. The performance of the developed methodology was evaluated utilizing some selected representative indicators and then compared with the well-known ordered logistic regression (OLR) model. The developed methodology was found to achieve an overall accuracy of 83% and attained a superior performance, as compared with the OLR model. Furthermore, the VB/GSA analysis could identify the most significant attributes to rear-end crashes INJ-S level.

Two-vehicle driver-injury severity: A multivariate random parameters logit approach

Two-vehicle crashes have been dominating all types of traffic accidents, wherein the vehicle drivers have been sustaining the highest risk of injury among all vehicle occupants. To understand the critical factors to the drivers’ injury severity of two-vehicle crashes, we employed the random parameters multinomial logit model as a data analyzing tool. To capture the unobserved heterogeneity and potential temporal instability, we combined two strategies: Bayesian random parameter logit and explicitly correlated outcomes. The random parameter logit models were validated with a nine-year large-scale dataset compiled by combining the Crash Report Sampling System (CRSS) and General Estimates Sampling (GES) databases. The results underscore the importance of explicit modeling of inter-outcome correlation, which captured the potential transition probability between adjacent levels of injury severity and improved the model’s predictability. Our model also highlighted substantial per-case and per-driver heterogeneity, which respectively explained 22.8% and 29.4% of the total variance (minor injury) and 25.4% and 24.9% of the variance (severe injury). We found that the female drivers, old (⩾65 years) drivers, unbuckled drivers, speeding drivers sustained a higher injury risk in their corresponding groups. Drivers in lighter and older vehicles suffer higher injury risks. Several other factors also considerably affect the injury severity outcomes, such as the road’s speed limit and variables that are proxies of traffic volume (intersection type, whether at the peak hours). Regarding Bayesian modeling, we observed that using weakly informative prior distribution has little effect on the parameter estimates. We also pointed to the directions to further improve the proposed modeling framework.

A Single-Center Retrospective Study on the Effects of Korean Medicine in 342 Traffic Accident Cases.

ObjectivesIn South Korea, traffic accident victims can be treated under automobile insurance coverage. Korean medicine (KM) clinics have reported the largest number of automobile insurance fee claims among medical institutions. This study investigated the status of the KM automobile insurance system in a single KM clinic.MethodsWe retrospectively surveyed the medical charts of 342 traffic accident patients treated at the Jisung KM clinic between January 2009 and June 2017.ResultsMost of the patients were men and in their 30s. The most common method of locating the clinic was an internet search. The most common traffic accident type was collision between vehicles (83.63%), with 70.76% of patients visiting during the most acute phase. The major disease codes included S434, M4836, F072, S0600, and S3350. The most frequent treatment period was within 1 month of the accident, and most patients received 10 or fewer treatments. The mean treatment duration and number of treatments were 37.68 ± 45.11 days and 11.68 ± 10.63 treatments, respectively. The initial pain numerical rating scale (NRS), 7.32 ± 0.96, decreased to 3.57 ± 1.40 at the end of treatment, with a symptom improvement score of 1.87 ± 0.60. Regarding sex, age, disease duration, location at the time of the accident, presence of additional and psychological symptoms, and chuna, there were statistically significant differences in treatment duration and number of treatments. A higher number of treatments and the longer treatment duration was associated with a higher initial NRS, lower post-treatment NRS, and better improvement score. Since the introduction of traffic accident (TA) pharmacopuncture, the rate of use of a single type of pharmacopuncture increased; however, no significant differences in treatment duration and number, NRS before and after treatment, and improvement score were observed between treatment groups before and after TA pharmacopuncture. No adverse reactions were observed for any treatment.ConclusionThis study confirmed the previous findings of a high treatment effect of KM under automobile insurance. We also observed significant correlations based on a detailed medical status, which may explain the increasing use of KM in the automobile insurance system. Additional multi-center studies in different regions are needed.

