Dynamic Risk Assessment Research Articles

Advancement in ice-jam flood risk management: Integrating dynamic adaptive behavior by an agent-based modeling in Fort McMurray, Canada

Ice-jam flooding is a prevalent extreme event that impacts flood hazard and vulnerability. We introduce a conceptual model framework for Dynamic Ice-jam Flood Risk Assessment (DIFRA). DIFRA integrates ice-jam flood hazard, ice-jam flood risk, and human adaptation. Using agent-based modeling, we captured top-down (artificial breakup) and bottom-up (flood-proofing) adaptive behavior. Our study in Fort McMurray, Canada, shows the complex interaction between micro-level behaviors and macro-level phenomena over time. Our variance-based global sensitivity analysis shows the role of dynamic adaptive behavior in ice-jam flood risk, where the artificial breakage by the government can lead to a regime shift and a decrease in the ice-jam flood risk. However, it can also decrease the number of newly adapted residents to flood-proofing and the role of residents in ice-jam flood risk. DIFRA offers a comprehensive approach to understanding and managing ice-jam flood risk, with potential applications to similar riverine communities in cold regions.

Dynamic risk assessment of re-construction dust from abandoned industrial buildings: A human-dust interaction perspective

Health risk assessment (HRA) is a well-established and widely used tool for quantitatively assessing the health status and likelihood of future illness and death of an individual or group. The dynamic properties of reconstruction dust from abandoned industrial buildings significantly affect HRA results. In recent years, dynamic health risk assessment (DHRA) has become an emerging research field. However, research on DHRA is still in the preliminary stage, and the research and application of its dynamic factors lack systematic analysis. Examining data transformation pathways through the U.S. Environmental Agency (USEPA) standard HRA framework, this study formalizes two distinct elements of dynamic assessment, namely, dynamic construction procedures and dynamic construction behaviour from a human-dust interaction perspective. Then, a DHRA framework for reconstruction dust of abandoned industrial buildings is proposed so that the aforementioned dynamic evaluation factors can be integrated into the HRA framework. Finally, feasible application solutions and data availability of dynamic factors are discussed, and case studies are carried out to better support DHRA application. The results show that the evaluation results are sensitive to different degrees of dynamic evaluation factors, and DHRA can provide more authentic and reliable evaluation results.

A Dynamic Threat Prevention Framework for Autonomous Vehicle Networks based on Ruin-theoretic Security Risk Assessment

In recent years, Autonomous Vehicle Networks (AVNs) have gained significant attention for their potential to make transportation safer and more efficient. These networks rely on Vehicle-to-Vehicle (V2V) communication to exchange critical information, such as location, speed, and driving intentions. However, V2V communication also introduces security vulnerabilities that can be exploited to compromise the safety and privacy of drivers and passengers. Malicious or selfish drivers can potentially intercept, modify, and manipulate V2V communication, causing confusion among vehicles or stealing sensitive data. Therefore, in order to identify and mitigate security threats that could jeopardize V2V communication in AVNs, the implementation of a threat prevention framework is imperative. This paper presents a threat prevention framework that assesses security risks dynamically to facilitate secure message forwarding in V2V communication. First, we propose a dynamic risk assessment technique that utilizes the PIER approach to evaluate the level of security threats posed to V2V communication, and ultimately generate a risk score. Second, we develop a security decay assessment method that utilizes ruin theory to continuously monitor security risk within the AVNs. Third, we design a risk-aware message forwarding protocol based on coalitional game theory to facilitate secure V2V communication. Our experiments using the simulator Veins demonstrate the efficiency and scalability of the proposed framework in preventing potential damage caused by common security threats and enhancing the security of the Automated Highway System (AHS).

