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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Evaluating the Safety of Port-to-Ship Liquefied Hydrogen Bunkering through Fault Tree Analysis and Fuzzy Bayesian Networks</title>
      <link>https://trid.trb.org/View/2698309</link>
      <description><![CDATA[As marine fuels transition toward zero carbon dioxide emissions, hydrogen emerges as a promising alternative due to its near-zero emissions and high energy density. However, hydrogen’s high flammability presents a significant risk of fire-related accidents onboard ships. Among the various operations involving hydrogen in the maritime sector, bunkering is one of the most critical. This process carries risks such as leakage, pressure failures, and human errors, which can lead to severe consequences. This study analyzes the safety of the liquefied hydrogen bunkering process. Fault Tree Analysis (FTA) was used to identify failures within the bunkering system, employing a top-down approach that traces failures from the top event to the basic events. Fuzzy Set Theory was applied to quantify risk by aggregating linguistic variables from five experts to estimate the probabilities of basic events. Bayesian Networks (BN) were then used to establish causal relationships between factors and identify the minimum cut sets leading to the top event of bunkering failure. Rate of Variation (RoV) analysis of the posterior probabilities of basic events revealed that human error, particularly due to task execution problems and human-machine interface issues, is the most significant factor influencing bunkering failures. The findings highlight critical areas that require attention to enhance the safety of liquefied hydrogen bunkering operations in the maritime sector, especially for relevant stakeholders.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698309</guid>
    </item>
    <item>
      <title>Study on a multi-factor lane-changing risk resilience assessment model based on genetic algorithm and fault tree analysis</title>
      <link>https://trid.trb.org/View/2670127</link>
      <description><![CDATA[Current lane-change risk assessment models often lack dynamic adaptation to adverse weather and validation against real-world outcomes. To bridge this gap, this study re-frames the problem through a resilience engineering lens, defining risk resilience as the lane-changing system’s capacity to absorb weather disturbances and maintain safety through adaptation. To operationalize this concept, the authors introduce two complementary metrics: the Risk Exposure Level (REL) and the Risk Severity Level (RSL). The authors propose a weather-aware, resilience-oriented assessment framework that integrates a Genetic Algorithm (GA)-calibrated Stopping Sight Distance (SSD) model with Fault Tree Analysis (FTA). Using the CitySim naturalistic driving dataset, a dual-threshold identification algorithm was applied to extract 310 lane-change events (218 sunny, 92 rainy). Key influencing factors, including weather, surrounding vehicle distribution, lane-change direction, and location, were identified through statistical testing. The GA was employed to optimize critical braking parameters (deceleration, reaction time) in the SSD/SDI model, enabling self-adaptive risk thresholds under different weather conditions. REL and RSL quantify the probability (exposure) and severity (consequence) of conflicts from multiple vehicle groups, which are systematically integrated via FTA to assess overall system robustness. Model calibration and testing using trajectory data showed a 42.38% improvement in fitness over the baseline model. A PyQt5-based visualization platform was developed to support practical application. The results confirm that the model effectively captures real-time lane-changing risk, providing a reliable tool for proactive safety management and resilience-oriented decision support in intelligent transportation systems.]]></description>
      <pubDate>Wed, 18 Mar 2026 08:59:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670127</guid>
    </item>
    <item>
      <title>Research on the toughness risk of jack-up platform legs</title>
      <link>https://trid.trb.org/View/2644086</link>
      <description><![CDATA[As global energy demand continues to rise, the development of ocean resources is receiving increasing attention, especially the exploitation of deepwater and marginal oil fields. Jack-up platforms play a pivotal role in the development of offshore marginal oil fields due to their flexible deployment and low cost. This paper focuses on the leg structure of self-elevating platforms, employing Dynamic Bayesian Networks to study the resilience-related risks of the platform legs. Firstly, the influencing factors of the jack-up platform's legs are analyzed to identify their primary contributors. Then, a power-law exponential model is employed to model the corrosion degradation of metal legs. For their specific environmental conditions, the Airy model and Morison model are considered to model the fatigue degradation