Prediction of neck injury using multiple linear regression and bumper damage: Case study for low-speed rear-end collisions

The most frequent type of traffic accident is a low-speed rear-end collision, which can damage parts of the vehicle, including the bumper, and cause neck injury to the occupants. Even in minor damage accidents, such as scratches on bumper covers, 26.3% of occupants received treatment for bodily injuries whose main symptom was neck injuries through auto insurance. This study was conducted to evaluate the potential for neck injuries in low-speed accidents. Fifty-nine low-speed rear-end impact tests were conducted, and the motion of the struck vehicle and the neck injury criterion (NIC) of the occupant according to the test conditions were predicted using multiple linear regression derived via supervised machine learning. It was confirmed that the NIC can be predicted using vehicle motion values that can be obtained through an event data recorder. The coefficients of determination of the regression equations were 0.67–0.83. Lastly, we investigated whether neck injuries can be predicted through bumper cover damage that can be checked immediately after a vehicle accident. In the case of the vehicle damage type 1/2/3 category applied to auto insurance by the Korean government, an occupant would have a very low possibility of neck injury or symptoms. No symptoms or injuries were reported in the volunteer tests conducted for this study.

Exploring injury severity of bicycle-motor vehicle crashes: A two-stage approach integrating latent class analysis and random parameter logit model

Bicycle–motor vehicle (BMV) crashes have been identified as a major type of traffic accident affecting transportation safety. In order to determine the characteristics of BMV crashes in cold regions, this study presents an analysis using police-reported data from 2015 to 2017 on BMV crashes in Shenyang, China. A two-stage approach integrating latent class analysis (LCA) and the random parameter logit (RP-logit) model is proposed to identify specific crash groups and explore their contributing factors. First, LCA was used to classify data into several homogenous clusters, and then the RP-logit model was established to identify significant factors in the whole data model and the cluster-based model from LCA. The proposed two-stage approach can maximize the heterogeneity effects both among clusters and within clusters. Results show that three significant factors in the cluster-based model are obscured by the whole data model in which male cyclists are associated with a higher risk of fatality, especially in the winter. Additionally, differences exist in the exploration of factors due to the characteristics of clusters; thus, countermeasures for specific crash groups should be implemented. This research can provide references for regulators to develop targeted policies and reduce injury severity in BMV crashes in cold regions.

Freeway accident detection and classification based on the multi-vehicle trajectory data and deep learning model

The freeway accident detection and classification have attracted much attention of researchers in the past decades. With the popularity of Global Navigation Satellite System (GNSS) on mobile phones and onboard equipment, increasing amounts of real-time vehicle trajectory data can be obtained more and more easily, which provides a potential way to use the multi-vehicle trajectory data to detect and classify an accident on freeways. The data has the advantages of low cost, high penetration, high real-time performance, and being robust to the outdoor environment. Therefore, this paper proposes a new method for accident detection and classification based on the multi-vehicle trajectory data. Different from the existing methods using GNSS positioning data, the proposed method not only uses the position information of the related vehicles but also tries to capture the development tendencies of the trajectories of accident vehicles over a period of time. A Deep Convolutional Neural Network model is developed to recognize an accident from the normal driving of vehicles and also identify the type of the accident, and the six types of traffic accidents are considered in this study. To train and test the proposed model, the simulated trajectory data is generated based on PC-Crash, including the normal driving trajectories and the trajectories before, in, and after an accident. The results indicate that the detection accuracy of the proposed method can reach up to 100%, and the classification accuracy can reach up to 95%, which both outperform the existing methods using other data. In addition, to ensure the robustness of the detection accuracy, at least 1 s of duration and 5 Hz of frequency for the trajectory data should be adopted in practice. The study will help to accurately and fastly detect an accident, recognize the accident type, and future judge who is liable for the accident.

Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types

IntroductionFreeway accidents are a leading cause of death in China, which also triggers substantial economic loss and an emotional burden to society. However, the internal mechanism of how microscopic kinetic parameters of vehicles influenced by road characteristics determine the occurrence of different types of accidents has not been explicitly studied. This research aimed to explore the “link role” of tire microscopic kinetic parameters in road characteristic variables and traffic accidents to aid in facilitating the traffic design and management, and thus to prevent traffic accident. Method: A mountain freeway in Zhejiang Province, China was used as the research object and the data used in this paper were obtained through a real-time vehicle experiment. Multiple estimation models, including the standard ordered logit (SOL) model, fixed parameters logit (FPL) model, and random parameters logit (RPL) model were established. Results: The findings show that road characteristics will affect the longitudinal kinetic characteristics of the vehicle and, consequently, map the level of risk of rear-end accidents. Driving compensation effects were also identified in this paper (i.e., the drivers tend to be more cautious in complicated driving circumstances). Another finding relating to the mountain freeway is that different tunnel characteristics (e.g., tunnel entrance and tunnel exit) have different effects on different types of traffic accidents. Practical Applications: The framework proposed in this article can provide new insight for researchers to enlarge the research subjects of both explanatory and outcome variables in accident analysis. Future research could be implemented to consider more driving conditions.

Design for AEBS Test Scenario Applying Domestic Traffic Accidents

This study is a study on the development of AEBS test scenarios for traffic accidents in Korea, and was compared and analyzed using the Traffic Accident Analysis Program. To ensure the safety of passengers and pedestrians in traffic accidents, the number of cars equipped with ADAS is increasing rapidly at all car manufacturers in each country. For traffic accidents used in this study, the domestic traffic accident database (ACCC) produced by SAMSONG was used. Domestic traffic accidents differ from overseas traffic accidents in terms of road type, signal system, driver's seat location and number of vehicles. ACCC databases, which supplemented and reinforced these differences, built a database based on the PC-CRASH program. In the study, we analyze the types of accidents to develop comparative scenarios for each type of road and collision type of traffic accidents. When the road types of traffic accidents in Korea were divided into five types and the collision types were divided into six, it was confirmed that the most types of FRONT-SIDE crashes appeared at the intersection. It is expected that the frequency of possible traffic accidents and collision types can be predicted according to the road type in the accident database, we that it can be used as an AEBS test scenario development suitable for the domestic road environment.

Identification and analysis of vehicle traces in traffic accidents

this article in view of the different types of traffic accidents of vehicle trace difference analysis, including motor vehicle accidents, accident of motor vehicles and non-motor vehicles, motor vehicles and solid content etc., combining with the factors influencing the accuracy of the vehicle trace identification results, by studying the vehicle trace identification in judging whether had collision with other vehicle, clearing the accident vehicle type, vehicle crash site, determining vehicle relative direction, clearing the specific application of hit-and-run vehicle. Its purpose is to improve the level of vehicle trace identification and to speed up the handling of traffic accidents.

Deep Learning-based Real-time Traffic Accident Type and Fault Information Provision Service

Deep Learning-based Real-time Traffic Accident Type and Fault Information Provision Service

Multicriteria Model Based on FITradeoff Method for Prioritizing Sections of Brazilian Roads by Criticality

Three of the most important objectives of the Federal Road Police (Patrol) when managing the traffic on federal roads in Brazil are to ensure that there is an efficient movement of traffic under stable conditions and minimal traffic congestion problems that the federal highways are safe and that accidents do not occur. Therefore, multiple matters are relevant for road safety and user security, such as prompt maintenance of these highways, regular monitoring of their state of conservation and their characteristics; controlling the traffic; and preventing and combatting criminal activities on the highways. However, considering the vast network and different conditions of roads, the different types of traffic accidents and their consequences, and different levels of violence on federal roads, it is essential that these roads undergo regular maintenance and regular inspections, are constantly patrolled, and are subject to continuous improvements. Therefore, defining the prioritization and criticality of roads takes on the characteristics of a multicriteria decision, given the multidimensional aspects of the risks inherent in them. Thus, this paper presents a multicriteria decision model for prioritizing road sections, based on their criticality and the risks that users face. The model was applied using the FITradeoff method, due to its flexibility and due to it requiring less cognitive effort from the decision-maker with regard to providing information regarding his/her preferences. A case study was undertaken on a set of the federal roads of the state of Pernambuco (Brazil), covering 22 different sections with different characteristics. As a result, it was possible to rank and identify the most critical sections of a highway. The use of FITradeoff gave support to decision-making on ordering the sections and also let a general analysis of the data be undertaken.

