Proactive Safety Management System Research Articles

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

Safety Culture Approach For Healthcare And Nursing Institutions

Abstract There are a few ways how to increase the rate of employees’ safety culture at an institution, one of these is a positive safety culture, which includes their proper attitudes, safety-related values, staff members’ professional competences, and their willingness to work. The key element in the offering of quality and safe services in healthcare is a positive safety quality according to definitions. The aim of the current paper is to identify potential predictors of employees’ safety behaviour and determine safety culture subcultures that support operational manager to develop proactive safety management systems (SMSs) and offer safety of patients and employees. Two independent surveys were the basis of the new proposed approach. Quantitative method approach was selected to investigate safety culture subcultures in Estonian healthcare and nursing institutions. According to the proposed approach, there are crucial subcultures (e.g., just, reporting, learning, professional competences, and psychosocial well-being), which should be developed and periodically evaluated within an organisation. This measurement should have a clear and shared understanding of patient safety goals and occupational health and safety (OHS). The general management of organisations should consider implementation of assessment as a proactive approach to use the SMSs. The sustainability and proactivity of the proposed approach lies in defining action plans for continuous improvement and employees’ involvement in patient safety and OHS management.

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements

Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent advancements in artificial intelligence, sensor fusion and algorithms have brought about the introduction of a proactive safety management system closer to reality. The basic prerequisite for developing such a system is to have a reliable crash prediction model that takes real-time traffic data as input and evaluates their association with crash risk. Since the early 21st century, several studies have focused on developing such models. Although the idea has considerably matured over time, the endeavours have been quite discrete and fragmented at best because the fundamental aspects of the overall modelling approach substantially vary. Therefore, a number of transitional challenges have to be identified and subsequently addressed before a ubiquitous proactive safety management system can be formulated, designed and implemented in real-world scenarios. This manuscript conducts a comprehensive review of existing real-time crash prediction models with the aim of illustrating the state-of-the-art and systematically synthesizing the thoughts presented in existing studies in order to facilitate its translation from an idea into a ready to use technology. Towards that journey, it conducts a systematic review by applying various text mining methods and topic modelling. Based on the findings, this paper ascertains the development pathways followed in various studies, formulates the ubiquitous design requirements of such models from existing studies and knowledge of similar systems. Finally, this study evaluates the universality and design compatibility of existing models. This paper is, therefore, expected to serve as a one stop knowledge source for facilitating a faster transition from the idea of real-time crash prediction models to a real-world operational proactive traffic safety management system.

Introducing Routine Measurement of Healthcare Worker's Well-being as a Leading Indicator for Proactive Safety Management Systems Based on Resilience Engineering

Although the need to improve patient safety is widely recognized, there is general agreement that ambitious targets for improvement have not been met. Resilience Engineering focuses on complex adaptive systems and provides a holistic view of interacting elements that adjust, adapt and reinforce one another in response to emerging forces. This provides a framework to understand both why organizations succeed or fail and, therefore, how to improve their reliability. One component that is often identified as a key factor in safety and reliability by complex adaptive systems and RE are the individuals within the system and their relationship to organizational performance. The goal of this paper is to identify and transfer knowledge regarding healthcare worker's well-being from health sciences to the development of proactive safety management systems, with a specific emphasis on the RE framework as offering the most potential for this. To achieve this, the paper will: First provide a review of relevant determinants associated with a healthcare worker's well-being that suggest both risk and contribution to the success of system performance. Next, the benefits of utilizing indicators for proactively monitoring system performance in healthcare will be reviewed. This will have a particular focus on the benefits and challenges to the routine monitoring of the healthcare worker's well-being as a specific indicator for system performance and patient safety. Finally, further research necessary to address a number of critical key factors to investigate the utility, validity, and usability of the design and implementation of healthcare worker well-being indicators into healthcare safety management systems are identified.

A data fusion framework for real-time risk assessment on freeways

The increased deployment of non-intrusive detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMSs) provides access to real-time traffic data from multiple sources. The availability of such rich data enhances the reliability of travel time estimation and route guidance systems, however, utilization of these data is absent in the context of proactive safety management systems. This paper presents a framework for real-time risk assessment on a freeway in Colorado by fusing data from two different detection systems (AVI and RTMS), real-time weather and roadway geometry. Stochastic Gradient Boosting (SGB), a relatively recent and promising machine learning technique is used to calibrate the models. SGB’s key strengths lie in its capability to fit complex nonlinear relationships, handling different types of predictors (nominal and categorical) and accommodating missing values with no need for prior transformation of the predictor variables or elimination of outliers. Boosting multiple simple trees together overcomes the drawback of single tree models of poor prediction accuracy and provides fast and superior predictive performance. The proposed framework is considered a good alternative for real-time risk assessment on freeways because of its high estimation accuracy, robustness and reliability.

A study of a fatigue management system for long LNG carriers using a new fatigue damage sensor

A pro-active safety management system for ship structures that quantifies the aging effect such as by fatigue and corrosion on the ship's structural integrity is proposed as a new approach to the ship's structural surveys that have conventionally been made according to a more passive management system. Some of the authors have developed a compact fatigue damage sensor that can detect accumulated fatigue damage in welded structures by attachment to the member and inspection after a certain period. The authors have applied this sensor to the hull structures of an LNG carrier for experimental purposes, ascertained its long-term durability under the severe environment to which the ship was exposed and obtained quantitative data on the accumulated fatigue damage. In this paper, the authors propose a fatigue management system using this sensor called the Hull Aging Management System and propose it as a pro-active safety management system for LNG carrier hull structural aging and introduce a method to improve the accuracy of accumulated fatigue damage detection by using this sensor.