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Scientific Reports Dec 2022Predicting traffic accident duration is necessary for ensuring traffic safety. Several attempts have been made to achieve high prediction accuracy, but researchers have...
Predicting traffic accident duration is necessary for ensuring traffic safety. Several attempts have been made to achieve high prediction accuracy, but researchers have not considered traffic accident text data in much detail. The limited text data of the first report on an incident describes the characteristics of an accident that are initially available. This paper uses text data fusing and ensemble learning algorithms to build a model to predict an accident's duration, and a preprocessing scheme of accident duration text data is established. Next, the random forest (RF) algorithm is applied to select feature variables of text data related to the traffic incident duration. Last, a text feature vector is introduced to models such as decision tree, k nearest neighbor, support vector regression, random forest, Gradient Boosting Decision Tree, and Xtreme Gradient Boosting. Our results show that the improved RF model has good prediction accuracy with RMSE, MAPE and R. From this, the textual factors important to determining the duration of the accident are identified. Further, we investigated that the cumulative importance of 60% is sufficient for traffic accident prediction using text data. These results provide insights into minimizing traffic congestion related to accidents and contribute to the input optimization in text prediction.
Topics: Accidents, Traffic; Data Mining; Algorithms; Machine Learning
PubMed: 36509866
DOI: 10.1038/s41598-022-25988-4 -
International Journal of Environmental... Feb 2023Subway operation safety management has become increasingly important due to the severe consequences of accidents and interruptions. As the causative factors and... (Review)
Review
Subway operation safety management has become increasingly important due to the severe consequences of accidents and interruptions. As the causative factors and accidents exhibit a complex and dynamic interrelationship, the proposed subway operation accident causation network (SOACN) could represent the actual scenario in a better way. This study used the SOACN to explore subway operation safety risks and provide suggestions for promoting safety management. The SOACN model was built under 13 accident types, 29 causations and their 84 relationships based on the literature review, grounded theory and association rule analysis, respectively. Based on the network theory, topological features were obtained to showcase different roles of an accident or causation in the SOACN, including degree distribution, betweenness centrality, clustering coefficient, network diameter, and average path length. The SOACN exhibits both small-world network and scale-free features, implying that propagation in the SOACN is fast. Vulnerability evaluation was conducted under network efficiency, and its results indicated that safety management should focus more on fire accident and passenger falling off the rail. This study is beneficial for capturing the complex accident safety-risk-causation relationship in subway operations. It offers suggestions regarding safety-related decision optimization and measures for causation reduction and accident control with high efficiency.
Topics: Accidents; Algorithms; Cluster Analysis; Railroads; Safety Management
PubMed: 36834080
DOI: 10.3390/ijerph20043386 -
Sensors (Basel, Switzerland) Mar 2022An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost...
An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost doubles due to bad visibility as well as road conditions. If an accident happens, especially in bad weather, it is important to inform approaching vehicles about it. Otherwise, there might be another accident, i.e., a multiple-vehicle collision (MVC). If the Emergency Operations Center (EOC) is not informed in a timely fashion about the incident, fatalities might increase because they do not receive immediate first aid. Detecting humans or animals would undoubtedly provide us with a better answer for reducing human fatalities in traffic accidents. In this research, an accident alert light and sound (AALS) system is proposed for auto accident detection and alerts with all types of vehicles. No changes are required in non-equipped vehicles (nEVs) and EVs because the system is installed on the roadside. The idea behind this research is to make smart roads (SRs) instead of equipping each vehicle with a separate system. Wireless communication is needed only when an accident is detected. This study is based on different sensors that are used to build SRs to detect accidents. Pre-saved locations are used to reduce the time needed to find the accident's location without the help of a global positioning system (GPS). Additionally, the proposed framework for the AALS also reduces the risk of MVCs.
Topics: Accidents, Traffic; First Aid; Geographic Information Systems; Weather
PubMed: 35336248
DOI: 10.3390/s22062077 -
Yakugaku Zasshi : Journal of the... 2017Sudden illness while driving has been identified as a major cause of vehicle collisions, accounting for approximately 1 in 10 collisions. Because most drivers who... (Review)
Review
Sudden illness while driving has been identified as a major cause of vehicle collisions, accounting for approximately 1 in 10 collisions. Because most drivers who experience sudden illnesses while driving do not perform avoidance maneuvers, the improvement of drivers' health is being promoted as a traffic safety strategy. Although stroke, heart disease, and epilepsy are common causes of sudden illness, common symptoms, such as abdominal cramps, vertigo, and syncope can also cause problems during driving. We found that regular referral to physicians was significantly less common among drivers who experienced health-related vehicle collisions or incidents. Inadequate control of chronic disease might lead to unusual symptoms and the onset of major attacks. Medications are prescribed to patients to relieve their symptoms and/or bring their diseases under control. However, pharmacists and doctors should ensure that patients are treated with appropriate medications to avoid drivers being distracted due to adverse reactions to medications. The author suggests that it is important to keep drivers in good health and administer appropriate medications if necessary. Both pharmacists and doctors should warn drivers that sudden illness or medication-associated distractions can cause vehicle collisions. Such interventions might contribute to reducing the frequency of sudden illness-related vehicle collisions.