Zenith Armor : Advancing Security with Zero Trust Measures

In an era of unprecedented digital connectivity and evolving cyber threats, traditional security paradigms have proven insufficient to safeguard sensitive information. This research paper introduces "Zenith Armor," a comprehensive exploration and implementation of Zero Trust Architecture (ZTA) in the realm of cybersecurity. The paper delves into the core principles of ZTA, emphasizing the imperative shift from implicit trust to continuous verification in securing organizational assets. "Zenith Armor" stands as a testament to the evolving landscape of security frameworks, challenging conventional models by prioritizing identity verification, least privilege access, micro-segmentation, and continuous monitoring. The framework not only mitigates the risks associated with unauthorized access but also addresses the lateral movement of threats within a network, enhancing overall resilience. This paper outlines the essential components of Zenith Armor, emphasizing its dynamic risk assessment capabilities, stringent access control policies, and encryption measures for safeguarding data. Additionally, the framework's applicability to diverse devices, coupled with user education initiatives, creates a robust security ecosystem. The implementation extends beyond traditional network perimeters, incorporating adaptive security measures, and continuous updates. Furthermore, "Zenith Armor" recognizes the significance of a proactive approach to security incidents. A well-defined incident response plan, coupled with vendor and third-party access controls, ensures a swift and effective response to potential breaches. The research paper concludes with a reflection on the transformative potential of "Zenith Armor" in redefining organizational security postures. By embracing the Zero Trust paradigm, organizations can fortify their defenses against an ever-evolving threat landscape, thereby establishing a new zenith in cybersecurity resilience. Keywords—delves ,imperative, micro-segmentation,proactive, zenith.v

A dynamic risk assessment model based on multidimensional and quantitative inference theory

With the continuous increase in petroleum consumption and the trend towards more refined management, crude oil storage tanks are playing an increasingly crucial role in the petrochemical industry. However, the corrosion issue of crude oil storage tanks has consistently been a major factor limiting their service life, and leaks in crude oil tanks are often attributed to corrosion of the tank bottom. Due to the high cost and complexity associated with tank opening inspections, in this paper, directly observable information and physical quantities are utilized as indirect evidence, combined with Bayesian network, to indirectly determine the probability of tank corrosion. This approach allows for selective internal tank inspections, enabling effective risk assessment while also facilitating cost savings. Some parameters in the Bayesian network model are obtained through a probability estimation model combining expert experience and fuzzy set theory. Finally, the feasibility of the method is demonstrated by citing examples. The current model can offer a relatively reliable reference for evaluating the risks associated with tanks and their ancillary facilities, while also reducing the costs of tank inspections to some extent.

A multi-task learning risk assessment method for the chemical process industry

Dynamic risk assessment is essential to prevent major accidents in the chemical process industry. The Prediction of accident risk categories, risk likelihood, and risk severity are three fundamental tasks for risk assessment. Single-task learning-based approaches, which merely analyze accident occurrence or quantify the likelihood and severity of an accident risk, often tend to ignore task relevance and inhibit the sharing of critical feature information. This oversight might lead to poor prediction performance. In this study, we propose a multi-task learning model, i.e., Robustly Progressive Layered Extraction (RoPLE), to systematically predict accident risk category, risk likelihood, and risk severity. The RoPLE consists of two parts: a robustly optimized pre-trained module for extracting semantic features from hazard analysis reports (unstructured data) and a progressive layered extraction module for fusing semantic features with features of leading indicators (structured data), which portray the risk state of equipment, workers, environment, and management in the chemical process industry. The progressive layered extraction module contains a multi-level extraction network and a tower network for these three tasks. The multi-level extraction network extracts higher-level shared information, while the tower network extracts task-specific feature information. Empirical experiments demonstrate the feasibility of our model on a real risk assessment dataset in Beijing’s chemical process industry. The RoPLE obtains high F1-scores of 63.1%, 79.5%, and 86.2% for the three tasks, and significantly outperforms other state-of-the-art single-task methods.

Predictors of recurrence after total thyroidectomy in 1,611 patients with papillary thyroid carcinoma: postoperative stimulated serum thyroglobulin and ATA initial and dynamic risk assessment.

