Unsafe Behavior Research Articles

Modeling collision risk for unsafe lane-changing behavior: A lane-changing risk index approach

The lane-changing maneuvers are challenging and contributes to traffic accidents and crashes. They are complicated task that often lead to an increased risk of vehicle collisions and deteriorates traffic flow. This paper proposes a vehicle collision risk model based on vehicle lane-changing movement for autonomous vehicles using the probability threat assessment approach and the lane-changing risk index (LCRI). Given two vehicles, A and B, we first estimate the movements of vehicle B using a GPS device, which accurately identifies the vehicle while changing lanes. We then evaluate the collision risks between vehicles using the probabilistic assessment approach to determine whether a collision event occurs at a later stage. Then, we propose the LCRI, which aims to identify the risk level associated with vehicle lane-changing movement and determine the severity of the crash level. In this study, the HighD vehicle trajectory dataset is used to obtain accurate traffic information and help us investigating collision risks among vehicles. The results show that the proposed model can identify the risks of collision ahead of time. Furthermore, the random parameter ordered logit (RPOL) with heterogeneity model achieves better performance and provides a good fit model and measures for improving traffic safety.

Analyzing the Unsafe Behaviors of Frontline Construction Workers Based on Structural Equation Modeling

The unsafe behavior of frontline workers at construction sites is the most important cause of construction accidents. This study proposed a comprehensive model of frontline workers’ unsafe behaviors based on a systems perspective and used structural equation modeling (SEM) to explore the influence mechanisms between the objective conditions (e.g., work environment, work climate, and task complexity), safety management (e.g., safety education and training, safety reward and punishment regulations, safety inspection, safety technology disclosure, and safety warning signs), group influence (propagation of unsafe behaviors among workers), personal perception (subjective judgment of operators on their safety knowledge and skills), and unsafe behaviors. Data from 460 frontline workers were collected through questionnaires and the correlation hypotheses were tested using SPSS 26.0 and Amos 26.0 software. The following conclusions were obtained: (1) objective conditions directly positively influence safety management, group influence, and personal perception but indirectly negatively influence unsafe behavior; (2) safety management not only directly positively affects personal perception but also directly negatively affects unsafe behavior. However, the direct effect of safety management on group influence is not significant; (3) group influence has a direct positive effect on unsafe behavior, but the direct effect on personal perception is not significant; (4) the direct effect of personal perception on unsafe behavior is insignificant. These findings can be used as preliminary data to guide decision makers or managers in construction companies to develop reasonable management plans to curb the unsafe behaviors of frontline workers.

Study on Service Quality and Priorities of Pedestrian Path Development in Malioboro Area to Support Sustainable Urban Mobility

This article aims to study the service quality and prioritize the development of pedestrian paths in the Malioboro area to support sustainable urban mobility. Malioboro is one of the commercial and tourist centers in Yogyakarta city, historically known for its pedestrian-friendly pathways. Challenges such as pedestrian circulation issues, inadequate pedestrian facilities, conflicts between pedestrians and motorized vehicles, and problems with drop-off and stop zones for less organized transportation contribute to the low service quality in the Malioboro area. This study adopts a quantitative approach using direct observation and a questionnaire distributed to 132 pedestrian path users. The collected data is analyzed using descriptive analysis techniques and Importance Performance Analysis (IPA) to understand the existing conditions, quality, and priority of pedestrian path services in the Malioboro area. The study results indicate that the service quality of pedestrian paths in Malioboro faces several challenges. Factors such as motor vehicle traffic density, lack of quality pedestrian infrastructure, and pedestrians' unsafe behavior contribute to the low service quality. The pedestrian satisfaction analysis using the Customer Satisfaction Index (CSI) revealed a satisfaction rate of 69.19%, categorizing it as "adequate." However, improvements in pedestrian path services are necessary based on the significance of the Malioboro area and its high appeal to pedestrians. The goal is to achieve good or even perfect satisfaction in terms of service quality. An analysis of interests was conducted, resulting in the identification of four quadrants with distinct characteristics. These quadrants serve as a valuable guide for enhancing the quality of pedestrian path services. Quadrant 1 encompasses attributes such as crossing facilities, CCTV surveillance, protection from weather conditions (rain, heat, cold), and parking convenience. Despite their high importance, these variables received low satisfaction ratings from respondents. Enhancing both the quantity and quality of these variables is crucial. Policymakers should pay special attention to these four variables and implement concrete measures to surpass the "adequate" assessment and attain better pedestrian service satisfaction. By implementing these measures, it is expected that Malioboro can serve as an example in supporting sustainable urban mobility by providing good and safe services for pedestrians. This study can also provide guidance and input for the government and relevant stakeholders in decision-making regarding pedestrian path infrastructure development in similar areas.

Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features

Ensuring the safety of autonomous vehicles (AVs) is of utmost importance, and testing them in simulated environments is a safer option than conducting in-field operational tests. However, generating an exhaustive test suite to identify critical test scenarios is computationally expensive as the representation of each test is complex and contains various dynamic and static features, such as the AV under test, road participants (vehicles, pedestrians, and static obstacles), environmental factors (weather and light), and the road’s structural features (lanes, turns, road speed, etc.). In this paper, we present a systematic technique that uses Instance Space Analysis (ISA) to identify the significant features of test scenarios that affect their ability to reveal the unsafe behaviour of AVs. ISA identifies the features that best differentiate safety-critical scenarios from normal driving and visualises the impact of these features on test scenario outcomes (safe/unsafe) in 2 D . This visualisation helps to identify untested regions of the instance space and provides an indicator of the quality of the test suite in terms of the percentage of feature space covered by testing. To test the predictive ability of the identified features, we train five Machine Learning classifiers to classify test scenarios as safe or unsafe. The high precision, recall, and F1 scores indicate that our proposed approach is effective in predicting the outcome of a test scenario without executing it and can be used for test generation, selection, and prioritisation.

The impact of safety culture dimensions on workplace accidents: an application in the Moroccan automotive industry

Introduction: There is a general accord that safety culture is multidimensional. but limited research and publications are there about those dimensions. Almost one-third of the publications presenting safety culture definitions do not discuss the safety culture dimension nor the relationship between the safety culture dimensions and workplace accidents. To further understand the factors influencing this correlation. it appeared appropriate in this situation to investigate the relationship between the safety culture dimensions and occupational accidents. This study aims to identify the safety culture dimensions in the automotive industry in Morocco and its relations with occupational accidents. Methods: A study was done with 35 of the largest automotive companies in Morocco. the effects of Safety Culture dimensions on workplace accidents in the automotive industry were examined based on the literature review. and a measurement questionnaire that was created with a sample of numerous automotive workers for 2 months in the winter semester of November and December 2022. Results: The results highlight that the safety culture dimensions have a strong influence on avoiding accidents especially the safety culture's dimensions which present 40%. In addition, the COVID-19 period had a big impact on the number of occupational accidents in the automotive sector. 40.54% of these occupational accidents occurred between 2020 and 2022. Conclusion: Based on the results gained from the Questionnaire the common safety culture dimensions are employees’ attitudes or unsafe behaviors, lack of staff participation, and inadequate supervision.

Factors Influencing Unsafe Work Behavior In Construction Work For The Construction Of The Upt Vertical Hospital In Surabaya By Pt Wijaya Karya, Building Division

This research has the purpose to determine the influence of knowledge, supervision, facility availability, and management commitment as moderators on unsafe behavior among workers in the Construction Project of UPT Vertical Hospital Surabaya. The research used a Structural Equation Model (SEM) approach with the measurement model using the SmartPLS version 3.2.9 program. The research results indicate that knowledge, supervision, and facilities can influence the unsafe behavior of workers, and management commitment cannot influence knowledge, supervision, and facilities on the unsafe behavior of workers in the construction of the UPT Vertical Surabaya Hospital.

Relationship Between General Health Domains and Pedestrian Safety Behavior: A Cross-Sectional Study

Background: Traffic injuries are one of the top ten leading causes of mortality worldwide that impose a substantial burden on the health and economy of nations. Among all traffic users, pedestrians are the most vulnerable, accounting for a significant share of injuries and fatalities resulting from traffic accidents. Numerous factors contribute to pedestrian traffic accidents, with unsafe pedestrian behavior being one of the most critical factors. Objectives: Various factors can influence the traffic behavior of pedestrians. Therefore, this study aimed to examine the relationship between demographic characteristics, the general health status of pedestrians, and their traffic behavior. Methods: In this cross-sectional study, after obtaining ethical approval and participants’ consent, data were collected from eligible individuals. The data included participants’ demographic information and their responses to the 28-question General Health Questionnaire (GHQ-28) and the Pedestrian Behavior Questionnaire (PBQ). Linear regression analysis was performed to assess the relationships between demographic variables, general health domains, and the total score obtained from the PBQ. Results: Among the 1 421 participants in the study, 720 (50.7%) were female and 1 310 (92.2%) were under the age of 60. In terms of marital status, 979 individuals were married (69.5%), and 368 (26.2%) were single. The overall score of the general health of the participants was high in 623 individuals (43.8%), moderate in 666 individuals (46.9%), low in 122 individuals (8.6%), and very low in 10 individuals (0.7%). The mean (standard deviation) score of pedestrian behavior was also 57.89 (±11.9). There was a significant relationship between female gender, age of 15-29 years, being single, and domains of general health (somatic symptoms, anxiety assessment, social dysfunction assessment, and depression assessment) with the overall score of pedestrian behavior. Additionally, a significant relationship was found between the overall score of the participants’ general health and the overall score of their pedestrian behavior. Conclusions: There is a significant relationship between demographic characteristics and various domains of the general health of pedestrians and their behavior. Paying attention to this issue in policymaking and conducting further studies to identify other factors affecting pedestrian traffic behavior to prevent traffic injuries seems necessary.

