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In the context of industrial relations, New Zealand's accident compensation scheme (ACC) is an internationally unique system. Recent reforms have been criticised by trade unions, however. They perceive ACC as a positive benefit of employment obtained for workers as of right in exchange for the extinguishment of the right to sue. Any threat to this is seen as a threat to workers' conditions of employment. The 1992 reforms have been by business leaders, on the other hand, for not going far enough along the road of privatisation and maintaining one of the bastions of welfare dependency. In the light of these tensions, the present article examines the history of the scheme, the current reforms and some implications for the future of ACC.

Background : The car accident injury level is known to be a result of a complex interaction of factors to drivers’ behavior, vehicle characteristics and environmental condition. Therefore it is obvious that identifying the contribution of the factors to the accident injury is very critical. The objective of study was to perform descriptive analysis to see the characteristics of car accident, to assess the prevalence and determinants of road safety practices in Mekelle City, Tigray, Ethiopia. Methods : A random sample of data was extracted from traffic police office from September 2014- July 2017. An ordered logistic regression model was used to examine factors that worsen the car accident level. Result : A total sample of 385 car accidents were considered in the study of which 56.7% were fatal, 28.6% serious and 14.7% slight injury. The model estimation result showed that, being experienced drivers (Coef. = 0.686; p-value< = 0.050) were found to increase the level of injury. On the other hand, being private vehicle (Coef. = -1.160; p-value

This study was conducted to investigate the possibility of using gaze entropy to evaluate an operator's situation awareness in an emergency accident situation of a nuclear power plant. Gaze entropy can be an effective measure for evaluating an operator's situation awareness at a nuclear power plant because it can express gaze movement as a single comprehensive number. In order to determine the relationship between situation awareness and gaze entropy for an emergency accident situation of a nuclear power plant, an experiment was conducted to measure situation awareness and gaze entropy using simulators created for emergency accident situations LOCA, SGTR, SLB, and LOV. The experiment was to judge the accident situation of nuclear power plants presented in the simulator. The results showed that situation awareness and Shannon, dwell time, and Markov entropy had a significant negative correlation, while visual attention entropy (VAE) did not show any significant correlation with situation awareness. The results determined that Shannon entropy, dwell time entropy, and Markov entropy could be used as measures to evaluate situation awareness.

Our work for the first time evaluates the effectiveness of visual and acoustic warning systems in an accident situation using a realistic, immersive driving simulation. In a first experiment, 70 participants were trained to complete a course at high speed. The course contained several forks where a wrong turn would lead to the car falling off a cliff and crashing - these forks were indicated either with a visual warning sign for a first, no-sound group or with a visual and auditory warning cue for a second, sound group. In a testing phase, right after the warning signals were given, trees suddenly fell on the road, leaving the (fatal) turn open. Importantly, in the no-sound group, 18 out of 35 people still chose this turn, whereas in the sound group only 5 out of 35 people did so - the added sound therefore had a large and significant increase in situational awareness. We found no other differences between the groups concerning age, physiological responses, or driving experience. In a second replication experiment, the setup was repeated with another 70 participants without emphasis on driving speed. Results fully confirmed the previous findings with 17 out of 35 people in the no-sound group versus only 6 out of 35 in the sound group choosing the turn to the cliff. With these two experiments using a one-shot design to avoid pre-meditation and testing naive, rapid decision-making, we provide clear evidence for the advantage of visual-auditory in-vehicle warning systems for promoting situational awareness.

Context: The use of Geographic Information System (GIS) as a real-time monitoring system for the control and management of accident events is well known as it provides a platform to perform various spatial and network analysis and also in the presentation of descriptive data. Aim and Objective: This paper presents a GIS-based approach to find out areas prone to road traffic accidents based on spatial analysis and to analyze the spatial accessibility using network analysis Material and Methods Accident particulars like date, location, time and outcome for the year 2015 were included in the GIS database. Moran’s I method of spatial autocorrelation was used for the assessment of spatial clustering of accidents and hotspots spatial densities. The clusters of high and low values of the severity of accidents were obtained using cluster and outlier analysis. Location-allocation was performed as part of network analysis to find the nearest hospital to these high-value clusters. Results: Spatial autocorrelation showed that there was overall clustering present, cluster and outlier analysis gave clusters of severe accidents at NH-1 and Fortis hospital was the nearest facility to these clusters. Conclusion: This system is highly useful and provides information about accident locations, accident and service diagnosis and fast delivery of emergency services.

