Browse Topic: Risk assessments
Aiming at the problem of insufficient modeling of spatio-temporal heterogeneity in road traffic accident prediction, a dual task machine learning framework integrating geographical environment, location attributes and time periodicity is proposed. The dataset used in this study was derived from traffic accident records of Nanchang during 2019–2023. Firstly, geographical identifiers are generated by rounding and aggregating latitude and longitude coordinates. At the same time, the location type is processed by a one-hot encoding, so as to carry out spatial clustering analysis of accident hotspots. Compared with the North-South pattern, the contribution of geographical features shows a strong East-West trend. The kernel density heatmap identified Zone A and zone B as dual core high-risk areas. Secondly, the sinusoidal/cosine function is used to encode the time feature circularly, which effectively captures the daily change of the accident. The quantitative analysis of random forest regression model showed that time characteristics accounted for 89.2% of the variance of accident frequency interpretation, significantly exceeding the contribution of geographical factors (10.2%) and location attributes (0.6%). After hyperparameter optimization, the accuracy of XGBoost classifier in predicting serious accidents is 75.97%, and the AUC value is 0.8412, which has strong robustness, and provides reliable support for dynamic risk assessment of traffic management system.
Noise phenomena in automobiles caused by the stick-slip effect are increasingly among the most frequent reasons for customer complaints and therefore represent a critical vehicle quality attribute. To proactively address such issues, stick-slip testing of contacting material pairs is commonly applied during development. However, the predictive capability of current stick-slip test methods remains limited, particularly when highly flexible materials and realistic, stochastic excitation conditions are involved. The flexibility of sealing systems often allows the actual relative motion at the contact interface to be accommodated through adhesion and elastic deformation, thereby delaying or even preventing sliding. To date, this effect has not been represented by any characteristic parameter in conventional stick-slip testing. Instead, existing evaluations focus exclusively on the analysis of occurring stick-slip oscillations. For the initiation of stick-slip phenomena, however, not only the mean displacement between two stick-slip oscillations during the sliding phase is relevant, but also the relative displacement required to initiate the first slip event of the sealing contact. With the algorithm developed in this work, which reproducibly determines the distance to first slip based on changes in the friction force slope, this methodological gap is now closed. The displacement to first slip depends on numerous influencing factors, including profile geometry, normal load, sliding velocity, excitation profile, and environmental conditions, and was previously inaccessible by both experimental and numerical approaches. In particular, the onset of slip in sealing contacts can now be determined under stochastic excitation of the friction pairing, thereby closely reflecting real operating conditions. As a result, the prevention of noise phenomena can be significantly strengthened at an early stage of vehicle development.
Air Traffic Management (ATM) must be familiar with the exact Aircraft Take-off Weights (ATOWs) of airplanes to make the most use of runways, maintain safety margins high, and keep utilization and resources in balance. This paper aims to present a dependable ATOW forecasting methodology that can assist the air transport industry in enhancing operational decision-making. This research used datasets acquired from the EUROCONTROL Performance Review Commission (PRC) 2024 Aircraft Take-Off Weight Estimation dataset featuring 527,000 flights over Europe containing aircraft details, air trips and flight conditions. Technique comprises structured data input, inspection of missing data, timestamp aggregation to identify demand cycles over time, and domain-specific feature engineering using distance_per_minute, block_minutes, taxiout_ratio, and a strong wake turbulence metric The two supervised learning models used were Linear Regression (LR) for understanding and XGBoost for performance prediction In comparison to LR's 4,409 kg MAE (mean absolute error), 7,061 kg RMSE (root mean square error), and 0.9825 R2 value, XGBoost significantly excelled with validation results showing an R2 value of 0.9992 and an RMSE of 1,514 kg In the absence of labelled test targets, cross-validation nevertheless showed a constant degree of generalizability The residual diagnostics showed that the model was reliable for practical execution with low-variance deviations that were unbiased An accurate ATOW estimate improves the demand-capacity balance and On-Time Performance (OTP) in ATM, which in turn affects the runway schedule, wake turbulence diversion, slot allocation, and fuel planning The results highlight the need to include ATOW predictions in both tactical and strategic planning to reduce delays, increase airspace usage, and promote sustainable aviation operation and possesses significant improvements will consist of weather and runway conditions, stochastic ambiguity computation, and drift monitoring to keep up with ever-changing operating variables while maintaining accurate forecasts.