Despite a favorable prognosis, some patients with papillary thyroid carcinoma (PTC) develop recurrence. The objective of this study was to examine the impact of the combination of initial American Thyroid Association (ATA) risk stratification with serum level of postoperative stimulated thyroglobulin (s-Tg) in predicting recurrence in patients with PTC and compare the results with an assessment of response to initial therapy (dynamic risk stratification). We retrospectively analyzed 1,611 patients who had undergone total thyroidectomy for PTC, followed in most cases (87.3%) by radioactive iodine (RAI) administration. Clinicopathological features and s-Tg levels obtained 3 months postoperatively were evaluated. The patients were stratified according to ATA risk categories. Nonstimulated thyroglobulin levels and imaging studies obtained during the first year of follow-up were used to restage the patients based on response to initial therapy. After a mean follow-up of 61.5 months (range 12-246 months), tumor recurrence was diagnosed in 99 (6.1%) patients. According to ATA risk, recurrence was identified in 2.3% of the low-risk, 9% of the intermediate-risk, and 25% of the high-risk patients (p < 0.001). Using a receiver operating characteristic curve approach, a postoperative s-Tg level of 10 ng/mL emerged as the ideal cutoff value, with positive and negative predictive values of 24% and 97.8%, respectively (p < 0.001). Patients with low to intermediate ATA risk with postoperative s-Tg levels < 10 ng/mL and excellent response to treatment had a very low recurrence rate (<0.8%). In contrast, higher recurrence rates were observed in intermediate-riskto high-risk patients with postoperative s-Tg > 10 ng/mL and indeterminate response (25%) and in those with incomplete response regardless of ATA category or postoperative s-Tg value (38.5-87.5%). Using proportion of variance explained (PVE), the predicted recurrence using the ATA initial risk assessment alone was 12.7% and increased to 29.9% when postoperative s-Tg was added to the logistic regression model and 49.1% with dynamic risk stratification. The combination of ATA staging system and postoperative s-Tg can better predict the risk of PTC recurrence. Initial risk estimates can be refined based ondynamic risk assessment following response to therapy, thus providing a useful guide for follow-up recommendations.

Examining Trajectories of Change on the Dynamic Risk Assessment for Offender Re-Entry (DRAOR).

Dynamic risk scales have largely been evaluated using singular assessment scores, including those obtained at the start of supervision. While this approach includes assessment of dynamic factors, it ignores changes with reassessment, failing to examine whether an instrument is truly dynamic in nature. This is problematic, as proximal risk assessments have consistently outperformed baseline assessments in the prediction of recidivism. In the current study, we examined the dynamic properties of the Dynamic Risk Assessment for Offender Reentry (DRAOR) in 4,736 adults on community supervision in Iowa, United States (N = 33,965 assessments). As expected, while clients demonstrated statistically significant changes on the DRAOR domains over time, changes were small in magnitude. We also examined the predictive validity of baseline and proximal DRAOR total and domain scores on criminal recidivism and revocation in a larger sample of 11,421 adults in the same jurisdiction. While DRAOR baseline scores did predict both outcomes, prediction did not improve with proximal scores. This conflicted with expected findings from previous research on the DRAOR in New Zealand. The results of both of these research questions indicate there was an overall lack of change reflected in this sample. Potential issues regarding implementation fidelity are discussed. Additional research is needed to examine the dynamic properties of the DRAOR in Iowa given the importance of reassessment data in community corrections.

Food safety risk assessment and countermeasures in China based on risk matrix method

BackgroundFood safety has always been a major concern for people, and frequent food safety incidents pose a serious threat to people’s health and safety. To identify high-risk patterns in the field of food safety and assist regulatory authorities in scientifically managing food safety risks, this study aims to evaluate food safety risks from multiple dimensions and provide a scientific basis for formulating targeted food safety management measures.MethodsThe risk matrix method, along with the Borda method, is used to evaluate food safety risks. Based on the constructed food safety incident database, the risk matrix method is applied for static risk assessment from seven aspects: food categories, years, provinces, incident entities, causes, pollution sources, and links, and for dynamic risk assessment in different provinces.ResultsThe top five food categories in risk ranking are meat and meat products, fruits and vegetables, grain and grain products, other category, and aquatic products. Food safety risks show an increasing trend year by year, with the years 2006, 2012-2015 and 2019 having higher risk rankings. The top three provinces in risk ranking are Hunan, Guangdong, and Shaanxi. The top four entities in risk ranking are restaurants, enterprises, households, and canteens. The top four causes in risk ranking are human intention, human error, mismanagement, and production and social environment. The top four pollution sources in risk ranking include chemical pollution, other pollution, biological pollution, and food additives. The top three links in risk ranking include production and processing link, catering link, and agricultural production link. Across provinces, food safety risks in the years 2012-2015 are generally higher compared to other years, with the highest number of high-risk provinces in 2014.ImplicationThe findings of this study have significant implications for food safety management in China. By implementing food safety risk prevention and control countermeasures proposed, China can strengthen its food safety management framework, reduce the incidence of food safety incidents, and safeguard the health and well-being of its population.