Accident analysis models play a significant role in accident investigation and analysis. Most traditional models, usually based on a chain of events, have serious limitations when used for complex systems, especially for coal mine accident. HFACS is a widely accepted accident cause model with complex structure and content. Based on HFACS and accident investigation need in China, a new systemic accident analysis method “Six-hierarchy model” was proposed. And the index database of accident causal factors at each hierarchy of coal mine accidents was established. This model provides clear guidance for the prevention of accidents from six levels, which helps to the targeted rectification after accidents and the improvement of safety management level. The source, application scope, advantages and disadvantages of HFACS and six-hierarchy model are compared. The “2.14” gas explosion accident in Sunjiawan coal mine is analyzed by using HFACS and six-hierarchy model and the safety management suggestions are put forward. The study shows that the Six-hierarchy model provides a clear guide for the prevention of accidents from six levels. And it benefits the targeted rectification after accidents and the continuous improvement of safety management.

Due to massive flammable chemical materials and dense layout, domino accident chain always occurs in major accidents of atmospheric storage tank farm. In this paper, 136 domino effect accidents of atmospheric tanks are analyzed to achieve relative probability by ETA method. Based on accident probability, an analysis model is proposed to define the most likely accident chain and the key tank in the atmospheric tank farm. To get the real accident scenario, the method adopts the heat radiation model of pool fire under the fire extinguishing conditions. Combined with the dynamic Bayesian network, the escalation model of accident chain is established to figure out the dynamic probability of the tank under the condition of fire extinguishing. Finally, an example is used to verify the feasibility of the method. It is of great theoretical significance and application value for the treatment of domino accident in atmospheric tank farm.

Some criticize the data-driven study of driverless car ethics for relying on deterministic crash scenarios where the focus should be on minimizing the occurrence of accidents instead. Indeed, decisions in road traffic are taken in the domain of risk. A relevant ethical question is, however, whether autonomous vehicles should actually minimize an accident’s probability or rather balance the accident’s probability and severity. While ethicists’ introspections might lead them to different conclusions, a participatory paradigm in ethics demands to include laypeople’s intuitions into the societal debate. In three online studies, we found that the mere minimization of accident probability in ethical dilemmas does not correspond to the views of our sample. Our participants favor a balanced consideration of accident probability and severity. This remains true even if the underlying dilemma is based on a more realistic scenario of allocating minor risks among road users. Our findings furthermore illustrate that trolley problems may be informative for autonomous vehicles and emphasize the importance of transparency if manufacturers and the public do not agree on guiding principles in road traffic.

Although the dangers of working in confined spaces have been known for many years, fatal accidents related to working in confined spaces still frequently occur. Considerable research has been conducted to identify potential contributory factors of confined space incidents through analyzing accident reports. However, accident databases are usually read and interpreted manually by human experts. The process of analyzing confined space accident reports can be time-consuming and labor-intensive. As the number of accident records increases, it is difficult for the experts to manually review all the reports. Moreover, different individuals may reach various conclusions from the same accident report. Some analysts may fail to capture all the meaningful and relevant causal factors. Automatic information extraction using special rules and ontology-based approaches can be used to mine reports of confined space accidents. However, such approaches tend to suffer from the problem of weak generalization. To overcome this limitation and improve the performance of contributory factors analysis, an improved deep learning based framework is proposed in this paper to automatically extract and classify contributory factors from confined space accident reports using BERT-BiLSTM-CRF and CNN models. Research results suggested that the proposed framework can be used as a feasible method to qualitatively and quantitatively explore the contributory factors of confined space accidents. By analyzing a large quantity of confined space accident reports, the frequency of contributory factors can be estimated automatically. This outcome is helpful to significantly improve the risk assessment quality of confined space works.