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence modeling, model training, and interpretability analysis. The study demonstrates how this pipeline uncovers hidden relationships among operational errors, environmental influences, maintenance actions, design considerations, and crew behavior. The findings show that the integrated approach improves the accuracy and stability of risk prediction and highlights specific human factors patterns that consistently contribute to elevated risk levels. These insights support targeted mitigation strategies, inform design improvements, and help prioritize safety interventions. The work concludes that a combined human factors and predictive modeling framework enhances the ability of organizations to identify vulnerabilities earlier, allocate resources more effectively, and strengthen system resilience. This approach is adaptable to diverse aviation contexts and offers a practical path for transforming human factors data into actionable safety intelligence.
The automotive industry is evolving from a reactive, independently self-determined approach to cybersecurity, complicated by a complex supply chain. Over time, this has resulted in a fragmented industry comprised of any number of proprietary solutions verses a standardized, regulated paradigm to facilitate a platform-oriented approach. This document, an update on collaborative work from the SAE Vehicle Electrical Hardware Security Task Force (TEVEES18B) and GlobalPlatform Automotive Task Force, outlines this transition strategy. An extensible number of additional examples of use cases of Global Platform Technologies are explored in this document.
In this Q&A, Audrey Turley, director of lab operations – biosafety at Nelson Laboratories, spoke with Medical Design Briefs about the critical importance of monitoring and managing material changes in medical devices. Even seemingly minor shifts — such as switching suppliers or altering processing steps — can introduce unknown additives or variations that impact biocompatibility and, ultimately, patient safety. Turley discusses how manufacturers can effectively document and justify changes, maintain regulatory compliance, and strengthen supplier relationships to ensure ongoing device safety. She also shares insights into trends shaping post-pandemic supply-chain strategies and the growing emphasis on proactive risk assessment and communication across the product lifecycle.
Thermal runaway in lithium-ion batteries represents a critical safety challenge, particularly in high-voltage battery systems used in electric vehicles and stationary energy storage. A comprehensive understanding of the multi-scale processes that initiate and propagate thermal runaway is essential for the development of effective safety measures and design strategies. This study provides a structured theoretical overview of the thermal runaway phenomenon across four hierarchical levels: electrode, single cell, module, and high-voltage battery system. At the electrode level, thermal runaway initiation is linked to electrochemical and chemical degradation mechanisms such as solid electrolyte interphase decomposition, separator breakdown, and internal short circuits. These processes lead to highly exothermic reactions that, at the cell scale, can result in rapid temperature increases, gas generation, and overpressure. On the module and system levels, thermal runaway can propagate through thermal and mechanical coupling between neighboring cells, influenced by layout, cooling design, and enclosure properties. The core contribution of this study lies in a detailed modeling approach that focuses exclusively on the chemical and thermal decomposition reactions occurring at the electrode scale. These reactions form the foundational layer of a broader simulation framework to be developed in subsequent work. A semi-empirical model is proposed, capturing key phenomena such as electrolyte decomposition, solid electrolyte interphase breakdown, and active material reactions. The model integrates thermal conduction and heat generation from exothermic reactions to characterize the local temperature evolution during the early stages of thermal runaway. By isolating and accurately representing these fundamental decomposition pathways, this modeling approach provides a critical building block for future extensions toward higher-scale thermal runaway simulations. It offers valuable insights into the onset mechanisms of thermal instability, supporting battery design optimization and risk assessment from the ground up.
In-Use emission compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding emission compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for emission compliance risk assessment using Fleet Intelligence Data, which includes high-frequency telematics data from over 500K machines, reporting more than 1000 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for emission compliance assessment. The resulting reports provide actionable insights on NOx sensor activity, engine warmup operations, high-risk drive cycles, and load profiles across different operation regimes. This approach significantly reduces the reliance on costly and labor-intensive physical testing with Portable Emissions Measurement Systems (PEMS) by integrating advanced analytical methods into the workflow. By leveraging high-frequency telematics data, this method enables engineers to identify failed machines in the field more efficiently. It also provides valuable insights and reasoning behind these failures, facilitating quicker and more informed decision-making. This not only enhances emission compliance monitoring but also optimizes resource allocation and reduces overall regulatory risks. In summary, the developed methods enable effective emission compliance monitoring, reduce regulatory risks, and help optimize calibration strategies by understanding customer usage patterns. These methods are scalable for various emission regulations.
