Real-time Risk Analysis Research Articles

Real-time quantitative risk analysis and routing optimization of gaseous hydrogen tube trailer transport: A Bayesian network and Dijkstra algorithm combining approach

With increasing global energy demand, the volume and scale of hydrogen energy transportation are also on the rise. Gaseous hydrogen tube trailers, as the most flexible mode of hydrogen transport, present risk characteristics such as dynamism, suddenness, and potential for severe consequences. However, current methods for real-time risk assessment and route optimization during transportation are limited, lacking in both efficiency and accuracy. To address this issue, we propose a comprehensive and novel real-time risk assessment and path optimization system for gaseous hydrogen tube trailer transport. The System Theory Process Analysis (STPA) method is employed to identify risk factors associated with this transport modality. We combine a Bayesian network model with real-time observational data to derive dynamic failure probabilities for various routes. The potential consequences of transportation accidents are calculated using a computational model and computational fluid dynamics (CFD). By analyzing population density along the routes, we estimate the number of fatalities resulting from accidents, leading to a dynamic assessment of accident consequences. Finally, we consider societal impacts, economic costs, time costs, carbon emissions, the proximity to environmentally sensitive areas, and locations prone to fire and explosion hazards to select the optimal transportation route using Dijkstra's algorithm. The findings of this research will provide valuable insights for the safe management and sustainable development of gaseous hydrogen tube trailer transport.

Enhancing International Trade Security: Real-Time Risk Assessment in Brazilian Customs with Blockchain Technology

This study introduces an innovative approach by integrating blockchain technology with risk analysis methods for international trade. The practical application and tangible improvements observed in a real case study highlight the originality and value of this approach, offering a robust framework for future research and practical implementation. The security of exports presents a challenge for customs authorities, requiring the analysis of vast volumes of data from diverse sources and stakeholders. Blockchain technology offers a viable solution by facilitating the sharing of reliable and secure data among participants in the international supply chain. This study proposes a distributed risk analysis method based on Blockchain to detect and prevent fraud and irregularities in Brazilian exports. The technique aims to enhance efficiency and protection in customs clearance, providing real-time risk analysis feedback at various supply chain stages. The method includes four stages: data collection, risk classification, validation, and feedback. It employs a Model Form for Insertion into the Blockchain, the DBSCAN algorithm to identify out-of-pattern transactions (outliers) at the manufacturing stage, and a Monte Carlo simulation to analyze the volume and specific products exported from Rio Grande do Sul. We applied this method in a case study of beef exports from Brazil. Using this method led to significant improvements in processing times, operational costs, transparency, and security in the supply chain. The blockchain-based approach identified fraudulent activities, such as value tampering and undeclared goods, enhancing compliance and trust among trade partners. Implementing this method can significantly reduce operational costs and processing times in international trade while increasing security and transparency. This approach benefits customs authorities and private operators, promoting a more reliable and efficient commercial environment. Adopting blockchain technology in international trade can reduce fraudulent and illicit practices, promoting greater fairness and equity in commercial transactions. Increased transparency strengthens trust among various actors in the supply chain, fostering a sense of social responsibility.

Credit risk evaluation on technological SMEs in China

ABSTRACT China’s reform and opening-up policies have prioritized technological advancement, with technological SMEs driving employment and economic growth. Despite their significance, these SMEs face substantial financing and operational risks due to inadequate credit measurement tools. This study reviews the historical financing challenges of technological SMEs since the 1980s, summarizes their current credit risk status, and compares four modern credit risk models: Credit Metrics, Credit Risk+, Credit Portfolio View, and KMV. We propose a pioneering KMV Strategy for real-time risk analysis, contributing to accurate credit metrics for these SMEs. Finally, we suggest policies for managing their credit risks through prevention, control, and governance.