Background Factors related to root causes can cause commonly occurring accidents such as falls, slips, and jammed injuries. An important means of reducing the frequency of occupational accidents in small- to medium-sized enterprises (SMSEs) of South Korea is to perform intensity analysis of the root cause factors for accident prevention in the cause and effect model like decision models, epidemiological models, system models, human factors models, LCU (life change unit) models, and the domino theory. Especially intensity analysis in a robot system and smart technology as Industry 4.0 is very important in order to minimize the occupational accidents and fatal accident because of the complexity of accident factors. Methods We have developed the modern cause and effect model that includes factors of root cause through statistical testing to minimize commonly occurring accidents and fatal accidents in SMSEs of South Korea and systematically proposed educational policies for accident prevention. Results As a result, the consciousness factors among factors of root cause such as unconsciousness, disregard, ignorance, recklessness, and misjudgment had strong relationships with occupational accidents in South Korean SMSEs. Conclusion We conclude that the educational policies necessary for minimizing these consciousness factors include continuous training procedures followed by periodic hands-on experience, along with perceptual and cognitive education related to occupational health and safety.

Headphones are used by most people as a source of entertainment. Many pedestrians, cyclists and joggers like to use headphones while they are on the road even though it can result in extreme sensory deprivation and distraction. The purpose of this paper is to conduct a study on how the use of headphones can reduce the standard limits of human perception. It examines how this increases the vulnerability of one facing road accidents and proposes a possible solution to overcome this issue.

Simulating traffic flows is an urgent task when choosing the optimal movement scheme of vehicles along the road network. One of the problems affecting the convenience of moving on the road network is road accidents. The article considers the problem of simulating the movement of vehicles when bypassing the scene of a traffic accident. A mathematical model reflecting the movement of motor vehicles in a dense traffic flow has been developed in the case when traffic along one of the lanes is blocked as a result of a traffic accident or repair work. The time intervals between cars in each lane are assumed to be subordinate to Erlang's law. The scene of the incident is presented as a queuing system. Using the method of pseudo-states and Markov chains, the author calculated the movement characteristics of the vehicles near the scene of a traffic accident. Simulating the movement of traffic flows near the accident sites allows predicting the delays caused by vehicles, and adjusting the optimal traffic management schemes.

Continuous metro-operation accidents lead to serious economic loss and a negative social impact. The accident causation analysis is of great significance for accident prevention and metro operation safety promotion. Network node importance (NNI) evaluation has been widely used as a tool for ranking the nodes in complex networks; however, traditional indicators such as degree centrality (DC) are insufficient for examining accident networks. This study proposed an improved method by integrating decision making trail and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) into traditional NNI evaluation, where the key nodes are determined by both the nature of the accident network topology and the contribution of the nodes to accident development. Drawing on this method, 32 accident causal factors were identified and prioritized on the ground of 248 accident cases. It was found that 14 important factors related to staff (e.g., “driver noncompliance”), environment (e.g., “extrinsic nature disturbance”), passenger (e.g., “passenger sudden illness”), and machine (e.g., “track failures”) should be given priority in safety management due to their significant tendency of causing metro accidents. Theoretical and managerial implications were discussed to provide useful insights into the understanding of the causation of metro accidents and form a basis for metro managers to develop targeted safety countermeasures related to metro operation. The proposed hybrid method is proven effective in investigating accident networks involving sequential and casual relationships and revealing factors with high possibility to increase accidents.

Learning from past incidents is critical to achieving and maintaining high process safety performance. Accident and incident records provide one way for learning; however, these are usually in the form of unstructured texts, which makes analysis difficult. Recently, text mining methods based on supervised learning have been proposed for analyzing accident reports; however, they require an impractically large number of labeled records as training examples. This paper proposes an automated, semi-supervised, domain-independent approach for analyzing accident reports. Given a set of user-defined classification topics and domain literature such as handbooks, glossaries, and Wikipedia articles, the method can identify domain-specific keywords and group them into topics with minimal expert involvement. These keywords and topics can then be used for various data mining purposes, including classification. The proposed approach is demonstrated using two different case studies across domains: (1) in aviation to identify the stage of flight when an accident occurs, and (2) in the process industry domain to identify the cause of pipeline accidents. The average classification accuracy of the proposed method was 80% which is comparable to that of supervised learning methods. The key benefits of this approach are that it can generate domain-specific predictive models with limited manual intervention.