Dynamics simulation model for risk probability assessment based on cognitive modeling in manned deep dive mission scenario

The manned submersible, as the main vehicle of transportation for human navigation in deep sea, has closed and narrow cabin, and with longer operating time, harder operating environment. However, once a manned deep submergence accident occurs, it is highly likely to lead to disastrous consequences. Therefore, it is urgent to develop a dynamic probabilistic risk assessment method which can quantitatively assess the cognitive behavior performance and human error of oceanauts. In this paper, a cognitive behavior simulation method for oceanaut based on knowledge and experience was proposed, which integrated richer performance shaping factors (PIF) into the IDAC model, and a cognitive environment simulation program of the IDAC model was developed through the system dynamics method, so as to analyze the dynamic human error probability of the oceanaut. In addition, the effectiveness and feasibility of the proposed model were evaluated based on real case data of manned deep submergence tasks, and the cognitive characteristics of PIFs were interpreted and classified. The research results are helpful for research institutions of manned deep submersible to accurately identify key risk points, improve the operational efficiency of the human-machine system in the cabin, and reduce the risks of the overall operating system.

MRI-guided dynamic risk assessment in cervical cancer based on tumor hypoxia at diagnosis and volume response at brachytherapy

Background and purposeImprovements in treatment outcome for patients with locally advanced cervical cancer (LACC) require a better classification of patients according to their risk of recurrence. We investigated whether an imaging-based approach, combining pretreatment hypoxia and tumor response during therapy, could improve risk classification. Material and methodsNinety-three LACC patients with T2-weigthed (T2W)-, dynamic contrast enhanced (DCE)- and diffusion weighted (DW)-magnetic resonance (MR) images acquired before treatment, and T2W- and, for 64 patients, DW-MR images, acquired at brachytherapy, were collected. Pretreatment hypoxic fraction (HFpre) was determined from DCE- and DW-MR images using the consumption and supply-based hypoxia (CSH)-imaging method. Volume regression at brachytherapy was assessed from T2W-MR images and combined with HFpre. In 17 patients with adequate DW-MR images at brachytherapy, the apparent diffusion coefficient (ADC), reflecting tumor cell density, was calculated. Change in ADC during therapy was combined with volume regression yielding functional regression as explorative response measure. Endpoint was disease free survival (DFS). ResultsHFpre was the strongest predictor of DFS, but a significant correlation with outcome was found also for volume regression. The combination of HFpre and volume regression showed a stronger association with DFS than HFpre alone. Patients with disease recurrence were selected to either the intermediate- or high-risk group with a 100 % accuracy. Functional regression showed a stronger correlation to HFpre than volume regression. ConclusionThe combination of pretreatment hypoxia and volume regression at brachytherapy improved patient risk classification. Integration of ADC with volume regression showed promise as a new tumor response parameter.

Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework

This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines.

Teaching manual handling through risk assessment empowers the nursing team to provide safe mobility-related care: A qualitative analysis

Introduction: Mobilisation of hospital patients is important for their functioning, and nurses play an important role in providing assistance. Manual handling training programs aim to help nurses to move patients safely, but most do not include dynamic risk assessment. We aimed to explore the experiences of members of the nursing team working in inpatient wards in a private hospital who participated in the manual handling training program Risk Assessment for moving Individuals SafEly (RAISE). Methods: Members of the nursing team who attended a 4-hour dynamic risk assessment training program led by physiotherapists participated in one of two focus groups guided by semi-structured questions 4 months post training. Focus groups were facilitated by an experienced qualitative researcher (physiotherapist). Results: Three themes emerged: (1) empowerment, (2) role and (3) implementation. The training session improved the nurses’ confidence and empowerment to employ a risk assessment model for mobility-related care, which was central to their role. Organisational factors, including cluttered environments, equipment and staffing challenges, were identified as the key barriers to implementation. Conclusion: Risk assessment manual handling training was perceived to improve confidence and empower the nursing team to provide safe mobility-related care. It is recommended that training sessions be relatively short; provide refresher training and consistent access to training within departments across the target health service; and take into consideration organisational factors that may affect implementation.