Investigation of Factors That Have Affected the Outcomes of Road Traffic Accidents on Lithuanian Roads

The purpose of this paper is to analyse the possibility for predicting the outcome of a road traffic accident concerning the traffic environment, personal traits of the traffic participant and the vehicle, i.e. aiming to answer the question whether specific values of the factors analysed to increase the likelihood of a fatal accident. The logistic regression model that allows identifying the relationship between the dependent and independent variables were used in the research. Other methods for describing and analysing categorical variables were also used alongside the logistic regression. When analysing the results, it was recognised that the odds ratio above 1 shows a higher likelihood for a representative of the category in question to be involved in a fatal accident compared to a representative of the base category. Odds ratios of likelihoods for calculation of the road traffic accident types show that the likelihood of a fatal accident is statistically significant affected by rollovers or driving into obstacles, compared to vehicular collisions. When summarising the results, it was stated that most of the factors researched have an impact on the outcome of a road traffic accident. The influence of some factors has a higher probability of resulting in a fatal accident as compared to other factors.

ESTABLISHMENT OF CAUSE AND EFFECT RELATIONSHIP BETWEEN IMPROPER ARRANGEMENT OF TRAFFIC MANAGEMENT FACILITIES AND TRAFFIC ACCIDENTS OCCURRENCE

The daunting issues arising at experts and forensic bodies when appointing and conducting comprehensive road and pavement forensic investigation and road accident analysis (forensic expert examination) are considered. Types of traffic accidents provided by the statistics of the Department of Patrol Police of Ukraine are analyzed. A number of road accidents with victims committed in conditions of the road and settlements streets unsatisfactory state is defined.
 
 More and more judicial and investigative bodies turn to forensic science institutes, in particular to KhRIFE, in order to determine whether the improper design of the road network affected the occurrence of an accident. In other words, the questions not only on the establishment of improper design of the road section where the accident occurred, but also on the existence of cause and effect relationship between the identified discrepancies and the accident are advanced for examination. The study of the above issues belongs to the forensic specialty 10.16 Road and Pavement Forensic Investigation. The demand for forensic research of improper design of the road network and the causes of accidents are constantly growing. At the same time, the practice of conducting road and pavement forensic investigation, namely the analysis of the materials provided for the study, shows that most criminal proceedings are investigated both while pre-trial investigation and in court for violation of traffic safety rules or transport operation by persons who drive vehicles. In the statistical report of the Department of Patrol Police of Ukraine, the data of road accidents are not classified as those that arose due to improper condition of roads and streets of settlements, and when conducting road and pavement forensic investigation it is established that improper design of the road network is in cause and effect relationship with the occurrence of an accident from a technical point of view.

Conditional unnecessity of head CT for whole-body CT of traffic accident victims: a pilot study.

To investigate whether head CT should be included in whole-body CT in road traffic accident victims. A review of electronic medical records identified 124 patients (81 males, 43 females; age 4 to 92 years, mean 47.7 years) involved in a road traffic accident in a 12-month period. All had undergone whole-body CT and physical and neurologic examinations. We recorded their age, sex, Glasgow Coma Scale (GCS), systolic blood pressure (SBP), the type of traffic accident, and the presence/absence of visible trauma above the clavicles (VTCs) and of acute traumatic brain injury (TBI) on CT. Statistical analyses were performed to evaluate predictors of acute TBI. Of 124 patients, 34 (27%) manifested acute TBI on CT. Univariate analysis identified their age, GCS, SBP, VTCs, and the accident type as statistically significant factors for acute TBI (p < 0.05). Multivariate analysis demonstrated VTCs, GCS score < 15, and SBP ≤ 90 mmHg were significant independent predictors of acute TBI (p = 0.001, p = 0.001, and p = 0.004, respectively); the odds ratio was 16.07 for VTCs, 14.85 for GCS score < 15, and 13.78 for SBP ≤ 90 mmHg. No patients without both decrease in GCS score and VTCs manifested acute TBI. Our pilot study showed that visible trauma above the clavicles and decrease in GCS score were highly associated with the presence of acute TBI in road traffic accident victims. In whole-body CT, a head CT may not be indicated in patients without these factors.

