Background: In China, despite the decrease in average road traffic fatalities per capita, the fatality rate and injury rate have been increasing until 2015. Purpose: This study aims to analyze the ...road traffic accident severity in China from a macro viewpoint and various aspects and illuminate several key causal factors. From these analyses, we propose possible countermeasures to reduce accident severity. Method: The severity of traffic accidents is measured by human damage (HD) and case fatality rate (CFR). Different categorizations of national road traffic census data are analyzed to evaluate the severity of different types of accidents and further to demonstrate the key factors that contribute to the increase in accident severity. Regional data from selected major municipalities and provinces are also compared with national traffic census data to verify data consistency. Results: From 2000 to 2016, the overall CFR and HD of road accidents in China have increased by 19.0% and 63.7%, respectively. In 2016, CFR of freight vehicles is 33.5% higher than average; late-night accidents are more fatal than those that occur at other periods. The speeding issue is severely becoming worse. In 2000, its CFR is only 5.3% higher than average, while in 2016, the number is 42.0%. Conclusion and practical implementation: A growing trend of accident severity was found to be contrasting to the decline of road traffic accidents. From the analysis of casual factors, it was confirmed that the release way of the impact energy and the protection worn by the victims are key variables contributing to the severity of road traffic accidents.
•We adopted two indicators, Human Damage and Case Fatality Rate, to describe the severity of accidents.•The two indicators reveal the increasing traffic accident severity in China from a new perspective.•Our analysis on accident severity provides insights and countermeasures to reduce fatalities in road traffic accidents.
Human factors have been defined by the International Civil Aviation Organization (ICAO) as “about people in their living and working situations; about their relationship with machines, with ...procedures and with the environment about them; and about their relationships with other people (at work)”. Human factors contribute to approximately 75% of aircraft accidents and incidents. As such, understanding their influence is essential to improve safety in the aviation industry. This study examined the different human factors causations in a random sample of over 200 commercial air transport accidents and incidents from 2000 to 2016. The main objective of this study was to identify the principal human factor contributions to aviation accidents and incidents. An exploratory research design was utilised. The qualitative data were recorded in a database, and were coded into categories about the flights (including date, manufacturer, carrier, state of occurrence, etc). These categories were then analysed using Chi-Squared tests to determine which were statistically significant in terms of having an influence on the accidents/incidents. The most significant human factor was found to be situational awareness followed by non-adherence to procedures. In addition, charter operations proved to have a significantly higher rate of human factor related occurrence as compared to other type of operations. A significant finding was that Africa has a high rate of accidents/incidents relative to the amount of traffic and aircraft movements. These findings reflect some of the more noteworthy incidents that have received significant media attention, including Air Asia 8501 on the 28th of December 2014, TransAsia Airways 235 on the 4th of February 2015, and Air France 447 on the 1st of June 2009; these accidents resulted in a significant loss of lives where situational awareness and non-adherence to procedures were significant contributing factors.
Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities ...and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities.
•Significant relationships exist between safety system practices and accident rates.•Safety management system characteristics predict worker engagement levels.•Worker engagement levels predict ...accident rates.•Engagement levels act as mediators between safety systems and performance outcomes.•To reduce accidents, safety systems should include worker engagement components.
The overall research objective was to theoretically and empirically develop the ideas around a system of safety management practices (ten practices were elaborated), to test their relationship with objective safety statistics (such as accident rates), and to explore how these practices work to achieve positive safety results (accident prevention) through worker engagement.
Data were collected using safety manager, supervisor and employee surveys designed to assess and link safety management system practices, employee perceptions resulting from existing practices, and safety performance outcomes.
Results indicate the following: there is a significant negative relationship between the presence of ten individual safety management practices, as well as the composite of these practices, with accident rates; there is a significant negative relationship between the level of safety-focused worker emotional and cognitive engagement with accident rates; safety management systems and worker engagement levels can be used individually to predict accident rates; safety management systems can be used to predict worker engagement levels; and worker engagement levels act as mediators between the safety management system and safety performance outcomes (such as accident rates).
Even though the presence of safety management system practices is linked with incident reduction and may represent a necessary first-step in accident prevention, safety performance may also depend on mediation by safety-focused cognitive and emotional engagement by workers. Thus, when organizations invest in a safety management system approach to reducing/preventing accidents and improving safety performance, they should also be concerned about winning over the minds and hearts of their workers through human performance-based safety management systems designed to promote and enhance worker engagement.
Despite preventive measures and initiatives, road traffic accidents are on the rise in the Kingdom of Saudi Arabia. This study aimed to investigate the emergency medical service unit's response to ...RTA by socio-demographic and accident-related variables in the Kingdom of Saudi Arabia. This retrospective survey included Saudi Red Crescent Authority data on road traffic accidents between 2016 and 2020. As part of the study, information on sociodemographic characteristics (e.g., age, sex, and nationality), accident-related data (type and place of the accident), and response time to road traffic accidents were extracted. Our study included 95,372 cases of road traffic accidents recorded by the Saudi Red Crescent Authority in the Kingdom of Saudi Arabia between 2016 and 2020. Descriptive analyses were performed to explore the emergency medical service unit's response time to road traffic accidents, and linear regression analyses were performed to investigate the predictors of response time. Most of the road traffic accident cases were among males (59.1%), and the age group of 25-34 years accounted for about a quarter (24.3%), while the mean age of the road traffic accident cases was 30.13 (±12.86) years. Among the regions, the capital city of Riyadh experienced the highest proportion of road traffic accidents (25.3%). In most road traffic accidents, the mission acceptance time was excellent (0-60 s; 93.7%), movement duration was excellent (<120 s; 91.1%), reaching site duration was excellent (<12 min; 57.9%), treatment start time was excellent (<120 s; 76.4%), duration at the scene was poor (>15 min; 40.8%), reaching hospital duration was good (30-60 min; 52.7%), and in-hospital duration was poor (>15 min; 44.1%). Regions, places and types of accidents, age, gender, and nationality of victims were significantly associated with different parameters of response time. Excellent response time was observed in most of the parameters except the duration at the scene, reaching hospital duration, and in-hospital duration. Apart from the initiatives to prevent road traffic accidents, policymakers should focus on strategies to improve accident response time to save lives.