process of the legs. Secondly, a comprehensive recovery capability model is taken into account to couple the recovery performance of multiple degradation aspects, and the MTTR (Mean Time To Repair) and MTTD (Mean Time To Detection) models are introduced to calculate the recovery capability under different influencing factors. Finally, corresponding fault tree models are established by considering extreme operating conditions such as wind, wave, and current loads, seawater corrosion, as well as collisions with vessels and ice that the offshore platform legs may endure. These models are then mapped onto a dynamic Bayesian network. The results indicate that this method can evaluate the degradation performance and recovery capability of the leg structure under different operational scenarios, and is also capable of analyzing its resilience performance.]]></description>
      <pubDate>Tue, 17 Mar 2026 09:48:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2644086</guid>
    </item>
    <item>
      <title>From fault trees to Bayesian networks: A hybrid model for fishing vessel compliance risk assessment</title>
      <link>https://trid.trb.org/View/2631247</link>
      <description><![CDATA[This study conducts a comprehensive compliance risk assessment for China’s ocean fishing vessels (COFV) with the Cape Town Agreement (CTA). The assessment starts with risk identification via Fault Tree Analysis (FTA), then converts the inspection tree into a Bayesian Network (BN). It subsequently integrates the sigmoid function into the Fuzzy Bayesian Network that addresses the retention status of fishing vessels and the probability of non-compliance for vessels at each node. Sensitivity analysis is subsequently conducted to identify critical codes. Utilizing this method, the COFV’s detention percentage in contracting state ports is calculated to be 0.26. The analysis results also reveal key risk factors for retention, such as radio personnel configuration, vessel stability, fire prevention in ship compartments, mechanical equipment safety, radio equipment configuration, and the implementation of emergency response plans. The new method forecasts the compliance of COFV to international agreements, and offers useful citation for strengthening China’s international compliance abilities in ocean fisheries, overseeing ocean fishing operations, and guaranteeing the secure production of fisheries.]]></description>
      <pubDate>Wed, 18 Feb 2026 13:22:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2631247</guid>
    </item>
    <item>
      <title>Architectural Design and Analysis of Electro-Mechanical-Brake System in View of Functional Safety Concept</title>
      <link>https://trid.trb.org/View/2617962</link>
      <description><![CDATA[With the development of automotive intelligence, drive-by-wire system has attracted extensive attention from academia and industry. However, the architectural design of electro-mechanical-brake (EMB) systems has not been emphasized enough, especially in the context of functional safety concept. In view of this, based on ISO 26262, this paper extends the system boundary of functional safety for the first time, and combines EMB system with other systems of the vehicle to build a 4-way redundant EMB system architecture. Furthermore, detailed qualitative and quantitative analysis results are given by using state transition location diagram and quantitative fault tree analysis (FTA) respectively, and the braking failure rate is Q ₀ ≈ 2.0003×10 ⁻¹⁰ < 10 ⁻⁸ meets the failure rate requirements of ASIL-D, which prove the security and reliability of the proposed architecture. This paper provides guidance and reference for the design and analysis of EMB system for subsequent researchers, and guarantees the safe development of autonomous vehicles from the safety and reliability of the system.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:53:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617962</guid>
    </item>
    <item>
      <title>Probabilistic fault tree analysis and dynamic redundancy optimization for next-generation avionic flight control systems</title>
      <link>https://trid.trb.org/View/2618403</link>
      <description><![CDATA[The increasing complexity of next-generation avionic systems necessitates advanced reliability frameworks to ensure fault tolerance while meeting stringent constraints on weight, cost, and certification. This paper presents a novel probabilistic modeling approach that integrates Dynamic Fault Tree Analysis (DFTA), Bayesian Belief Networks, and semi-Markov processes to assess failure probabilities in safety-critical flight control architectures, such as Fly-by-Wire (FBW) systems and Integrated Modular Avionics (IMA). To complement this, a Dynamic Redundancy Optimization (DRO) framework using a Multi-Objective Genetic Algorithm (MOGA) is introduced to optimally allocate redundancy under realistic conditions, including common-cause failures and intermittent faults. The methodology is validated on a triplex-redundant Flight Control Computer (FCC), achieving a 41.8 % improvement in mean time between failures (MTBF) and a 36.5 % reduction in catastrophic event probability compared to static baseline models. Importantly, the framework conforms to DO-178C and ARP4761A standards, ensuring traceability and certification readiness. The results reveal a Pareto frontier representing optimal trade-offs between reliability enhancements and system resource overheads, providing critical guidance for the design of next-generation avionic systems and regulatory assessment.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618403</guid>