Analysing the Impact of Human Error on the Severity of Truck Accidents through HFACS and Bayesian Network Models

Truck accidents are a prevalent global issue resulting in substantial economic losses and human lives. One of the principal contributing factors to these accidents is driver error. While analysing human error, it is important to thoroughly examine the truck’s condition, the drivers, external circumstances, the trucking company, and regulatory factors. Therefore, this study aimed to illustrate the application of HFACS (Human Factor Classification System) to examine the causal factors behind the unsafe behaviors of drivers and the resulting accident consequences. Bayesian Network (BN) analysis was adopted to discern the relationships between failure modes within the HFACS framework. The result showed that driver violations had the most significant influence on fatalities and multiple-vehicle accidents. Furthermore, the backward inference with BN showed that the mechanical system malfunction significantly impacts driver operating error. The result of this analysis is valuable for regulators and trucking companies striving to mitigate the occurrence of truck accidents proactively.

Willingness to accept metaverse safety training for construction workers based on extended UTAUT.

Safety training (ST) is essential in avoiding unsafe behavior of construction workers. With the rise of metaverse technology, metaverse safety training (MST) has gradually become a new model to guide construction workers in safety production. An in-depth study of construction workers' willingness to accept the metaverse safety training (WAMST) helps improve its effectiveness, but studies need to pay more attention to it. This study constructs a conceptual model of WAMST for construction workers, and the influencing factors of WAMST are explained based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT). It established a Structural equation modeling to verify the relationship between influencing factors. An example verifies the feasibility of the model. The results show that the framework significantly contributes to the willingness of construction workers to participate and improves safety awareness. Specifically, performance expectancy, effort expectancy, social influence, and convenient conditions significantly affect the construction workers' willingness to accept. Convenient conditions have a direct effect on actual behavior. Willingness to accept plays a mediating role between performance expectancy and actual behavior. Perceived trust moderates the effect between willingness to accept and actual behavior, and the force of positive interpretation increases proportionally. It confirms how to improve the safety capacity of construction workers and provides references for governments, enterprises, and projects to formulate ST strategies.

A chemical accident cause text mining method based on improved accident triangle

BackgroundWith 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.MethodsBased 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.ResultsThe 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.ConclusionCompared 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.

Cause analysis of secondary explosion accident in Hushan Gold Mine, Shandong Province, China based on HFACS-CM model

Explosion accidents often occur during mining, especially sudden secondary explosions, which pose a serious threat to the life and property safety of employees. Based on the development process and consequences of typical secondary explosion accidents in Hushan Gold Mine, Qixia City, Shandong Province, China, this paper uses accident analysis model and cone measurement calculation method to determine the direct cause of secondary explosion accidents. The main conclusions are as follows: The HFACS-CM model is used to analyze the causes of secondary explosion from five aspects: external environment, organizational influence, unsafe leadership, the prerequisite conditions of unsafe behavior and unsafe behavior. It is found that the CO ignition explosion in the mining process is the direct cause of secondary explosion. Then, combined with the experimental data of the cone measuring instrument, the field CO amount was calculated, and the MCO ≥ 101.4 m3 was obtained, indicating that the CO production did reach the condition of explosion range, and the analysis results of the model were verified. Through HFACS-CM model analysis and calculation verification, the key causes of secondary explosion accidents in such mines are found and lessons are learned, which provides an effective reference for the effective prevention of deflagration accidents in the field of mine safety.