A novel dynamic risk assessment method for hazardous chemical warehouses based on improved SVM and mathematical methodologies

Effective dynamic risk assessment is crucial for identifying process hazards and preventing accidents. The rapid development of modern technology urgently requires the development of new dynamic risk assessment methods to address current societal needs, especially in situations where a large amount of real-time data is available. Consequently, this manuscript presents an improved support vector machine model (SVM), integrated mathematical modeling, to facilitate dynamic risk assessment for hazardous chemical warehouses (HCWs). Firstly, an indicator framework and a quantitative risk factor table are crafted. Then a mathematical model using knowledge graph and DEMATEL-variable weight theory (VWT) is established, which combines accident information with VWT to optimize the weight calculation and provides reliable prior knowledge for the improved SVM model. Ultimately, the improved SVM model is crafted by the establishment of an improved hybrid kernel of polynomial and radial basis function kernel, fine-tuning its hyperparameters with particle swarm optimization, and utilizing sensitivity analysis to simplify the model. The model is formulated to accommodate the intricate non-linear relationships within indicators and dynamic risk, thereby establishing dynamic risk assessment rules. Through a case study, the improved SVM model demonstrates higher precision in dynamic risk prediction compared to original models. The results substantiate that the model can provide innovative insight and methodology for the precise evaluation of dynamic risk in HCWs.

Agent-based risk modeling of domino effects in urban LNG stations

As the transfer hub between natural gas and users, urban LNG stations (ULSs) are the important link of the whole LNG industry chain. Due to special geographical location and various process equipment, potential risks in ULSs, especially domino effect accidents, usually lead to catastrophic consequences and social impacts. In this study, a dynamic risk assessment model for analyzing domino effects in urban LNG stations (DRADL) based on agent modelling and simulation is proposed. Tanks and tank cars as well as vaporizers and boilers in ULSs are modeled. A set of physical process and effect models are employed as the simulation process in this paper. Considering the lightweight nature and narrow flammability limit of LNG vapor cloud, a cyclic process is applied to judge the consequences of vapor cloud dispersion, which could avoid overestimating the results. A real urban LNG station is taken as a case study for modeling its potential domino effect risk. The results demonstrate the effectiveness and the advantages of the proposed ABMS approach for LNG dynamic risk assessment. The proposed DRADL model takes into account the combined effects of explosion and fire and LNG characteristics, which can provide guidance for risk management and rescue operation of domino effect accidents in urban LNG stations.

SYNTHESIZING AI'S IMPACT ON CYBERSECURITY IN TELECOMMUNICATIONS: A CONCEPTUAL FRAMEWORK

As the telecommunications sector increasingly relies on interconnected digital infrastructure, the proliferation of cyber threats poses significant challenges to security and operational integrity. This review presents a conceptual framework for understanding and harnessing the potential of artificial intelligence (AI) in fortifying cybersecurity within the telecommunications industry. The framework integrates the transformative capabilities of AI with the unique demands of cybersecurity in telecommunications, aiming to enhance threat detection, mitigation, and response strategies. It encompasses a multidimensional approach that encompasses both technical and organizational facets, recognizing the interconnectedness of technology, human factors, and regulatory environments. Firstly, the framework delves into the application of AI in bolstering proactive threat intelligence gathering and analysis. Through advanced algorithms and machine learning techniques, AI empowers telecom operators to identify anomalous patterns, predict potential vulnerabilities, and pre-emptively adapt defensive measures. Secondly, it explores AI-driven solutions for dynamic risk assessment and adaptive cybersecurity protocols. By leveraging real-time data analytics and automated decision-making, telecom networks can swiftly adapt to evolving threats and ensure continuous protection against intrusions or breaches. Furthermore, the framework emphasizes the role of AI in augmenting human capabilities through intelligent automation and cognitive assistance. By offloading routine tasks and providing context-aware insights, AI enables cybersecurity professionals to focus on strategic initiatives and complex threat scenarios. Lastly, the framework addresses the imperative of ethical considerations, accountability, and transparency in deploying AI for cybersecurity in telecommunications. It advocates for responsible AI governance frameworks that prioritize privacy, fairness, and bias mitigation while fostering collaboration across industry stakeholders. In summary, this conceptual framework provides a roadmap for harnessing AI's transformative potential to fortify cybersecurity resilience in telecommunications, thereby safeguarding critical infrastructure and ensuring the integrity of global communication networks.&#x0D; Keywords: AI, Cybersecurity, Telecommunication, Framework, Conceptual, Impact, Review.