This study introduces an innovative intelligent tire system capable of estimating the risk of total hydroplaning based on water pressure measurements within the tread grooves. Dynamic hydroplaning represents an important safety concern influenced by water depth, tread design, and vehicle longitudinal speed. Existing intelligent tire systems primarily assess hydroplaning risk using the water wedge effect, which occurs predominantly in deep water conditions. However, in shallow water, which is far more prevalent in real-world scenarios, the water wedge effect is absent at higher longitudinal speeds, which could make existing systems unable to reliably assess the total hydroplaning risk. Groove flow represents a key factor in hydroplaning dynamics, and it is governed by two mechanisms: water interception rate and water wedge pressure. In both the shallow water and deep water cases, the groove water flow will increase as a result of increasing the longitudinal speed of the vehicle for a constant water depth. Therefore, the water pressure in the tread grooves will also increase as the longitudinal speed of the vehicle approaches the critical hydroplaning speed. Unlike conventional systems, the proposed intelligent tire design utilizes the amplitude and shape of the measured pressure signals from the tread grooves for estimating the total hydroplaning risk in both shallow and deep water conditions. Experimental results indicate that peak groove water pressure increases with the risk of total hydroplaning. Furthermore, the overall shape of the pressure signal will also be influenced by the total hydroplaning risk. By addressing the limitations of current intelligent tire systems, the proposed intelligent tire design offers a robust solution for real-time total hydroplaning risk estimation across diverse driving conditions.
Aircraft Certification is a mature and complex bureaucracy that has successfully ensured a very high degree of safety of aircraft design, construction, operation and maintenance. Outside of a very few doing the work, there is a general lack of knowledge of certification details. For novel technologies such as electric power, and innovative configurations such as multi-rotors, the rules are far less mature and still emerging and so also poorly understood. Within the Advanced Air Mobility (AAM) initiative, many new aircraft developments are underway using novel configurations, and the public announcements of regulatory progress toward FAA or EASA Type Certification capitalize on this ignorance by being vague or even misleading. Honeywell conceived the Regulatory Readiness Level (RRL) indicator as an objective measure of certification status to serve the AAM industry and ecosystem, with applicability across aviation. The released RRL Version 1 now enables credible, objective assessment of new aircraft progress toward FAA Type Certification, and Operational Approval for Part 135 operations, to allow consistent apples-to-apples comparisons with other aircraft in development. An emerging complementary version of the rubric for EASA Type Certification is ready for publication to enable RRL determination against the European Union criteria. Future releases will consider other Nation's regulatory authorities, supplemental types certifications (STCs), and risk-based airworthiness assessments such as the Specific Operations Risk Assessments (SORA).
Airworthiness certification of aircraft requires an Airworthiness Security Process (AWSP) to ensure safe operation under potential unauthorized interactions, particularly in the context of growing cyber threats. Regulatory authorities mandate the consideration of Intentional Unauthorized Electronic Interactions (IUEI) in the development of aircraft, airborne software, and equipment. As the industry increasingly adopts Model-Based Systems Engineering (MBSE) to accelerate development, we aim to enhance this effort by focusing on security scope definitions – a critical step within the AWSP for security risk assessment that establishes the boundaries and extent of security measures. However, our findings indicate that, despite the increasing use of model-based tools in development, these security scope definitions often remain either document-based or, when modeled, are presented at overly abstract levels, both of which limit their utility. Furthermore, we found that these definitions frequently lack alignment with airworthiness security regulations. To address these two distinct gaps, this paper presents a model-based approach for detailed security scope definitions using the Systems Modelling Language (SysML). Our approach aligns with airworthiness security regulations ED-202A / DO-326A and ED-203A / DO-356A and incorporates a SysML profile based on the CORAS language for accurate modeling of security scopes. This facilitates a model-based security risk assessment by creating unambiguously system models that represent assets through model elements, document entry points to the assets and determine their environment. This SysML-based approach supports certification related activities by ensuring that security scope definitions are comprehensive and aligned with airworthiness regulations, directly addressing the identified gaps. The approach's applicability and effectiveness are demonstrated through an illustrative example in the domain of aircraft cabin system development. Moreover, the approach provides valuable inputs that assist operators in deriving guidance for the safe operation and maintenance of the aircraft, complementing existing methods and practices.
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