Bicycles are employed as means of transportation across various age groups, from young students to the elderly, for work, education, health, and leisure trips. Despite not achieving high speeds, ...bicyclists remain vulnerable to severe and even fatal injuries when they are involved in traffic accidents. Although the rising awareness of ecological issues and traffic law enforcement mean that cyclists are increasingly susceptible to road traffic crashes and injuries. Injuries resulting from a traffic accident involving cyclists can show distinct and specific characteristics depending on the manner of occurrence. The aim of this study is to provide a systematic review of the literature on injuries sustained in cyclists involved in road accidents describing and analysing elements useful for forensic assessment. The literature search was performed using PubMed, Scopus, and Web of Science from January 1970 to March 2023. Eligible studies have investigated issues of interest to forensic medicine about traffic accidents involving bicycles. A total of 128 studies satisfied the inclusion criteria and were categorized and analyzed according to the anatomical regions of the body affected (head, neck, thoraco-abdominal, and limb injuries), and the assessment of lesions in reconstruction of the bicycle accident was examined and discussed. This review highlights that injuries resulting from a traffic accident involving cyclists can show distinct and specific characteristics depending on the manner of occurrence and the energy levels involved in the crash. The assessment of injuries offers valuable insights that integrated with circumstantial and engineering data perform the reconstruction of accident dynamics.
•Bicycle riders are susceptible to high and low-energy impacts, each associated with distinct fatal injury patterns.•The unique injury patterns found in bike riders can relate to helmet usage or not and to bike structural components.•Features of the injuries help in reconstructing the accident dynamics, combined with circumstantial and engineering data.
Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs ...is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one--after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.
Construction accidents can easily cause massive casualties and property losses. This research uses machine learning technique to analyze 16 critical factors and assess the impact of diverse ...combinations of factors on the performance of predicting the severity of construction accidents. The prediction is carried out with eight algorithms: Logistic regression, Decision tree, Support vector machine, Naive Bayes, K-nearest neighbor, Random forest, Multi-Layer Perceptron and AutoML. The results show that (1) Based on 16 accident factors, Naive Bayes and Logistics regression achieve the best F1-Score of 78.3 % on raw data set. (2) With AutoML method, severity classification can achieve an average F1-Score of 84 %. (3) The analysis of the confusion matrix shows that the subjective classification of the original data and specific unusual accidents are the sources of misprediction. (4) The “Type of accident” and “Accident reporting and handling” are the most critical factors and “Emergency management” and “Safety training” are important subsystems, both of which greatly affect the severity of the accident. (5) Based on the Decision tree, a set of assessment rules for the severity of construction accidents can be extracted. The prediction models and conclusions obtained from this study can be used to enhance the experience of safety professionals in urban construction and to make the safety intervention measures more efficient.
•We use HFACS to identify contributory factors involved in 39 collisions.•MCA and hierarchical clustering reveal three patterns of factors.•Collisions in restricted waters are linked to communication ...and BRM deficiencies.•One class of collisions shows deficiencies at every level of the system.•A third class is characterised by non-compliance with the Safety Management System.
Over the last decade, the shipping industry has implemented a number of measures aimed at improving its safety level (such as new regulations or new forms of team training). Despite this evolution, shipping accidents, and particularly collisions, remain a major concern. This paper presents a modified version of the Human Factors Analysis and Classification System, which has been adapted to the maritime context and used to analyse human and organisational factors in collisions reported by the Marine Accident and Investigation Branch (UK) and the Transportation Safety Board (Canada).
The analysis shows that most collisions are due to decision errors. At the precondition level, it highlights the importance of the following factors: poor visibility and misuse of instruments (environmental factors), loss of situation awareness or deficit of attention (conditions of operators), deficits in inter-ship communications or Bridge Resource Management (personnel factors). At the leadership level, the analysis reveals the frequent planning of inappropriate operations and non-compliance with the Safety Management System (SMS). The Multiple Accident Analysis provides an important finding concerning three classes of accidents. Inter-ship communications problems and Bridge Resource Management deficiencies are closely linked to collisions occurring in restricted waters and involving pilot-carrying vessels. Another class of collisions is associated with situations of poor visibility, in open sea, and shows deficiencies at every level of the socio-technical system (technical environment, condition of operators, leadership level, and organisational level). The third class is characterised by non-compliance with the SMS.
This study shows the importance of Bridge Resource Management for situations of navigation with a pilot on board in restricted waters. It also points out the necessity to investigate, for situations of navigation in open sea, the masters’ decisions in critical conditions as well as the causes of non-compliance with SMS.
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