Construction is an industry well known for its very high rate of injuries and accidents around the world. Even though many researchers are engaged in analysing the risks of this industry using various techniques, construction accidents still require much attention in safety science. According to existing literature, it has been found that hazards related to workers, technology, natural factors, surrounding activities and organisational factors are primary causes of accidents. Yet, there has been limited research aimed to ascertain the extent of these hazards based on the actual reported accidents. Therefore, the study presented in this paper was conducted with the purpose of devising an approach to extract sources of hazards from publicly available injury reports by using Text Mining (TM) and Natural Language Processing (NLP) techniques. This paper presents a methodology to develop a rule-based extraction tool by providing full details of lexicon building, devising extraction rules and the iterative process of testing and validation. In addition, the developed rule-based classifier was compared with, and found to outperform, the existing statistical classifiers such as Support Vector Machine (SVM), Kernel SVM, K-nearest neighbours, Naïve Bayesian classifier and Random Forest classifier. The finding using the developed tool identified the worker factor as the highest contributor to construction site accidents followed by technological factor, surrounding activities, organisational factor, and natural factor (1%). The developed tool could be used to quickly extract the sources of hazards by converting largely available unstructured digital accident data to structured attributes allowing better data-driven safety management.

Occupational Safety and Health (OSH) is an important aspect to pay attention to in the workplace. This is due to the workplace environment that has potential hazards and risks that can cause work-related accidents and diseases. Accidents and occupational diseases need to be prevented and minimized because they reduce work productivity and harm workers and business owners. The risk of accidents and occupational diseases also exists in the informal batik sector. One of the informal Batik sectors in Banyumas Regency is Batik Papringan. Papringan Batik workers have not implemented the OSH Standard Operational Procedure (SOP). This causes workers to experience health complaints such as coughing, itching, and musculoskeletal conditions. Therefore, it is necessary to analyze occupational accidents and diseases with the Hazard and Operability Study (HAZOP) and its control. The research objective was to determine the hazards and risks of accidents and diseases due to the work of Batik Papringan workers. The method used is the HAZOP method. The results showed that the stooped and static sitting posture, exposure to chemicals from wax smoke and dyes and the absence of 5R (Ringkas, Rapi, Resik, Rawat, Rajin) were hazards that pose extreme risks. Risk control in the form of ergonomic work place and equipment design and compliance with the 5R and the use of PPE are the most prioritized risk controls. The conclusion from this research is that the implementation of risk control in the form of ergonomic work place and equipment design, fulfilling the 5R and the use of PPE need to be done continuously to prevent accidents and occupational diseases. Keywords: Accident, diseases, HAZOP, control, batik

Now a day’s road accident is one of the principal concerns in our states. Reckless automobile driving and driver drowsiness are the major motives in the back of those road accidents. The alarming rate of accidents and uncontrollable automobiles on the streetdemand an automatic system that could guide drivers right now in dangerous conditions. While any obstacle (just like the human, vehicle, and some other item) comes in front of the vehicle, control speed of the vehicle is the viable strategy to keep away from injuries. While a driver in a drowsy state or sleep state, give warning to the driver and control the velocity of the vehicle is the possible way to keep away from injuries. We endorse a solution in our mission to avoid avenue injuries and to manipulate the rate of automobiles. The machine will locate boundaries and motive force drowsiness using an ultrasonic Sensor and eye blink sensor and Arduino will execute a collision-avoidance gadget according to a pre-burnt code in the Arduino. The machine has also velocity manipulate features. It’s going to reduce or increase the rate of the vehicle relying on the impediment distance from the shifting car to decrease the damage or collision of an accident. Furthermore, if any obstacle from the bottom comes in the direction of cars, right now a buzzer will alert the driving force, and if any quick circuit happens in the engine element smoke sensor detecting and gives alert to the driving force and stop the automobile.

The main steam line break accident is an essential initiating event of the pressurized water reactor. In present work, the fuzzy set theory and the signal-based fault detection method has been used to detect the occurrence and diagnosis of the location and break area for the small scale MSLB. The models are validated by the AP1000 accident simulator based on MAAP5. From the test results it can be seen that the proposed approach has a rapid and proper response on accident detection and location diagnosis. The method proposed to evaluate the break area shows good performances for small scale MSLB with the relative deviation within ±3%.