Evaluation of Power Grid Social Risk Early Warning System Based on Deep Learning

In the context of the continuous development of the power grid, the tasks of regulation, operation, and management are becoming increasingly complex, and the operation risks are also increasing dramatically. Sensor technology can deal with the impact of uncertain risk factors, such as extremely disastrous weather, equipment failure, and load fluctuation, on the power grid. Therefore, this article proposes a real-time risk analysis and early warning system for the power grid based on machine learning and combined with sensing technology—a stack self-coding (SSC) neural network prediction model—and introduces the functional composition of the system, clarifying the research content. The experiment compared the accuracy of power grid load forecasting between the SSC forecasting model and the fuzzy neural network (FNN) forecasting model and obtained the forecasting curves of a holiday, a workday, and a Sunday, as well as a comprehensive forecasting accuracy comparison. The experimental results showed that the SSC prediction model based on machine learning designed in this paper improved the prediction accuracy by 12.94% compared with the FNN model. The power grid risk can be assessed through load forecasting, and it is also of great significance for load dispatching and reducing generation costs.

A novel approach for fine-grained traffic risk characterization and evaluation of urban road intersections

An urban road intersection environment with low traffic risk is an essential component of sustainable cities. Various risk analysis and simulation techniques have been developed to measure and evaluate traffic risks. However, flexible assessment of intersection risk at a fine resolution remains a great challenge. This study proposes a novel approach for characterizing the field distribution of traffic risks at an urban road intersection and assessing traffic risks in multiple dimensions to increase the flexibility of risk assessment. First, we define a new traffic risk metric to quantify the risk of intersection traffic conflict points by combining their frequency and severity. Second, the plume diffusion model is introduced to model the non-linear relationship between traffic conflict points by considering the propagation of the traffic risk in the intersection. In this way, the entire intersection risk distribution map is derived for real-time risk analysis. Finally, a multidimensional hierarchical evaluation system of traffic risk at intersections is designed, including the traffic risk indicators at the intersection, path, and turn mode levels. The results of a case study in Wuhan, China, demonstrate the continuous characterization results of the risk field and provide multidimensional risk assessment index results, which are highly consistent with the cognitive results of manual risk scoring (up to 90% similarity). It also allows for risk assessment in a single path and turn modes through the risk field characterization and reveals the spatiotemporal dynamic risk distribution, thus providing an alternative tool for risk decision-making at urban road intersections.

Research on run-time risk evaluation method based on operating scenario data for autonomous train

Recent years witness the focus of the research of next-generation railways on risk situation awareness and safety decision-making to enhance the autonomy of unmanned trains. However, complex environmental factors make it difficult to assess the risks of train operation. Thus, it is of great necessity to clearly monitor the scenario parameters under which the train control system is designed to work, and to infer real-time risk through the collected scenario data. This paper first clarifies the key scenario parameters that need to be collected during the operation according to the concept of Operational Design Domain (ODD) and operating scenario. The key parameters and their dependencies are used to derive the Dynamic Bayesian Network (DBN) structure. Second, for data probability uncertainty, Fuzzy Set Theory is introduced, within the framework of which a fuzzy dynamic reasoning process is presented by monitoring the scenario data deviation. Finally, a case of real-time risk evaluation and analysis of the accident of Singapore MTR is explicated to demonstrate its contribution to operating data-based runtime risk analysis.

Risk analysis on corrosion of submarine oil and gas pipelines based on hybrid Bayesian network

Submarine pipeline is currently the safest and most economical transportation of subsea oil and gas. Corrosion is one of the main threats to submarine pipelines because of the corrosive of the sea. Therefore, it is significative to evaluate the corrosion risk of the submarine pipelines. In this work, a fault tree model is first employed to identify the corrosion hazards in the transportation process. To overcome the limitations of the fault tree analysis for the states of the events, a Bayesian network (BN) is established through mapping from the fault tree model. Because of the limitations of objective data, experts’ judgment combining fuzzy set theory are used to determine the parameters in the BN. The dynamic nodes in the BN are divided into real-time and discrete nodes, and the dynamic characteristics of them are displayed by the evidence nodes. In this way, when a new observation occurs, the real-time risk analysis of the corroded submarine pipeline is realized. Finally, a case study is carried out to verify the risk level variation with the observing events at different moments. Results shows that the proposed model can dynamically characterize the corrosion risk of the submarine pipeline and provide suggestions for the risk reduction.