Topics: Accident Prevention; Accidents, Traffic; Automobile Driving; Drug-Related Side Effects and Adverse Reactions; Epilepsy; Health Promotion; Heart Diseases; Humans; Medication Adherence; Patient Education as Topic; Pharmacists; Physicians; Stroke; Vertigo
PubMed: 28250326
DOI: 10.1248/yakushi.16-00237-1 -
Sleep Medicine Reviews Dec 2018Estimates in developed countries of the extent to which fatigue contributes to road accidents range from as low as 5% to as high as 50% of all accidents. Compared with... (Review)
Review
Estimates in developed countries of the extent to which fatigue contributes to road accidents range from as low as 5% to as high as 50% of all accidents. Compared with other causes of road accidents (e.g., speeding, drink-driving), the variability in these estimates is exceptionally high and may be indicative of the difficulty in determining the likelihood of fatigue as a cause of road accidents. This review compares differences in the way road accidents are classified as fatigue-related (or not) by expert panels and road safety regulators, highlighting conflicting conceptual approaches, lack of consistency, and the poor psychometric qualities of classification rules used across jurisdictions. In order to facilitate future research, the review then proposes a new theoretical approach and a potentially more logical accident 'taxonomy'. A putative accident 'taxonomy' is proposed using two dimensions: (1) estimating the likelihood that a driver was fatigued at the time of the accident, and (2) estimating the degree to which accident phenomenology is consistent with fatigue-related error. This 'taxonomy' could assist accident investigators and road safety regulators to more reliably quantify the contribution of fatigue to road accidents, and may also assist researchers and regulators in the post-hoc interrogation of existing accident databases to better determine the relative incidence of fatigue-related road accidents.
Topics: Accidents, Traffic; Automobile Driving; Fatigue; Humans; Psychometrics; Sleep Deprivation
PubMed: 30274744
DOI: 10.1016/j.smrv.2018.08.006 -
Deutsches Arzteblatt International Feb 2021E-scooter sharing systems were initiated in Hamburg in June 2019. The number of persons injured in Hamburg in e-scooter accidents rose thereafter. The goal of this study...
BACKGROUND
E-scooter sharing systems were initiated in Hamburg in June 2019. The number of persons injured in Hamburg in e-scooter accidents rose thereafter. The goal of this study was to determine the typical accident mechanisms and injury patterns after e-scooter accidents in Germany, and to compare these with bicycle accidents.
METHODS
In a retrospective study, accidents with e-scooters and bicycles that occurred from June 2019 to June 2020 were registered and analyzed with respect to demography, accident mechanisms, diagnostics, patterns of injury, emergency medical care, operations, and inpatient hospitalizations.
RESULTS
89 persons sustained e-scooter accidents (mean age 33.9 years, standard deviation [SD] 14 years); 435 persons who sustained bicycle accidents (mean age 42.5 years, SD 17 years) served as a comparison group. E-scooter accidents more commonly occurred at night (37% versus 14%), and 28% of the persons who sustained them were under the influence of alcohol (cyclists: 6%). 54% of the injured e-scooter riders suffered trauma to the head or face; 14% had a severe head injury and 16% had a severe facial injury. Fractures of the upper limbs were more common than fractures of the lower limbs (18% versus 6%). On initial assessment in the emergency room, injured cyclists were more frequently classified as needing immediate treatment than injured e-scooter riders (7% versus 1%).
CONCLUSION
The head, face, and upper limbs are the most commonly affected parts of the body in e-scooter accidents. Compared to bicycle accidents, e-scooter accidents more commonly occur on weekends and in association with alcohol. From a medical point of view, abstaining from alcohol consumption and wearing a helmet when using an e-scooter is strongly recommended.
Topics: Accidents; Accidents, Traffic; Adult; Craniocerebral Trauma; Germany; Head Protective Devices; Humans; Retrospective Studies
PubMed: 33879309
DOI: 10.3238/arztebl.m2021.0019 -
BMC Public Health Jan 2024With the rapid development of China's chemical industry, although researchers have developed many methods in the field of chemical safety, the situation of chemical...
BACKGROUND
With the rapid development of China's chemical industry, although researchers have developed many methods in the field of chemical safety, the situation of chemical safety in China is still not optimistic. How to prevent accidents has always been the focus of scholars' attention.
METHODS
Based on the characteristics of chemical enterprises and the Heinrich accident triangle, this paper developed the organizational-level accident triangle, which divides accidents into group-level, unit-level, and workshop-level accidents. Based on 484 accident records of a large chemical enterprise in China, the Spearman correlation coefficient was used to analyze the rationality of accident classification and the occurrence rules of accidents at different levels. In addition, this paper used TF-IDF and K-means algorithms to extract keywords and perform text clustering analysis for accidents at different levels based on accident classification. The risk factors of each accident cluster were further analyzed, and improvement measures were proposed for the sample enterprises.