The objective of this study is to estimate the occurrence probabilities of severe accident phenomena based on a fuzzy elicitation technique. Normally, it is difficult to determine these probabilities due to the lack of information on severe accident progression and the highly uncertain values currently in use. In this case, fuzzy set theory (FST) can be best exploited. First, questions were devised for expert elicitation on technical issues of severe accident phenomena. To deal with ambiguities and the imprecision of previously developed (reference) probabilities, fuzzy aggregation methods based on FST were employed to derive the occurrence probabilities of severe accidents via four phases: 1) choosing experts, 2) quantifying weighting factors for the experts, 3) aggregating the experts’ opinions, and 4) defuzzifying the fuzzy numbers. In this way, this study obtained expert elicitation results in the form of updated occurrence probabilities of severe accident phenomena in the OPR-1000 plant, after which the differences between the reference probabilities and the newly acquired probabilities using fuzzy aggregation were compared, with the advantages of the fuzzy technique over other approaches explained. Lastly, the impact of applying the updated severe accident probabilities on containment integrity was quantitatively investigated in a Level 2 PSA model.

The number of motor vehicles on road increased spectacularly which causing major social and transportation problems such as death and economic losses. Eradication of accident blackspots is a crucial part of the road safety approach. So, this issue informed the quest to study and identify the accidental black spot in the context of NH 6. The stretch of 35 km on NH 6 near Pune was considered to identify the black spot. Accident blackspot locations were distinguished through the accident severity index (ASI) method by employing two years of accident data. These identified black spots were prioritized based on their final severity index score. The two most vulnerable black spots out of nine were selected for further analysis and countermeasure process. Conclusively, the accident severity-based framework can effectively correlate the nature of the accident with the type of casualty to provide detailed and accurate black spot location. Keywords: Black Spots, Accident Severity Index (ASI), Black Spot Prioritization, Traffic Volume, Highway, Countermeasures.

It takes advantage of the health crisis, the state of public calamity, to create opportunities to reduce the scope of accident coverage. The use of the COVID-19 pandemic as an artifice to denature the Brazilian accident regulatory system has been attractive to those who intend to change it indirectly and subtly, without the need for legal change. It starts with an anomaly – disregarding the accident on the way in the calculation of the Accident Prevention Factor (FAP) under the oxymoron: it is an accident at work, but it is not! It is argued that the employer does not have control over risk situations and, therefore, cannot be held responsible, under penalty of discouraging the FAP as an incentive to prevention. In the same vein, the mirroring of this oxymoron is rehearsed in an attempt to adopt the same anomalous logic of excluding COVID-19's involvement from the accident list. This article presents the accidental typology, while demonstrating the obsoleteness of the causal nexus replaced by the technical nexus since 1967, as well as reaffirming the accidental nature of incapacities resulting from route accidents and COVID-19, except for home office.

Background: An inevitable traffic accident frequently resulted from the dereliction of individualized community from disobeying regulation which causes an impact on the safety of another person. The impact raised is not merely in terms of material but also non-material. Objective: This research was aimed to know the correlation between risky riding behavior and the accident of motorcycles experienced by university students of Halu Oleo University of Kendari, Indonesia. Methods: This research utilized the cross-sectional design. Data were collected on June 2021 using a validated questionnaire online among 251 respondents. Results: 84.5% of respondents had ever experienced motorcycle accidents. There was a correlation between listening to music while riding ( p = 0.973), riding with the speed more than 50 km/hour ( p = 0.220), sudden braking ( p = 0.267), overtaking without turning rear light ( p = 0.518), with the motorcycle accidents. Conclusion: The dangerous riding behavior can potentially decrease the rider’s ability and increase the possibility of a car accident. The university could improve safe riding through empowering and encouraging education about riding towards students.

A common procedure when conducting physics experiments is to repeat a measurement several times to calculate the mean and standard deviation. That might be the only instruction we give to students as a means to minimize random errors. However, that technique does not guarantee that the answer will be correct. It might give the same wrong answer every time. How then can we teach students to avoid systematic errors? We don’t focus on that problem enough when teaching physics. Part of the problem is that we and the students usually know the correct answers in advance, since student experiments have usually been repeated thousands of times by others.