Towards real-time ship collision risk analysis: An improved R-TCR model considering target ship motion uncertainty

Collision between ships is one of the major contributors to ship accidents. To facilitate the development of the real-time collision risk analysis model for collision avoidance, in this research, an improved Rule-aware Time-varying Collision risk Model is proposed, which considers the estimation of target ship motion and the corresponding uncertainty in the risk analysis process. To make the collision risk model more in line with the actual situation, ship maneuverability, the COLREGs, and good seamanship are considered and integrated into the framework of the TCR model. Firstly, the Gaussian process is used to predict the potential trajectories of the target ship. Secondly, the Probabilistic Velocity Obstacle (PVO) model is utilized to integrate the uncertainty of the target ship's motion into the collision risk model. The collision risk is therefore formulated as a ratio of available maneuvers leading to a collision to all available maneuvers. To verify the effectiveness of collision risk, two actual target ships and six groups of collision risk detection experiments under different encounter scenarios were carried out. Compared with the traditional collision risk index model and original R-TCR model, the collision risk detected by the Improved R-TCR model is closer to the actual situation.

Integrated real-time flood risk identification, analysis, and diagnosis model framework for a multireservoir system considering temporally and spatially dependent forecast uncertainties

Uncertainties in flood forecasts that result from error propagation in meteorological–hydrological simulation processes present spatial and temporal dependences that are the principal sources of risk to real-time flood control in a multireservoir system. Risk analysis models that neglect such dependences potentially yield biased results regarding flood control operations. To enhance the accuracy of flood risk analysis and inform decision making regarding real-time flood control for a multireservoir system, this study proposed an integrated flood risk identification, analysis, and diagnosis model framework that incorporates the multidimensional dependences of forecast uncertainties. The framework includes an uncertainty identification model that uses the copula function to characterize the joint probability function of the mixed dependent uncertainties. It also incorporates a real-time flood risk analysis module that couples simulation and optimization to convert the complex multiobjective operation under uncertainty into a tractable model capable of flexibly evaluating individual and joint flood risks. The third component is a risk diagnosis module that calculates risk contributions using conditional probability, and identifies stable and vulnerable reservoirs within the system to alter operational decisions toward risk reduction. Methodologies were verified through application to a mixed four-reservoir system in the Pi River Basin, China. The principal findings were as follows. (1) Positive-dominated dependences of forecast uncertainties were identified because of error propagation through the meteorological and rainfall–runoff forecasts within the spatial and temporal continuity connection, and the copula function accurately described and quantified the multidimensional temporal and spatial dependences of the uncertainties. (2) In neglecting the spatial dependences of forecast uncertainties, the joint flood risk of the reservoir system was underestimated; in ignoring high-order temporal dependences, individual flood risk was underestimated owing to biased estimation in error variance. (3) Systematic reorganization of the operational strategies of diagnosed stable and vulnerable reservoirs could provide guidance for reduction and homogenization of flood risks. The proposed framework could be used to improve the accuracy of flood risk analysis and support reliable real-time flood control operation of a multireservoir system.

Multi-Ship Collision Avoidance Decision-Making Based on Collision Risk Index

Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.

Convolutional neural networks with refined loss functions for the real-time crash risk analysis