Electronic performance monitoring framework to quantify unhealthy and unsafe on-site behaviours

Health and Safety (H&S) should be a significant concern in construction projects. The quantification of the work injury accidents started with work A. W. Heinrich (1931), passing per Frank E. Bird Jr. (1969) to a study by ConocoPhillips Marine (2003). These theories, also known as the Accident triangle, Heinrich's triangle, or Bird's triangle, are a base for industrial accident prevention applied in practice by the Dupont STOPTM methodology. Over this methodology, the number of unhealthy and unsafe behaviours is classified and accounted for to diagnose the potential workers' recordable injuries, lost workdays, and fatalities. The At-risk behaviours and the Near Misses are the most challenging safety measurement approach as they depend on the human-observation notes usually conducted in a safety audit. Electronic Performance Monitoring (EPM) concerns the use of innovative hardware and software to assess on-site safety and productivity. The electronic monitoring of construction workers can contribute to evaluating unsafe behaviours. At-risk behaviours could be measured based on location, trajectory, and motions. Also, with the monitoring of the equipment and vehicles, it is possible to measure Near Misses. The Information Technologies integration allows for collecting information about Recordable Injuries, Lost workdays, and Fatalities. This work discusses and presents a framework connecting EPM deployment to data acquisition of unhealthy and unsafe on-site behaviours. The contributions in the theoretical and practical fields concern the opportunities of using new technological assumptions to increase data acquisition and improve the accuracy and effectiveness of quantitative safety analysis.

The Influence of Leadership in Chinese Construction Enterprises on Workers' Unsafe Behavior

Frequent safety accidents in the construction industry have resulted in many casualties and serious economic losses. The main cause of construction safety accidents is the unsafe behavior of construction workers. Although scholars have conducted extensive research on the influencing factors and mechanisms of unsafe behavior among construction workers, there is relatively little research on the emotions of construction workers as its influencing factors. Through pre research and literature review, it was found that emotions are an important factor affecting unsafe behavior among construction workers. This paper combines system analysis theory, accident causation theory, emotional event theory, and behavioral decision-making theory, and uses literature analysis to propose hypotheses. Data is obtained through questionnaire surveys, on-site observations, and text analysis, and statistical methods are used to verify the proposed hypotheses; Mainly using methods such as correlation analysis and regression analysis. This paper proposes and verifies that emotions are an important theoretical perspective for explaining unsafe behavior among construction workers and verifies the significant mediating role of emotions between leadership and unsafe behavior, as well as the significant moderating effect of safety atmosphere. In summary, the paper enriches the theoretical dimensions of the impact mechanism of unsafe behavior among construction workers by introducing emotions as an element that characterizes individual psychological states. At the practical level, the paper proposes a new management strategy to reduce the level of unsafe behavior among construction workers by improving management factors and regulating emotions.

Impact of the Offshore Oil and Gas Working Environment on the Mental Health and Safety Behaviour of Workers

Abstract The rate of work-related accidents is rising in the global Oil and Gas industry, with a significant contribution of human error driven primarily by unsafe behaviour in a hazardous and isolated work environment involving a unique mix of psychological and social risks. From this standpoint, the working environment could be the hidden hand that can negatively affect workers' behaviour, leading to errors. To mitigate the risks of accidents and incidents caused by unsafe behaviour, the potential impact of the working environment's psychosocial hazards, represented by remoteness and isolation, on workers' mental health, was investigated, and the associated influences on safety behaviour were assessed. The study proposed a research model in which characteristics of the offshore working environment negatively affect workers' mental health and safety behaviour. Stress, Anxiety and Depression were chosen to represent mental health disorders due to their significant prevalence among work-related mental illnesses as per recent reports of the International Labour Organization (ILO) and multiple other studies. The methods used included a review of relevant literature and the quantitative research approach, where data was obtained from a survey involving a sample of (409) Egyptian workers in the oil and gas industry. The descriptive analysis was employed, and the results, according to the study sample, indicated a "high" level of psychosocial hazards in the work environment. The results also revealed a "moderate" level of worker safety behaviour, which can be explained by significantly lower safety engagement and communication levels despite their high adherence to safety rules. Hence, the researcher recommends that industry regulators and operators initiate mental health interventions, including raising worker awareness, alleviating isolation, controlling high workloads, improving living conditions, providing adequate means of communication, reasonable privacy, and recreational facilities as the adoption of The approach can lay the foundation for safety and optimal performance for the oil and gas workforce.

Safety analysis of Civil aviation Flight and UAV Operation based on STAMP/STPA

In order to prevent the occurrence of dangerous approaches or accidents in the low-altitude UAV conflict resolution process, the security problem in the process was transformed into the control problem, and a conflict resolution security analysis method for low altitude UAV based on STAMP/STPA was proposed. Firstly, based on STAMP (System Theoretic Accident Modeling and Processes), the security control structure in the conflict resolution system was constructed. Then STPA (Systems Theoretic Process Analysis) was used to determine system-level accidents and hazards based on the context information of system operation, identify the unsafe control behaviors in the process of conflict resolution, and analyze the key causes of unsafe control behaviors.