It is important to systematically identify the contributing factors in coal mine accidents from a large-scale analysis of accident reports. However, previous scholars have mainly used human analysis methods to define accident-causing factors, leading to incomplete and biased cause checklists due to personal experience and knowledge. Furthermore, a data-driven method is needed to quantify the importance of each factor and clarify the mechanism of different types of accidents. Considering these, this study creatively combined text mining technology and a complex network to explore the coal mine accident-causing mechanism. Through the text mining of 307 accident reports, 52 main accident-causing factors were identified, and a coal mine accident causation network was constructed based on the strong association rules among factors. Second, eight core factors and their associated sets, as well as seven critical links for different accident types, were clarified through network centrality analysis and accident path analysis. This study shows that regulatory authority is the most influential level of accident causation, gas accidents are the most easily triggered accident type, a lack of effective mechanism for safety supervision→failure to arrange full-time safety inspectors to follow the shift→lack of serious and thorough on-site hidden danger investigations→inadequate anti-surge measures are the key links in gas accident causation. This study contributes new perspectives on identifying contributing factors and their complex interaction mechanisms from accident report data for practical applications in risk analysis and accident prevention.

En el artículo publicado en el presente volumen, Verónica Rodríguez-Blanco argumenta que el punto de vista interno hartiano es inestable en la medida que, al centrarse en los estados mentales del agente, solo puede conducirnos a comprender el sentido o propósito de las acciones de un modo accidental. El núcleo del argumento es que el punto de vista interno presupone un enfoque inward-looking de la acción y que, para comprender adecuadamente el sentido o propósito de las acciones, es necesario adoptar, en cambio, un enfoque outward-looking. En este artículo, sostengo que los argumentos de Rodríguez-Blanco en contra del enfoque inward-looking de la acción son insatisfactorios. Argumento, además, que las críticas que dirige a la teoría del punto de vista jurídico de Raz están igualmente desencaminadas, y que su propuesta positiva relativa al rol de las reglas jurídicas en el razonamiento práctico tiene serios defectos.

Introduction Freeway 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.

The commercial shipping industry continues to be responsible for a vast majority of the world’s trade, yet it remains behind similar transport industries in terms of safety and accident prevention. The regulation of safety systems and naval architecture have led to significantly fewer accidents, but human error remains a constant factor and has been identified as the most likely cause of a marine accident. A systematic review of the methods used to investigate latent factors regarding human error in marine accidents has been conducted in an effort to further examine casual factors which lead to human error. Widely accepted methods or modified methods from other high-risk industries such as Human Factors Analysis and Classification System (HFACS) and Bayesian Networks have been utilized, but few have taken into account all of the sociotechnical macroergonomics of such a complex industry, leaving significant research and engineering potential.

This paper proposes an innovative framework of modeling the statistical properties of the near-accident event and pedestrian behavior at non-signalized intersections based on Poisson process and logistic regression. The first contribution of this study is that the predictive intensity model of the near-accident event is established by regarding the near-accident event as a Poisson process on space of the vehicle velocity, distance to the intersection and lateral distance to the pedestrian at the time when pedestrian appears. Besides, logistic regression is used to build the model which can predict the probability of pedestrian behavior. The two proposed models are validated in a generative simulation. The simulated pedestrian behavior data is generated by the proposed models and compared with the real data. The real data set is from the drive recorder data base of Smart Mobility Research Center (SMRC) at Tokyo University of Agriculture and Technology. Accident and near-accident data has been collected in the city streets with an image-captured drive recorder mounted on a taxi since 2006. The findings in this study are expected to be useful for constructions of traffic simulators or safety control design which considers the pedestrian-vehicle interaction.

When public policies are designed from scratch, with no or limited possibility of learning from past experience, decision makers rely on their creativity and lateral thinking to counterbalance cognitive biases. One of the crucial issues for both evaluators and policy makers in such situations is not only to elaborate a sound theory of the programme but also to be aware of the existence of competing theories to solve the same problem. Being able to detect such theories constitutes an essential step to avoid ‘Type III errors’, that is, enacting the right solution for the ‘wrong’ problem. Reflecting on the analysis of two programmes aimed at preventing the death of children by heatstroke, this article illustrates the implications of alternative ‘accident theories’ for the evaluation of safety policies.