The real-time crash risk analyses were proposed to establish the relationships between crash occurrence probability and pre-crash traffic operational conditions. Given its great application potentials that link with Active Traffic Management System (ATMS) for proactive safety management, it has become an important research area. Currently, researchers mainly developed the real-time crash risk analysis models with traffic flow descriptive statistics employed as explanatory variables and with re-sampled balanced dataset, which hold the limitations of insufficiently capturing the temporal-spatial traffic flow characteristics and failing to provide classification capabilities when deal with the imbalanced datasets. In this study, a Convolutional Neural Network (CNN) modelling approach with refined loss functions has been first time introduced to the real-time crash risk analyses. The primary objectives of the proposed CNN models are: (1) utilizing the tensor-based data structure to explore the multi-dimensional, temporal-spatial correlated pre-crash operational features; and (2) optimizing the loss functions to overcome the low classification accuracy issue brought by the imbalanced data. Data from the Shanghai urban expressway system were utilized for the empirical analysis. And a total of three types of loss functions, including traditional binary cross entropy, the α-weighted cross entropy and the focal loss, were introduced and being tested with varying ratios of crash and non-crash datasets. The modeling results show that the CNN model has better classification performance compared to the traditional Multi-layer Perceptrons (MLP) model with the tensor-based structure data. Besides, the developed CNN model with focal loss function has substantial classification enhancement under the imbalanced datasets. Finally, thedistributions of predicting probabilities for balanced and imbalanced datasets were plotted to understand the effects of the imbalanced dataset and revealed how the proposed CNN model with focal loss function improves the model performance.

Dynamic safety prewarning mechanism of human–machine–environment using computer vision

PurposeThis study provides a safety prewarning mechanism, which includes a comprehensive risk assessment model and a safety prewarning system. The comprehensive risk assessment model is capable of assessing nine safety indicators, which can be categorised into workers’ behaviour, environment and machine-related safety indicators, and the model is embedded in the safety prewarning system. The safety prewarning system can automatically extract safety information from surveillance cameras based on computer vision, assess risks based on the embedded comprehensive risk assessment model, categorise risks into five levels and provide timely suggestions.Design/methodology/approachFirstly, the comprehensive risk assessment model is constructed by adopting grey multihierarchical analysis method. The method combines the Analytic Hierarchy Process (AHP) and the grey clustering evaluation in the grey theory. Expert knowledge, obtained through the questionnaire approach, contributes to set weights of risk indicators and evaluate risks. Secondly, a safety prewarning system is developed, including data acquisition layer, data processing layer and prewarning layer. Computer vision is applied in the system to automatically extract real-time safety information from the surveillance cameras. The safety information is then processed through the comprehensive risk assessment model and categorized into five risk levels. A case study is presented to verify the proposed mechanism.FindingsThrough a case study, the result shows that the proposed mechanism is capable of analyzing integrated human-machine-environment risk, timely categorising risks into five risk levels and providing potential suggestions.Originality/valueThe comprehensive risk assessment model is capable of assessing nine risk indicators, identifying three types of entities, workers, environment and machine on the construction site, presenting the integrated risk based on nine indicators. The proposed mechanism, which adopts expert knowledge through Building Information Modeling (BIM) safety simulation and extracts safety information based on computer vision, can perform a dynamic real-time risk analysis, categorize risks into five risk levels and provide potential suggestions to corresponding risk owners. The proposed mechanism can allow the project manager to take timely actions.

Current status and advancement suggestions of ice-jam flood hazard and risk assessment

In many northern rivers, ice-jam flooding can be more severe than open-water flooding, leading to human casualties, damages to property and infrastructure, and adverse impacts on the ecology. Consequently, ice jam related flooding is a major concern for many riverside communities, water authorities, insurance companies, and government agencies. Ice-jam flood hazard delineation and risk analysis are important measures for flood preparation, mitigation, and management strategies. Although methodologies and techniques for open-water flood hazard and risk assessment are well established, methodologies and techniques for ice-jam flood hazard and risk assessment are often unavailable or less developed. In addition to this, a considerable number of studies have been conducted in the context of flood management, but a very limited number of studies have been carried out in real-time flood risk analysis during operational flood forecasting. In this paper, the current status of ice-jam flood hazard delineation and risk analysis is discussed. A framework for real-time risk analysis for operational flood forecasting is also discussed. Finally, current limitations and future requirements for developing effective ice-jam flood hazard delineation and risk analysis methodologies are provided.

