Dynamic Safety Management Research Articles

Dynamic safety management model for rail traffic control

In rail transport, increasing emphasis has been placed in recent years on improving safety levels. Therefore, more requirements and legal documents require risk analyses to be carried out at various stages of investment implementation. One of the leading legal documents that introduce the obligation to monitor risk is Directive (EU) 2016/798 of the European Parliament and of the Council of 11 May 2016 on railway safety and Commission Implementing Regulation (EU) No 402/2013 of 30 April 2013 on the common safety method for risk evaluation and assessment and re-pealing Regulation (EC) No 352/2009. Additionally, for traffic control systems, the requirements of CENELEC standards are mandatory. These documents present the subject of safety level and show its relation with the safety targets defined in the railway system, including the different ways of measuring them. Methods are also available to analyse the safety level of railway system components in detail, both at the level of individual components, subsystems, and the whole national railway system. However, after conducting an in-depth analysis of the literature, the authors of the article indicate that these methods are not consistent with each other. There is no method defined to present the direct relation of the safety level of the components of the system on the achievement of safety targets for the national railway system. The research and analysis aimed to define an approach, a method that would meet all legal requirements but at the same time would allow to clearly and reliably determine the safety level of the railway system. To define a unified approach, the authors of the article propose to develop a model of a dynamic object - a railway system safety model, which has also been verified on accurate safety data in rail transport in recent years. This model organises the process of safety management on railways and allows to determine values influencing the achievement of safety targets on an assumed level.

Modeling the dynamic safety management of buildings adjacent to karst shield construction: An improved cloud Bayesian network

Shield construction in karst tunnels inevitably causes settlement deformation and even damage to surrounding buildings. To accurately evaluate the security of neighboring buildings, a hybrid method combining an improved cloud model (CM) and a Bayesian network (BN) is proposed for dynamic security analysis and the evaluation of neighboring buildings. First, through engineering practice, simulation analysis and literature research, the safety risk factors and evaluation criteria for adjacent buildings influenced by shield construction in karst areas are determined, and a BN for safety evaluations of adjacent buildings is constructed. The improved CM is used to model the uncertainty of the risk state, realize the discretization of continuous data and obtain prior knowledge. By improving the cloud Bayesian network (CBN) to perform three kinds of comprehensive analyses, including forward prediction reasoning in advance, sensitivity analysis and reverse risk diagnosis for events, and appropriate risk management and control measures, the dynamic safety perception and control of buildings adjacent to karst shield construction is realized. The data for buildings under construction adjacent to Guiyang Rail Transit Line 3 are used to demonstrate the applicability and effectiveness of the proposed method. The contributions of the research results are as follows: (a) The key risk factors affecting the safety of buildings adjacent to shield construction areas in karst zones are extracted, and an evaluation index system and an evaluation standard are established; (b) An improved CM is used to describe the fuzziness and uncertainty in the process of node state discretization in a risk inference network, thus improving the prediction and diagnosis performance of BN security evaluation models; (c) The improved cloud Bayesian model is applied to construct a knowledge representation-based safety model of buildings adjacent to karst shield construction areas, and accurate perception and control diagnosis results are obtained for effective safety risk prediction and control, providing a reference for other similar projects.

Dynamic Safety Management on the Key Equipment of Coal Gasification Based on Dbt-Dbn Method

Gasifier system is one of the important components of coal gasification device. The technical characteristics of this system mainly lie in the following facts as huge technical scale and high complexity, and there is a dynamic correlation between the failure modes of gasification equipment. Traditional safety analysis methods such as fault tree and bow-tie diagram suffer from drawbacks as being static and ineffective in handling uncertainty, which hamper their application to risk analysis of process systems. This paper presents a newly developed model based on Dynamic Bow-Tie (DBT) and Dynamic Bayesian network (DBN) for quantitative dynamic risk assessment of gasifier system. In the meantime, in order to cope with the uncertainty of the failure data, fuzzy numbers and the defuzzification method are used to transform the experts' language into the failure rates. The results showed that dynamic risk assessment can solve the difficulties dealing with complex dynamic systems which have process variables and characteristics such as multiple, failure correlations, and noncoherence. And it also has important theoretical significance and application value for coal chemical industry to improve the scientificity of risk assessment.

Dynamic study of critical factors of explosion accident in laboratory based on FTA

Laboratory safety has been widely concerned, but fire, explosion, poisoning and other accidents occur from time to time. Among them, the proportion of safety accidents, casualties and economic losses caused by explosion are relatively high. In particular, the safety, reliability and maintainability of laboratories in colleges and universities and scientific research institutes should be evaluated and optimized. Therefore, it is necessary to use efficient, reliable and economical methods to identify and monitor the key factors that will cause explosion accidents in the laboratory. In this paper, through the combination of fault tree analysis (FTA) and binary decision diagram (BDD), the three main sources of explosion accidents in laboratory (gas cylinder, pressure device and hazardous chemicals) are analyzed quantitatively. When using BDD to simplify FT operation, a variety of event sorting methods are compared. Finally, it is found that “Level” and “AND” methods can provide a more convenient and reasonable number of cut sets. Considering different methods to measure the importance of basic events, it is found that similar results can be obtained. The importance of Critical is used to identify the importance of events, combined with its change with time, the change of importance is analyzed dynamically. The key factors from four aspects of “human”, “machine”, “environment” and “management” in laboratory explosion accident are determined. It provides a new idea for the dynamic safety management and maintenance of the laboratory, and puts forward some ideas for the new research content at the end of this paper.