RESULTS
The results show that reducing unit-level accidents can prevent group-level accidents. The accidents of the sample enterprises are mainly personal injury accidents, production accidents, environmental pollution accidents, and quality accidents. The leading causes of personal injury accidents are employees' unsafe behaviors, such as poor safety awareness, non-standard operation, illegal operation, untimely communication, etc. The leading causes of production accidents, environmental pollution accidents, and quality accidents include the unsafe state of materials, such as equipment damage, pipeline leakage, short-circuiting, excessive fluctuation of process parameters, etc. CONCLUSION: Compared with the traditional accident classification method, the accident triangle proposed in this paper based on the organizational level dramatically reduces the differences between accidents, helps enterprises quickly identify risk factors, and prevents accidents. This method can effectively prevent accidents and provide helpful guidance for the safety management of chemical enterprises.
Topics: Humans; Accidents; Chemical Hazard Release; Environmental Pollution; Risk Factors; Safety Management
PubMed: 38166879
DOI: 10.1186/s12889-023-17510-w -
Lakartidningen Nov 2019In Sweden equestrian sport activities are the sixth most popular sport and predominantly women and girls are engaged. Horses are prey animals and humans are predators,... (Review)
Review
In Sweden equestrian sport activities are the sixth most popular sport and predominantly women and girls are engaged. Horses are prey animals and humans are predators, and the two species therefore act in completely different ways. It is well known that accidents can occur when horses and humans interact. Literature from different countries in the world reveals that most accidents happen to females and also children are at risk. The most common accident is when a rider falls from a horse, but also unmounted humans are at risk for injuries. Most of the injuries are uncomplicated, but there are several reports of serious injuries and death. Prevention of injuries is very important. Education about how horses behave and react in different situations and how to communicate with horses according to Natural Horsemanship strategies make the horses less inclined to escape. The effectiveness of helmets in preventing serious head injury has been well established.
Topics: Accident Prevention; Accidents; Adolescent; Adult; Animals; Athletic Injuries; Child; Horses; Humans; Protective Devices; Sweden; Wounds and Injuries; Young Adult
PubMed: 31742653
DOI: No ID Found -
International Journal of Environmental... Feb 2022A metro collapse accident is the main type of metro construction accidents. How to scientifically analyze the key cause factors and their interaction coupling mechanism...
A metro collapse accident is the main type of metro construction accidents. How to scientifically analyze the key cause factors and their interaction coupling mechanism of the existing metro collapse accidents is crucial to reduce the occurrence of metro collapse. Based on the Fault Tree Analysis (FTA) and the Behavior security "2-4" Model (24Model), the FTA-24Model accident cause analysis framework was constructed by combing their respective characteristics. To be more specific, a logical analysis program was developed to analyze the accident causes by the four-module analysis method. An empirical study was carried out by taking the "12.1" major cave-in accident at the construction site of the Metro Line 11 in Guangzhou as an example. Compared with the case accident report, the FTA-24Model framework analysis method can not only systematically deduce the logical relationship between the accident causes and provides a panorama of the accident cause chain and its evolution process, but also identify the key causes of accidents and their coupling risk effects. For a metro construction accident, this method can not only effectively investigate the accident causes, but also provide a reference for the formulation of prevention strategies.
Topics: Accidents; Accidents, Occupational
PubMed: 35206287
DOI: 10.3390/ijerph19042102 -
International Journal of Environmental... Jun 2019In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the... (Review)
Review
In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the e-bike risky riding behavior from three aspects: the characteristics and causes of e-bike accidents, the characteristics of users' traffic behavior, and the prevention and intervention of traffic accidents. The analysis results show that the existing research methods on risky riding behavior of e-bikes mainly involve questionnaire survey methods, structural equation models, and binary probability models. The illegal occupation of motor vehicle lanes, over-speed cycling, red-light running, and illegal manned and reverse cycling are the main risky riding behaviors seen with e-bikes. Due to the difference in physiological and psychological characteristics such as gender, age, audiovisual ability, responsiveness, patience when waiting for a red light, congregation, etc., there are differences in risky cycling behaviors of different users. Accident prevention measures, such as uniform registration of licenses, the implementation of quasi-drive systems, improvements of the riding environment, enhancements of safety awareness and training, are considered effective measures for preventing e-bike accidents and protecting the traffic safety of users. Finally, in view of the shortcomings of the current research, the authors point out three research directions that can be further explored in the future. The strong association rules between risky riding behavior and traffic accidents should be explored using big data analysis. The relationships between risk awareness, risky cycling, and traffic accidents should be studied using the scales of risk perception, risk attitude, and risk tolerance. In a variety of complex mixed scenes, the risk degree, coupling characteristics, interventions, and the coupling effects of various combination intervention measures of e-bike riding behaviors should be researched using coupling theory in the future.
Topics: Accident Prevention; Accidents, Traffic; Adult; Awareness; Bicycling; Electricity; Female; Humans; Male; Motorcycles; Probability; Risk-Taking; Safety; Young Adult
PubMed: 31261838
DOI: 10.3390/ijerph16132308