Accident; analysis and prevention
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Construction project features (CPFs) are organisational, physical and operational attributes that characterise construction projects. Although previous studies have examined the accident causal influence of CPFs, the multi-causal attribute of this causal phenomenon still remain elusive and thus requires further investigation. Aiming to shed light on this facet of the accident causal phenomenon of CPFs, this study examines relevant literature and crystallises the attained insight of the multi-causal attribute by a graphical model which is subsequently operationalised by a derived mathematical risk expression that offers a systematic approach for evaluating the potential of CPFs to cause harm and consequently their health and safety (H&S) risk implications. The graphical model and the risk expression put forth by the study thus advance current understanding of the accident causal phenomenon of CPFs and they present an opportunity for project participants to manage the H&S risk associated with CPFs from the early stages of project procurement.
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The UK Intelligent Speed Adaptation (ISA) project produced a rich database with high-resolution data on driver behaviour covering a comprehensive range of road environment. The field trials provided vital information on driver behaviour in the presence of ISA. The purpose of this paper is to exploit the information gathered in the field trials to predict the impacts of various forms of ISA and to assess whether ISA is viable in terms of benefit-to-cost ratio. ⋯ Of the two deployment scenarios, the Market Driven one is substantially outperformed by the Authority Driven one. The benefits of ISA on fuel saving and emission reduction are real but not substantial, in comparison with the benefits on accident reduction; up to 98% of benefits are attributable to accident savings. Indeed, ISA is predicted to lead to a savings of 30% in fatal crashes and 25% in serious crashes over the 60-year period modelled.
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Maryland (MD) recently became one of fourteen states in the United States to enact a traffic law requiring motor vehicles to pass bicyclists at a distance of greater than three feet. To our knowledge, motorist compliance with the law has never been assessed. This study measured the distance between overtaking motor vehicles and cyclists [e.g. vehicle passing distance (VPD)], to develop baseline metrics for tracking implementation of the three-foot passing law in Baltimore, MD and to assess risk factors for dangerous passes. ⋯ A multiple linear regression model was created, which explained 26% of the variability in VPD. Significant model variables were lane width, bicycle infrastructure, cyclist identity, and street identity. Interventions, such as driver education, signage, enforcement, and bicycle infrastructure changes are needed to influence driving behavior in Baltimore to increase motorist compliance with the three-foot law.
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Since the safety professionals are the key decision makers dealing with project safety and risk assessment in the construction industry, their perceptions of safety risk would directly affect the reliability of risk assessment. The safety professionals generally tend to heavily rely on their own past experiences to make subjective decisions on risk assessment without systematic decision making. Indeed, understanding of the underlying principles of risk assessment is significant. ⋯ The model (Q(2)REM) not only act as a useful supplementary guideline of risk assessment for the construction safety professionals, but also assists them to pinpoint the potential risks on site for the construction workers under respective trades of works through safety trainings and education. It, in turn, arouses their awareness on safety risk. As the Q(2)REM can clearly show the potential accident causes leading to different types of accident by trade of works, it helps the concerned safety professionals and parties to plan effective accident prevention measures with reference to the priority of the risk levels.
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Construction accident research involves the systematic sorting, classification, and encoding of comprehensive databases of injuries and fatalities. The present study explores the causes and distribution of occupational accidents in the Taiwan construction industry by analyzing such a database using the data mining method known as classification and regression tree (CART). ⋯ The results of this study show that the occurrence rules for falls and collapses in both public and private project construction industries serve as key factors to predict the occurrence of occupational injuries. The results of the study provide a framework for improving the safety practices and training programs that are essential to protecting construction workers from occasional or unexpected accidents.