Emergency response decision-making for maritime accidents needs to consider the possible consequences and scenarios of an accident to develop an effective emergency response strategy to reduce the ...

This research was based on a rare case involving an explosion accident of liquefied natural gas (LNG) bus that occurred in the second week of December 2019 in China. The author of this paper participated in the whole accident investigation process as expert. Through the nonlinear elastoplastic analysis of the bursting tank by finite element simulation, this explosion accident was caused by the rapid phase transition of residual LNG during the deicing of the frozen pipeline by water, and the human factor was the direct cause of the accident. To understand the accident causation in depth, human factors involving safety issues in the LNG vehicle maintenance operation were identified using a modified human factors analysis and classification system and the analytic hierarchy process method. Furthermore, the lessons learned, and recommendations were revealed to prevent such accidents in the field of liquefied natural gas vehicle.

The objective of this study is to investigate the applicability of one-class classification (OCC) models in traffic accident prediction. So far, the accident prediction problem has been considered ...

Purpose At the US passenger stations, train operations approaching terminating tracks rely on the engineer’s compliant behavior to safely stop before the end of the tracks. Noncompliance actions from the disengaged or inattentive engineers would result in hazards to train passengers, train crewmembers and bystanders at passenger stations. Over the past decade, a series of end-of-track collisions occurred at passenger stations with substantial property damage and casualties. This study’s developed systemic model and discussions present policymakers, railway practitioners and academic researchers with a flexible approach for qualitatively assessing railroad safety. Design/methodology/approach To achieve a system-based, micro-level analysis of end-of-track accidents and eventually promote the safety level of passenger stations, the systems-theoretic accident modeling and processes (STAMP), as a practical systematic accident model widely used in the complex systems, is developed in view of environmental factors, human errors, organizational factors and mechanical failures in this complex socio-technical system. Findings The developed STAMP accident model and analytical results qualitatively provide an explicit understanding of the system hazards, constraints and hierarchical control structure of train operations on terminating tracks in the US passenger stations. Furthermore, the safety recommendations and practical options related to obstructive sleep apnea screening, positive train control-based collision avoidance mechanisms, robust system safety program plans and bumping posts are proposed and evaluated using the STAMP approach. Originality/value The findings from STAMP-based analysis can serve as valid references for policymakers, government accident investigators, railway practitioners and academic researchers. Ultimately, they can contribute to establishing effective emergent measures for train operations at passenger stations and promote the level of safety necessary to protect the public. The STAMP approach could be adapted to analyze various other rail safety systems that aim to ultimately improve the safety level of railroad systems.

The prediction of possible damage due to accidents on linear sections of oil and gas pipelines is considered. In the proposed mathematical model, the total economic loss includes components associated with damage to the pipeline itself, gas or oil leaks, and environmental pollution. A fully connected neural network is used to calculate the probability of an accident due to equipment wear, while a fully convolutional neural network is used to calculate the probability of an accident due to external mechanical factors. The result is a mechanism for predicting the possible economic loss on the basis of neural networks.

The paper examines the features of the use of automatic control of offenses in the field of road traffic, taking into account the territorial and climatic features of the Khabarovsk region, their impact on road safety. The author analyzes the main problems, as well as offers solutions for better and more objective recording of traffic flow and accident prevention. The novelty of the research consists in a comprehensive analysis of the theoretical and legal aspects of the fundamental ideas of road safety.

There is very little discussion about the needs of occupational health within the University area. In fact, the University is a place where interaction occurs not only for lecturers and students, but also employees, contractors and other visitors with very varied backgrounds and cultures. The lack of application of OHSMS in the construction sector in the University area causes a high number of work accidents. Making a safety plan at the initiation stage can reduce and prevent potential hazards that will occur. One of the methods can be used to reduce work accidents is by developing a safety plan using WBS (Work Breakdown Structure). This study aims to develop a WBS-based construction occupational safety and health plan for structural and architectural work buildings. This study develops a safety plan based on Regulation of Minister of PUPR No. 21 of 2019. This study uses literature study techniques for risk identification and questionnaires validation by experts with a minimum of 5 years experiences in OHS construction project. The result of this study is identification of hazards, impacts, prevention, OHS objective and programs. The results will be used for the development of a safety plan, intending to improve occupational safety and health performance and reduce occupational accidents in construction projects.