An Investigation into Unsafe Behaviors and Traffic Accidents Involving Unlicensed Drivers: A Perspective for Alignment Measurement.

Road traffic plays a vital role in countries’ economic growth and future development. However, traffic accidents are considered a major public health issue affecting humankind. Despite efforts by governments to improve traffic safety, the misalignment between the policy efforts and on-ground infringements, distractions and breaches reflect the regulatory failure. This paper uses the Bayesian network method to investigate unsafe behaviors and traffic accidents involving unlicensed drivers as a perspective for the regulatory alignment assessment. The findings suggest that: (1) unlicensed drivers are more likely to have unsafe driving behaviors; (2) the probability of being involved in a severe traffic accident increases when the drivers are unlicensed and decreases in the case of licensed drivers; (3) young drivers are noticeably more likely to engage in unsafe behaviors, usually leading to serious injuries and deaths, when their driving licenses are invalid; (4) women are more likely to engage in right-of-way violations and to have collisions with no serious injuries, contrary to unlicensed men drivers, who are involved in other types of traffic accidents resulting in serious injuries.

Road traffic accidents of the elderly in Chile between the years 2008-2017: A register-based descriptive study.

To determine whether Chilean elderly persons have higher rates of traffic incidents, resulting in death or injury, compared to non-older adults between 2008 and 2017. The databases of the National Traffic Safety Commission of the last ten years and the data of inhabitants according to the projections made by the National Statistics Institute were used. Effect measures and 95% confidence intervals were calculated. Older adults in Chile had 0.5 times the rate of suffering a traffic accident, 0.6 times the rate of injury, and 1.3 times the rate of dying in a traffic accident when compared against non-elderly adults. Antofagasta and Arica were the regions with the lowest and highest occurrence of death for the elderly, respectively. The leading causes of traffic accidents were the imprudence of the driver and pedestrian, which presented 1.6 and 2.6 times the death rate when compared against non-elderly adults. Of the main types of traffic accidents, only when the pedestrian was hit by a vehicle, the elderly had a higher occurrence of death (rate ratio: 2.4). The elderly presented greater vulnerability to die in a traffic accident. Substantial differences are seen across different regions. Future research should address the characteristics of the users, the strategies that could help them to have better interaction, and promote health with a focus on road safety in the elderly.

Pediatric Maxillofacial Trauma Patterns Among Different Types of Road Traffic Accidents

Traffic accidents are one of the most leading cause of maxillofacial trauma. Defining the bone fracture patterns in different type of traffic accidents may enable for appropriate surgical approach. This study research the pattern of pediatric maxillofacial fractures in terms of different type of traffic road accidents and age groups. The data on road traffic accidents that caused maxillofacial trauma between May 2014 and January 2019 was gained from the University of Gaziantep hospital data system. Road accident type, age groups, and the type of fractures were the evaluation parameters. A total of 61 patients meet the criteria and 41 of those were boys. The midface bones of orbital, maxillar, and frontal are the most fractured structures with a total number of 48. The least fractured bone was mandibula with a number of 4. The comparisons of these fractures among the different type of road traffic accidents and age groups were found to be statistically significant. Car-pedestrian and car-passengers type of road traffic accident mostly seem to cause midface fractures at the primary and secondary school-aged children.