    </item>
    <item>
      <title>Risk management of ship independent navigation in ice-covered Arctic waters: an early warning model</title>
      <link>https://trid.trb.org/View/2623410</link>
      <description><![CDATA[With the retreat of Arctic sea ice, independent ship navigation in ice-covered waters is increasing, especially in areas with lower ice concentration and thinner ice, reducing reliance on icebreaker escort. However, this trend raises risks of ship-ice (or iceberg) collisions and ship besetting in ice. This study proposes a risk early warning model for such scenarios by integrating multi-state fault tree analysis (MS-FTA) and Bayesian network (BN). First, risk influencing factors (RIFs) related to environmental, technical, human, and organizational aspects are identified from maritime accident reports and set as model nodes. Then, MS-FTA is used to construct model structures for the two accident scenarios, with if-then rules formulated as early warning conditions. These rules are embedded into the BN framework to compute accident probabilities and identify critical RIFs. Based on the results, targeted risk control options are recommended. The proposed model provides a systematic tool for assessing and managing the risk of ship independent navigation in Arctic and other ice-covered waters.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2623410</guid>
    </item>
    <item>
      <title>A Comprehensive Study on Integration of Safety Analysis with Technical Safety Concept to Enhance the Product Safety</title>
      <link>https://trid.trb.org/View/2623984</link>
      <description><![CDATA[This manuscript presents a comprehensive study on the integration of Safety Analyses with Technical Safety Requirements (TSRs) to enhance functional safety in complex automotive systems and off-highway applications. It emphasizes the importance of systematically identifying potential hazards and translating them into precise, actionable TSRs that guide the design, implementation, and validation of safety-critical systems. By aligning safety analysis techniques—such as Fault Tree Analysis (FTA) and Failure Mode and Effects Analysis (FMEA)—with ISO 26262, the study demonstrates how safety goals can be effectively transformed into technical specifications that ensure robust system behavior under fault conditions. Part 1 outlines the use of Failure Modes and Effects Analysis (FMEA) to identify potential failure modes and single point faults across system, subsystems, and components. FMEA assesses the severity, likelihood, and detectability of these failures, guiding the development of relevant test cases. The risks uncovered through FMEA serve as a basis for updating the TSRs by implementing safety measures such as redundancy, fail-safe mechanisms, and diagnostic systems to mitigate identified hazards. Part 2 explores the role of Fault Tree Analysis (FTA) in identifying multiple-point failures in a system by performing a deductive (top down) analysis. The insights from FTA further refine TSRs, ensuring that the safety requirements address both simple and complex fault scenarios in the system. Part 3 introduces Dependent Failure Analysis (DFA) to detect interdependent failures and failure propagation paths, focusing on risks from common cause and common point failures. The results of DFA assist in developing more resilient systems by adding redundant paths to prevent or mitigate such dependent failures. Part 4 focuses on safety analysis in production phase, ensuring that Production related Safety requirements are identified in TSC and met during the manufacturing phase, emphasizing traceability, compliance, and verification. Finally, Part 5 presents a case study demonstrating how the integration of Safety Analyses with the ISO 26262 standard results in well-defined TSRs that support system design, testing, and validation, thereby ensuring the product’s safety.]]></description>
      <pubDate>Tue, 30 Dec 2025 08:57:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2623984</guid>
    </item>
    <item>
      <title>The SOTIF Meta-Algorithm: Quantiative Analyses of the Safety of AI</title>
      <link>https://trid.trb.org/View/2604444</link>
      <description><![CDATA[This paper presents updates to a "meta-algorithm: for achieving safer AI driven systems by integrating systems theoretic process analysis, quantitative fault tree analysis, structured generation of safety metrics, and statistical hypothesis testing of metrics between simulation and reality. This paper presents updates to the meta-algorithm after its application in use cases involving commercial autonomous vehicle deployment.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:24:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604444</guid>