In this paper a random sample of drivers aged sixty-five years or older was selected from the Alabama Department of Public Safety Records. The data in the sample has information on many variables including the number of accidents, demographic information, driving habits, and medication. The purpose of the sample was to assess the effects of demographic factors, driving habits, and medication use on elderly drivers. The generalized Poisson regression (GPR) model is considered for identifying the relationship between the number of accidents and some covariates. About 59% of drivers who rate their quality of driving as average or below are involved in automobile accidents. Drivers who take calcium channel blockers show a significantly reduced risk of about 34.5%. Based on the test for the dispersion parameter and the goodness-of-fit measure for the accident data, the GPR model performs as good as or better than the other regression models.

As an organization there is a range of responsibilities and legislation that require you to have a plan to ensure the safety of employees, customers and stakeholders who may be on site when an emergency occurs. Equally important is the ability of facility to react quickly to an emergency, saving time and money in restoring normal business. A well thought out, coordinated response helps prevent personal injury, property damage, and lessen the resulting hazard. When an organization plans on how it will respond to an emergency threatening its operations, it is more likely to survive the incident. During a large-scale disaster, local response agencies may be overwhelmed and unable to immediately respond to the organization site. The modelling and analysis of threats in a plant is not just for post hazard analysis, it includes all the working procedures when a hazard occurs.

The accident in Fukushima Dai-ichi nuclear power plants reconfirms the necessity of the safety assessment considering multiple nuclear reactor units. However, consideration of the interdependency a...

Previous studies have found that false memories increase when people receive stimuli which is related to their knowledge base. In the current set of four experiments, we examined how accident experience affected false memory production among motorcyclists using different types of false memory word lists. Specifically, Experiments 1, 2, and 3 were conducted involving motorcyclists with and without accident experience, whereas Study 4 was conducted on non-motorcyclists without accident experience. In all experiments, participants were given associatively-related word lists known to foster false memory creation. Different types of word lists were used consisting of accident-related words, emotionally-negative words, and neutral words. Levels of false memory were measured using recall and recognition tests. We found consistent results in Experiments 1, 2, and 3 in which among motorcyclists, accident-related words produced higher levels of false memory than emotionally-negative and neutral words. This pattern was not observed in Experiment 4 where non-motorcyclists were involved. Our results shed light on the role of previous experience on false memory formation. One implication could be that the police should be careful with reports of motorcyclists' experiences about their accidents as motorcyclists may spontaneously come up with false, but related details concerning these experiences.

Following the accident at Fukushima Daiichi nuclear power plant in 2011, a series of health literacy training workshops for local health workers was developed and implemented. This study aimed to analyse who among the intended audience gained the greatest benefit from the training. Nine health workers attended a workshop consisting of classroom training and a follow-up feedback assessment of materials they created in the workshop. The materials were assessed by a total of 131 intended readers. Using Sakai’s tool, those items asking readers to rate the accessibility of written information were used to compare the materials before and after revision. The total score for Sakai’s measure showed a significant improvement, and the elderly and those without regular doctor visits were more likely to notice improvements after revision. Such health literacy training could serve as a model programme to prevent inequity in access to health information in the face of a regional health crisis.

Following manufacturing, construction is the industry that is responsible for the majority of work-related accidents due to the complexity and uniqueness of each construction site. The accidents that occur on construction sites reflect the safety culture and the general health and safety (H&S) policy in the construction field. The purpose of this paper is to investigate the frequency of accidents and their contributing factors in each stage of 9 different types of construction. This paper utilizes data from published scientific literature to extract the universal contributing factors to be evaluated in the Greek construction industry and to compare results with documented international accidents. The riskiest construction stages per construction type and the main factors behind them are determined. The results show contradictions regarding the contributing factors as perceived by Greek professionals and those verified by international real accident data.