Dynamic risk assessment of gas pipeline operation process by fusing visual and olfactory monitoring

With the rapid increase in urban gas consumption, the frequency of maintenance and repair of gas pipelines has escalated, leading to a rise in safety accidents during these processes. The traditional manual supervision model presents challenges such as inaccurate monitoring results, incomplete risk factor analysis, and a lack of quantitative risk assessment. This research focuses on developing a dynamic risk assessment technology for gas emergency repair operations by integrating the monitoring outcomes of artificial olfactory for gas leakage information and video object recognition for visual safety factor monitoring data. To quantitatively evaluate the risk of the operation process, a three-dimensional risk assessment model combining gas leakage with risk-correlated sensitivity was established as well as a separate three-dimensional risk assessment model integrating visual risk factors with predictable risk disposition. Furthermore, a visual risk quantification expression mode based on the risk matrix-radar map method was introduced. Additionally, a risk quantification model based on the fusion of visual and olfactory results was formulated. The verification results of simulation scenarios based on field data indicate that the visual-olfactory fusion risk assessment method can more accurately reflect the dynamic risk level of the operation process compared to simple visual safety factor monitoring. The outcomes of this research can contribute to the identification of safety status and early warning of risks related to personnel, equipment, and environmental factors in emergency repair operations. Moreover, these results can be extended to other operational scenarios, such as oil and gas production stations and long-distance pipeline operations.

A traffic dynamic operation risk assessment method using driving behaviors and traffic flow Data: An empirical analysis

Presently, traffic safety analysis is mainly based on accident-based indicators, but they are incidental and small probability events, making safety analysis based on accident-related data difficult to achieve a comprehensive application in network size, and to realize risk assessment before accidents occur. To this end, this study innovatively develops a metric named Traffic Dynamic Operation Risk (TDOR) based on aggressive driving behaviors (ADBs) and traffic flow data for traffic safety evaluation. And Non-Negative Matrix Factorization (NMF) is adopted to explore the quantitative risk under the coupling of multiple risk factors. The results show that: 1) segments with high TDOR are always concentrated in a certain area and in a spatial distribution of Gaussian, and the peaks of these distributions are almost the assesses of transportation hubs or complex intersections; 2) traffic volume and coefficient of speed variation are the significant risk factors at ordinary sections and branches, while traffic volume and speed difference between upstream are the largest contributions at intersections; 3) roads with more accidents are associated with a higher TDOR, but the relationship between the two is not completely linearly correlated. The experimental results show that it is reasonable and feasible to use ADBs and traffic flow parameters to quantify traffic safety risks. Compared with other matrix factorization methods, the importance of variables based on NMF is more interpretable and more consistent with the change pattern of risk variables. The method proposed in this paper can change the previous traffic risk assessment pattern based on accident-related indicators and provide a basis for pre-accident risk prevention.

A Dynamic Collision Risk Assessment Model for the Traffic Flow on Expressways in Urban Agglomerations in North China

Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and mode of dynamic risks on Chinese highways are still unclear. Therefore, this study adopts the Adaptive Neural Fuzzy Inference System (ANFIS) and the method of decision tree, combined with data from the Beijing section of the Beijing Harbin Expressway, to model the risk of accident-prone highways in urban agglomerations. To determine the optimal model, we evaluated the model’s bias at different time intervals. In addition, key factors affecting highway safety were analyzed, providing scientific support for the risk prevention of highways in urban agglomerations in China.

The effects of dynamic multi-hazard risk assessment on evacuation strategies in chemical accidents

With the agglomeration and complication of the chemical industry, the multi-hazard and dynamic characteristics have become new challenges for chemical safety management and loss prevention research. As an important topic of safety research in chemical industries, the evacuation strategy also needs to be improved according to the new characteristics of chemical accidents. Here we propose a method for evacuation strategy development in chemical accidents based on dynamic and multi-hazard risk assessment. This method adopts the cumulative individual risk as the evaluation indicator for the optimal evacuation strategy. This method can dynamically consider the multi-hazard characteristics and domino effects of chemical accidents during the evacuation. A multi-hazard chemical accident that happened in Xiangshui, China in 2019 is taken as a case study to show the effects of risk assessment results on evacuation strategy development. The case shows that a reasonable time period for shelter-in-place after the occurrence of the primary accident can reduce the cumulative individual risk in the evacuation process. It is expected that this paper can provide guidance and reference for loss prevention in chemical industries, and can also provide useful tools for the management of emergency response.