R-HUMO: A Risk-Aware Human-Machine Cooperation Framework for Entity Resolution with Quality Guarantees

Even though many approaches have been proposed for entity resolution (ER), it remains very challenging to enforce quality guarantees. To this end, we propose a r isk-aware HUman-Machine cOoperation framework for ER, denoted by r -HUMO. Built on the existing HUMO framework, r -HUMO similarly enforces both precision and recall guarantees by partitioning an ER workload between the human and the machine. However, r -HUMO is the first solution that optimizes the process of human workload selection from a risk perspective. It iteratively selects human workload by real-time risk analysis based on the human-labeled results as well as the pre-specified machine metric. In this paper, we first introduce the r -HUMO framework and then present the risk model to prioritize the instances for manual inspection. Finally, we empirically evaluate r -HUMO's performance on real data. Our extensive experiments show that r -HUMO is effective in enforcing quality guarantees, and compared with the state-of-the-art alternatives, it can achieve desired quality control with reduced human cost.

Real-time risk analysis of road tanker containing flammable liquid based on fuzzy Bayesian network

The risk of road transportation of flammable liquid is of great uncertainty due to the time-varying conditions of the passing locations and the environment, which leads to challenging risk analysis. In this work, a real-time risk analysis method for road tanker transportation based on the fuzzy Bayesian network (FBN) is proposed. The bow-tie model is first employed to identify hazards in flammable liquid road transportation systems and shows risk evolution. The framework of the Bayesian network (BN) is then determined accordingly. In the case that the historical statistics of accidents are limited, a probabilistic estimation model that combines expert judgment and fuzzy set theory is established to determine the prior probabilities and the conditional probabilities of the BN nodes. Case studies of typical road tanker transportation accidents were carried out to show the risk level variation with both the internal and external conditions at different moments. Sensitivities of the parent nodes were analyzed, and the critical factors leading to accidents were identified. Studies show that this method can dynamically characterize the changes in both the probabilities and the consequence levels of road tanker transport accidents. Based on the vehicle’s GPS data and the local environment, the proposed method can provide an estimation of the real-time risk for road tankers.

Real-time risk analysis method for diagnosis and warning of offshore downhole drilling incident

Downhole drilling incidents in offshore oil and gas fields pose a threat to wellbore safety. Moreover, they are likely to develop into hazardous mishaps owing to delayed detection and false alarm. Real-time risk diagnosis and warning analysis are of substantial importance to gain time for reconstructing the wellbore pressure balance and thereby improve the well-control safety at an early stage. This paper presents a method for comprehensively analyzing the drilling parameters and data to estimate drilling downhole risk in real-time. The relative difference is introduced to capture dynamic characteristics for quantifying the trend evolution of the drilling data. The method involves models for determining the dynamic safety thresholds to diagnose downhole incidents. Further, it imparts the capability for preventing measurement errors and unapparent trends caused by ambiguous data. This approach is based on the Bayesian inference theory and incorporates the Gaussian rules to handle uncertainty issues and reduce false alarm rates. In contrast to previous works, the non-linear dependence of the drilling parameters is considered for the warning probability estimation. Case studies that were focused on lost-circulation incident for an offshore field well are used to illustrate the feasibility of the proposed method and to demonstrate that three alarm-levels based on risk propagation can be triggered rapidly for decision-making.

Emergency Evacuation Plan for Hazardous Chemicals Leakage Accidents Using GIS-based Risk Analysis Techniques in South Korea.

Despite improvements in chemical safety management systems, incidents involving the release of hazardous chemicals continue to happen. In some cases, they result in the evacuation of residents. For hazardous chemical release accidents, an evacuation plan needs to be selective enough to consider both the indoor and outdoor concentrations of nearby buildings and the time in which the maximum allowable concentration may occur. In this study, a real-time risk analysis tool was developed based on the geographic information system (GIS) in order to establish the emergency response and risk communication plan for effectively assisting decision-making personnel. A selective evacuation plan was also established by a proposed assessment module considering the indoor/outdoor pollution concentration of buildings and the release duration time of chlorine gas leakage. The GIS-based simulated modules were performed based on eleven buildings of Ulsan city, located near an industrial cluster and home to a high population density. As a result of the simulated real-time risk assessment, only four buildings were affected by chlorine gas concentration according to wind direction and diffusion time. In addition, it was considered effective to establish an indoor/outdoor evacuation plan as opposed to an outdoor evacuation plan which is outside the range of the damage. Subsequently, an emergency evacuation plan was established with the concentration of a hazardous chemical according to the decision-making matrix. This study can enlighten the real-time emergency risk assessment based on GIS while effectively supporting the emergency action plans in response to the release of hazardous chemicals in clustered plants and the community.