    </item>
    <item>
      <title>Reliability Model of Autonomous Transport with Life Support Systems Based on Closed Biotechnological Complexes</title>
      <link>https://trid.trb.org/View/2407294</link>
      <description><![CDATA[Among autonomous transport systems operating in an isolated environment, systems with a crew constitute a special group. Such systems include space and underwater stations, arctic and Antarctic stations, and other objects. In these facilities, one of the central places is occupied by life support systems for crew members, the efficiency and reliability of which largely determine the duration of missions of autonomous vehicles.At present, all life support systems are considered as independent functional circuits. At the same time, the main emphasis in the process of their research is placed on the features of their technical feasibility. In the process of long-term operation, the reliability of such systems is critical for the life of the crew. Basic reliability models should be synthesized early in the development.The purpose of this article is to study the structure of an integrated life support system from the standpoint of the reliability of its functioning.The article describes an approach to creating an integral life support ecosystem for autonomous transport systems of long-term operation. The paper presents an integrated architecture of biotechnological life support systems for autonomous vehicles, broken down into five closed biotechnological cycles (loops): oxygen loop, methane loop, carbon dioxide loop, food loop and water loop.The reliability models for these closed biotechnological loops based on the Fault Tree Analysis are developed in the paper.]]></description>
      <pubDate>Thu, 23 Oct 2025 09:22:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407294</guid>
    </item>
    <item>
      <title>Incorporation of Fault Tree Analysis and Evidential Reasoning in Navigational Risk Assessment of Offshore Wind Farms</title>
      <link>https://trid.trb.org/View/2526747</link>
      <description><![CDATA[Quantifying the navigation risk in waters of offshore wind farms (OWFs) using traditional probabilistic methods is difficult due to various impact factors, intricate interactions, and limited accident records. To address this issue, this paper develops a novel risk assessment framework that incorporates fault tree analysis (FTA) with evidential reasoning (ER) approaches. The proposed framework provides a clear and logical structure for evaluating navigational risks in OWF waters. The FTA is used to identify and establish the risk factors that affect navigational safety in OWF waters and their interrelationships. Rules assigning factor weights based on the logical gates in the fault tree (FT) are proposed, enabling the calculation of risk using ER. This research tests the applicability of the proposed framework by using the cases in the OWF waters of the Haitan Strait in Fujian Province. The results indicate a moderate navigation risk level for ships sailing in OWFs water. Additionally, this research can identify significant risk factors influencing the navigation safety in OWFs waters. The proposed framework can also provide valuable insights for port authorities for evaluating navigation risk and improving vessel safety supervision in complex maritime environments.]]></description>
      <pubDate>Fri, 30 May 2025 15:53:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2526747</guid>
    </item>
    <item>
      <title>Fire risk assessment of electric ships on inland waterway based on GT-FFTA : A case study of China</title>
      <link>https://trid.trb.org/View/2544685</link>
      <description><![CDATA[To address the global challenge of greenhouse gas emission reduction, electric ships (ES) are emerging as a promising alternative in shipping industry. However, fire incidents triggered by lithium-ion batteries employed in ES could lead to severe losses while the underlying causes remain unclear. Currently, there is a lack of comprehensive risk assessment for ES and it is challenging to conduct risk analysis for ES based on traditional accident causality frameworks. To address these issues, This paper proposes a novel risk assessment model: grounded theory based fuzzy fault tree analysis (GT-FFTA). Firstly, fire risk factors of ES were identified to form a comprehensive classification system based on grounded theory. Secondly, probabilistic risk assessment of ES was conducted using proposed model. Furthermore, Monte Carlo simulations are employed to mitigate the limitations of single assessment. Finally, the F-V measure is applied to identify the most critical causes. The findings indicate that battery quality and state monitoring equipment failures are the primary contributors to fire incidents, followed by inadequate maintenance and lack of professional training. Additionally, emergency plans require special attention. This study provides valuable insights for shipping enterprises regarding supplier selection, crew training, and emergency planning for ES on inland waterway.]]></description>