Application of a Rule-Based Approach in Real-Time Crash Risk Prediction Model Development Using Loop Detector Data

Objectives: There is a growing trend in development and application of real-time crash risk prediction models within dynamic safety management systems. These real-time crash risk prediction models are constructed by associating crash data with the real-time traffic surveillance data (e.g., collected by loop detectors). The main objective of this article is to develop a real-time risk model that will potentially be utilized within traffic management systems. This model aims to predict the likelihood of crash occurrence on motorways.Methods: In this study, the potential prediction variables are confined to traffic-related characteristics. Given that the dependent variable (i.e., traffic safety condition) is dichotomous (i.e., “no-crash” or “crash”), a rule-based approach is considered for model development. The performance of rule-based classifiers is further compared with the more conventional techniques like binary logistic regression and decision trees. The crash and traffic data used in this study were collected between June 2009 and December 2011 on a part of the E313 motorway in Belgium between Geel-East and Antwerp-East exits, on the direction toward Antwerp.Results: The results of analysis show that several traffic flow characteristics such as traffic volume, average speed, and standard deviation of speed at the upstream loop detector station and the difference in average speed on upstream and downstream loop detector stations significantly contribute to the crash occurrence prediction. The final chosen classifier is able to predict 70% of crash occasions accurately, and it correctly predicts 90% of no-crash instances, indicating a 10% false alarm rate.Conclusions: The findings of this study can be used to predict the likelihood of crash occurrence on motorways within dynamic safety management systems.

Towards STAMP Based Dynamic Safety Management of Eco-Socio-Technical Maritime Transport System

Under an increasing maritime transport in the Baltic Sea, and especially in the Gulf of Finland, the possibility of environmental harm and accidents due to shipping pressure is growing. To counteract increasing risks, adequate measures for accident prevention and mitigation of environmental consequences are critically important. The Gulf of Finland Mandatory Ship Reporting System (GOFREP) is a the complex socio-technical maritime transport safety management system that has been in efficient operation since 2004 and it is open to further improvement and development. However, environmental safety is not explicitly covered by the GOFREP so far. According to our working hypothesis, the Systems Theoretic Accident Models and Processes (STAMP) can be extended beyond the area of socio-technical system safety into realm of complex eco-socio-technical systems safety. This paper attempts to demonstrate the conceptual potential of STAMP based on adaptive management of the Maritime Spatial Planning processes and integrated safety management of eco-socio-technical maritime transport system.

Development of a Real-Time Crash Risk Prediction Model Incorporating the Various Crash Mechanisms Across Different Traffic States

Objective: This study aimed to identify the traffic flow variables contributing to crash risks under different traffic states and to develop a real-time crash risk model incorporating the varying crash mechanisms across different traffic states.Methods: The crash, traffic, and geometric data were collected on the I-880N freeway in California in 2008 and 2009. This study considered 4 different traffic states in Wu's 4-phase traffic theory. They are free fluid traffic, bunched fluid traffic, bunched congested traffic, and standing congested traffic. Several different statistical methods were used to accomplish the research objective.Results: The preliminary analysis showed that traffic states significantly affected crash likelihood, collision type, and injury severity. Nonlinear canonical correlation analysis (NLCCA) was conducted to identify the underlying phenomena that made certain traffic states more hazardous than others. The results suggested that different traffic states were associated with various collision types and injury severities. The matching of traffic flow characteristics and crash characteristics in NLCCA revealed how traffic states affected traffic safety. The logistic regression analyses showed that the factors contributing to crash risks were quite different across various traffic states. To incorporate the varying crash mechanisms across different traffic states, random parameters logistic regression was used to develop a real-time crash risk model. Bayesian inference based on Markov chain Monte Carlo simulations was used for model estimation. The parameters of traffic flow variables in the model were allowed to vary across different traffic states. Compared with the standard logistic regression model, the proposed model significantly improved the goodness-of-fit and predictive performance.Conclusions: These results can promote a better understanding of the relationship between traffic flow characteristics and crash risks, which is valuable knowledge in the pursuit of improving traffic safety on freeways through the use of dynamic safety management systems.

Predicting crash likelihood and severity on freeways with real-time loop detector data

Real-time crash risk prediction using traffic data collected from loop detector stations is useful in dynamic safety management systems aimed at improving traffic safety through application of proactive safety countermeasures. The major drawback of most of the existing studies is that they focus on the crash risk without consideration of crash severity. This paper presents an effort to develop a model that predicts the crash likelihood at different levels of severity with a particular focus on severe crashes. The crash data and traffic data used in this study were collected on the I-880 freeway in California, United States. This study considers three levels of crash severity: fatal/incapacitating injury crashes (KA), non-incapacitating/possible injury crashes (BC), and property-damage-only crashes (PDO). The sequential logit model was used to link the likelihood of crash occurrences at different severity levels to various traffic flow characteristics derived from detector data. The elasticity analysis was conducted to evaluate the effect of the traffic flow variables on the likelihood of crash and its severity.The results show that the traffic flow characteristics contributing to crash likelihood were quite different at different levels of severity. The PDO crashes were more likely to occur under congested traffic flow conditions with highly variable speed and frequent lane changes, while the KA and BC crashes were more likely to occur under less congested traffic flow conditions. High speed, coupled with a large speed difference between adjacent lanes under uncongested traffic conditions, was found to increase the likelihood of severe crashes (KA). This study applied the 20-fold cross-validation method to estimate the prediction performance of the developed models. The validation results show that the model's crash prediction performance at each severity level was satisfactory. The findings of this study can be used to predict the probabilities of crash at different severity levels, which is valuable knowledge in the pursuit of reducing the risk of severe crashes through the use of dynamic safety management systems on freeways.