Research on Quantitative Risk Assessment Method of Packaged Cargoes Carried By Ship Based on Online Dynamic Big Data Fusion Technology

In this paper, a quantitative risk assessment method of packaged cargoes carried by ship based on online dynamic big data fusion technology was researched. In recent years, major accidents such as oil spill, fire, explosion and ship loss occurred frequently in shipborne cargo transportation. Due to the lack of real-time dynamic risk analysis, both early warning and emergency handling of those accidents are extremely difficult. In this paper, a risk transmission model was built, which based on the flow chain for cargo transportation. A system including the analysis of cargo transportation risk and quantitative assessment model based on real-time dynamic big data fusion technology was built. Finally, a scientific quantitative assessment method was formed. And a container ship passing through Shenzhen waters was taken as an example in order to verify the practicality of the method. In this way, it can realize the quantitative assessment of risk (especially the risk of dangerous packaged cargoes)in the waterage. What’s more, according to the assessment results, it can analyze the high-risk factors and high-risk parts in the process of waterage, and then realize the informationalized and modernized intelligent supervision in a whole process and all round way, which will effectively improve the level of accident prevention in China.

Approach-level real-time crash risk analysis for signalized intersections

Intersections are among the most dangerous roadway facilities due to the complex traffic conflicting movements and frequent stop-and-go traffic. However, previous intersection safety analyses were conducted based on static and highly aggregated data (e.g., annual average daily traffic (AADT), annual crash frequency). These aggregated data may result in unreliable findings simply because they are averages and cannot represent the real conditions at the time of crash occurrence. This study attempts to investigate the relationship between crash occurrence at signalized intersections and real-time traffic, signal timing, and weather characteristics based on 23 signalized intersections in Central Florida. The intersection and intersection-related crashes were collected and then divided into two types, i.e., within intersection crashes and intersection entrance crashes. Bayesian conditional logistic models were developed for these two kinds of crashes, respectively. For the within intersection models, the model results showed that the through volume from “A” approach (the traveling approach of at-fault vehicle), the left turn volume from “B” approach (near-side crossing approach), and the overall average flow ratio (OAFR) from “D” approach (far-side crossing approach), were found to have significant positive effects on the odds of crash occurrence. Moreover, the increased adaptability for the left turn signal timing of “B” approach and more priority for “A” approach could significantly decrease the odds of crash occurrence. For the intersection entrance models, average speed was found to have significant negative effect on the odds of crash occurrence. The longer average green time and longer average waiting time for the left turn phase, higher green ratio for the through phase, and higher adaptability for the through phase can significantly improve the safety performance of intersection entrance area. In addition, the average queue length on the through lanes was found to have positive effect on the odds of crash occurrence. These results are important in real-time safety applications at signalized intersections in the context of proactive traffic management.

Using Bayesian network for safety risk analysis of diaphragm wall deflection based on field data

This study proposes a risk analysis model based on the Bayesian network for diaphragm wall deflection to ensure safety during excavation. Compared with the traditional methods, such as fault tree analysis, the proposed approach can more accurately illustrate the dynamic and updated features of hydrogeological design and construction variables as construction progresses on the basis of metro construction field data, which can address the risks in uncertain environments and achieve dynamic control during the entire construction process. The procedures include risk identification, sample collection, model building and validation, risk analysis and reasoning, and real-time control measures. The proposed model was applied in the metro station construction project, in Wuhan Metro Line 7. Results showed that the proposed risk analysis method can achieve real-time risk analysis, which includes pre-accident, during-construction, and post-accident control, to ensure construction safety at the station.