Novel blockchain deep learning framework to ensure video security and lightweight storage for construction safety management

In construction management, video data tampering behavior like manual forging and deletion can negatively impact on-site safety and accident accountability. Blockchain technology holds the potential to address this issue by leveraging distributed ledger characteristics. However, blockchain's limited storage capacity and block size make it difficult to upload large-sized construction data such as daily monitoring video. Furthermore, it is unnecessary to store all construction data in any case. Therefore, this study proposes a blockchain deep learning framework that focuses on how to efficiently extract and securely store key information (i.e., video summarization that involves worker’s unsafe behavior) on-blockchain for data traceability. The framework involves a novel data-driven and rule-based keyframe extraction (DRKE) model to lightweight large-sized construction video in the nascent field of deep learning and blockchain combination. To define parameters for the DRKE model, specific construction rules (e.g., people’s unsafe behavior-based and people-based rules) have been pre-defined. This framework has been evaluated, and the results demonstrate its capability for effective video security storage, facilitating practical needs in construction management. The study extends existing research and provides a practical solution for large-sized construction video storage with security and lightweight considerations. The proposed video security storage and data lightweight process offers substantial benefits to construction management, such as streamlined accident investigation and accountability and improved on-site work efficiency, contributing to the smooth progress of construction projects.

Human reliability of the intelligent construction site tower crane driver interface based on DEMATEL-ISM-BN.

With the arrival of Industry 4.0, intelligent construction sites have seen significant development in China. However, accidents involving digitized tower cranes equipped with smart systems continue to occur frequently. Among the main causes of these accidents is human unsafe behavior. To assess the human factors reliability of intelligent construction site tower cranes, it is necessary to shift the safety focus to the human-machine interface and identify patterns of human error behaviors among tower crane drivers through text mining techniques (TF-IDF-TruncatedSVD-ComplementNB). Based on the SHEL model, the behavioral factors influencing human factors reliability in the human-machine interface are categorized and a Performance Shaping Factors (PSF) system is constructed. Building on the foundation of constructing an indicator system for human factors error influence in the driver interface of intelligent construction site tower cranes, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is combined with the Interpretive Structural Modeling (ISM) to analyze the importance of various factors in causing human errors and to analyze the logical structure among these factors. Simultaneously, a Bayesian network is constructed using a multi-level hierarchical structural model, thus establishing a new evaluation method for the human-machine interface. The effectiveness of the proposed method is validated through Bayesian network causal inference based on real case studies. The results demonstrate that the evaluation process of this method aligns with the operational scenarios of tower crane drivers in intelligent construction sites. It not only allows for quantifying the likelihood of human errors but also enables the development of targeted measures for controlling unsafe behaviors among tower crane drivers in intelligent construction sites.

Development and evaluation of a de-escalation training intervention in adult acute and forensic units: the EDITION systematic review and feasibility trial.

Development and evaluation of a de-escalation training intervention in adult acute and forensic units: the EDITION systematic review and feasibility trial.

Acting out when psychosocial safety climate is low: understanding why middle-level managers experience upward mistreatment.

Upward mistreatment, despite being under studied, is an influential phenomenon affecting middle managers' well-being and performance. The work environment hypothesis of bullying proposes that an undesirable work context is the main cause of workplace bullying, suggesting the importance of creating an anti-mistreatment climate, that is, psychosocial safety climate (PSC). In this study, we argue that upward bullying and aggression are unsafe behaviors, a "retaliation" by employees resulting from their unsafe work context. Using a large-scale multisource sample collected from 123 organizations, 6,658 middle managers and 34,953 employees, we examined the relationship between collective PSC, individual-perceived PSC and middle managers' experience of upward mistreatment. Single-level and multi-level modeling results suggested that PSC is an important element in reducing the likelihood of upward bullying and aggression, in turn, protecting managers' well-being. More importantly, upward bullying is a way that employees act out when there is an undesirable working context. Future research on workplace mistreatment should examine PSC and upward mistreatment. Interventions provided should focus on improving PSC which could in turn preventing upward mistreatment, thereby improving psychosocial safety for both employees and middle managers to prevent negative actions.

Characteristics and trends of pediatric firepit burns: insights for prevention and safety

IntroductionAs fire pits grow in popularity, so do the associated burn injuries. Our study examines pediatric fire pit burns characteristics to raise awareness and promote safety precautions.MethodsWe conducted a retrospective...