      <pubDate>Tue, 27 May 2025 09:33:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2544685</guid>
    </item>
    <item>
      <title>Safety risk assessment for connected and automated vehicles: Integrating FTA and CM-improved AHP</title>
      <link>https://trid.trb.org/View/2495196</link>
      <description><![CDATA[To reduce safety accidents from functional failures in connected and automated vehicles (CAVs), risk assessment and prevention are essential. However, traditional hazard analysis and risk assessment (HARA) methods suffer from limitations: insufficient quantitative assessment and inadequate consideration of ambiguity and uncertainty. To this end, the authors propose a quantitative risk assessment method for CAVs based on fault tree analysis (FTA) and cloud model (CM)-improved analytic hierarchy process (AHP). First, they use the golden section method and CM to refine the automotive safety integrity level (ASIL), representing ambiguity in level boundaries. Next, they incorporate potential functional failure paths and construct basic events for the FTA based on the failure modes of vehicle components. Meanwhile, the CM-improved AHP is applied to assess risks for each basic event, reducing uncertainty from subjective data. Finally, the authors combine the technique for order preference by similarity to an ideal solution (TOPSIS) with the conversion function to provide quantitative probabilities for the top event and perform a sensitivity analysis of basic events. A case study on a real open-source test vehicle shows that the proposed method quantifies the probability of automatic emergency braking (AEB) system failure and ranks the risk for each basic event. Compared to existing methods, it has significant advantages in the comprehensiveness and objectivity of risk assessment, providing more accurate information for risk prevention.]]></description>
      <pubDate>Tue, 25 Mar 2025 16:57:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2495196</guid>
    </item>
    <item>
      <title>Assessment of human contribution to cargo ship accidents using Fault Tree Analysis and Bayesian Network Analysis</title>
      <link>https://trid.trb.org/View/2509850</link>
      <description><![CDATA[Maritime accidents are significant contributors to human casualties, environmental damage, and economic losses. This study examines the role of human factors in very serious maritime casualties involving cargo ships, employing Fault Tree Analysis (FTA) and Bayesian Network (BN) methodologies. Using data from the European Maritime Casualty Information Platform (EMCIP) for incidents between 2010 and 2020, the analysis focuses on 60 maritime accidents linked to human actions. FTA displays causes related to human error that can lead to accident, while BN models the relationships and dependencies among Risk Influencing Factors (RIFs). The combined approach enables a comprehensive evaluation of system risks, highlighting key contributors such as shipboard operations and shore management practices. The study also explores minimal cut sets and mutual information to assess the influence of environmental and operational factors on accident probabilities. Results indicate that factors like crew resource management and workplace conditions significantly affect the likelihood of casualties. Scenario analyses further demonstrate the dynamic interactions between RIFs and their impact on maritime safety. This dual methodology provides actionable insights for improving risk management strategies and reducing human error in maritime operations, offering a robust framework for enhancing safety in the shipping industry.]]></description>
      <pubDate>Wed, 19 Feb 2025 17:11:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509850</guid>
    </item>
    <item>
      <title>Fuzzy Fault Tree Analysis of Railway Traffic Safety</title>
      <link>https://trid.trb.org/View/2264005</link>
      <description><![CDATA[The safety analysis of railway traffic system is presented in this paper. Fuzzy fault tree technique has been adopted in safety analysis. The main traffic accidents—top events of fault trees—have been analyzed down to the level of human errors and hardware failures. In the fuzzy fault-tree model, the possibility of failure, i.e. a fuzzy set defined in probability space, is proposed to replace the probability of failures. The maximum possibility of railway traffic system failure is determined from the possibility of failure of each unit within the system according to the extension principle. An example is given to illustrate the method.]]></description>
      <pubDate>Sat, 25 Jan 2025 12:17:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2264005</guid>
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