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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Data-Driven Risk Analysis and Management Framework for Rail Hazmat Transportation in Canada: Machine Learning Approach</title>
      <link>https://trid.trb.org/View/2652885</link>
      <description><![CDATA[Rail transportation of hazardous materials (hazmat) is essential to Canada’s economy but carries significant safety and environmental risks. This study develops a data-driven predictive risk assessment framework for hazmat release following railway accidents, integrating multi-year incident records with operational, environmental, and geographic variables from multiple public sources. Supervised machine learning models—logistic regression, decision trees, and neural networks—are applied to classify hazmat release outcomes following rail incidents. Key predictors include track type, hazmat class, weather conditions, train configuration, and operational density. The best-performing models demonstrated competitive predictive performance, with metrics such as AUC-ROC, F1-score, and balanced accuracy indicating consistent behavior despite substantial class imbalance, while also offering interpretable insights into key risk factors. We also developed the Rail HAZMAT Release Predictor, a web-based tool that applies our models to assess hazmat release risks in rail incidents. Findings inform targeted mitigation strategies aligned with Public Safety Canada’s emergency management framework, including limiting hazmat car counts, implementing predictive maintenance, and tailoring emergency protocols to regional risk profiles. By combining multi-source data integration with advanced modeling, this research advances proactive, evidence-based decision-making for safer hazmat rail operations.]]></description>
      <pubDate>Mon, 06 Apr 2026 08:50:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652885</guid>
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    <item>
      <title>Quantitative risk analysis for transportation of dangerous goods in Turkiye</title>
      <link>https://trid.trb.org/View/2647635</link>
      <description><![CDATA[This study presents a comprehensive Quantitative Risk Analysis (QRA) framework for assessing road transport accidents involving dangerous goods in Turkiye. The proposed methodology integrates national accident statistics, scenario-based event tree modeling, and ALOHA software/ correlations for consequence and impact assessment. Three representative routes in İzmir were selected as pilot areas to evaluate accident frequencies, physical impact zones, and associated individual and societal risks. Results indicate that LNG and LPG transport pose the highest risk levels, with scenario frequencies exceeding the regulatory threshold (1 × 10⁻⁴/year). Population exposure analysis revealed that social risk varies significantly with local demographic density. Sensitivity analyses confirmed that both the frequency of the initiating event and population distribution are critical determinants of total risk. The study presents a data-driven, nationally adapted QRA model aligned with Turkish transport infrastructure and regulations, providing a robust decision-support tool for improving road safety and emergency preparedness in dangerous goods logistics.]]></description>
      <pubDate>Fri, 20 Mar 2026 17:00:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647635</guid>
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    <item>
      <title>Integration of MIMAH and Fuzzy Bayesian Networks for risk analysis in chemical tanker loading operations</title>
      <link>https://trid.trb.org/View/2616224</link>
      <description><![CDATA[This study provides a systematic risk assessment approach for chemical tanker loading operations, focusing on a high-risk scenario identified through operational data from a model vessel. To address the complexities of chemical transportation, a hybrid methodology combining the Methodology for the Identification of Major Accident Hazards (MIMAH) and Fuzzy Bayesian Network (FBN) analysis was developed. MIMAH's structured framework systematically identifies critical events using a Bow-Tie (BT) diagram, integrating Fault Tree (FT) and Event Tree (ET) providing a thorough breakdown of potential accident pathways. This BT structure was converted into a Bayesian Network (BN) to improve probability estimations by incorporating conditional dependencies and expert-driven fuzzy logic, particularly where historical data was limited. The study further employed a dual-method sensitivity analysis, integrating Fussell-Vesely (FV) importance measures and Improvement Index (II), to identify critical and improvement-prone basic events (BEs). Key findings highlight the dominance of human error in high-risk events, particularly manifold connection failures and incorrect valve operations, alongside mechanical vulnerabilities with significant improvement potential. This hybrid approach extends ARAMIS principles to maritime contexts, integrating reliability-based and fuzzy-based probability estimation methods in chemical tanker operations, and provides a detailed and adaptable framework that enhances safety and resilience in hazardous maritime transport.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616224</guid>
    </item>
    <item>
      <title>Topological analysis of risks in hazardous materials transportation systems using fitness landscape theory and association rules mining</title>
      <link>https://trid.trb.org/View/2569931</link>
      <description><![CDATA[Determining the failure modes of hazardous materials transportation systems, considering the coupled effects of risk factors, is crucial for ensuring transportation safety. This study proposes a coupled topological analysis method for hazardous materials road transport risks, based on association rule mining and fitness landscape theory. This method can reflect the correlations and evolutionary patterns of risk factors, thereby providing a basis for formulating risk mitigation strategies. Firstly, text mining techniques are employed to identify critical risk factors and gather a structured dataset comprising 165 entries. Secondly, association rule algorithms are used to uncover potential relationships among sub-factors, employing the Apriori algorithm with set thresholds to extract strong association rules, which are then mapped into a landscape model depicting the coupled evolution of system risk factors. Finally, by employing a defined fitness function, typical system failure paths are further analyzed topologically. The results indicate that directly mining failure paths from sub-risk factors can elucidate more detailed system failure mechanisms. Coupled failure modes involving human and environmental factors warrant particular attention. Vehicle factors often lead to accidents without further evolution, necessitating the establishment of corresponding inspection mechanisms.]]></description>
      <pubDate>Fri, 29 Aug 2025 10:05:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569931</guid>
    </item>
    <item>
      <title>Personalized prediction of unsafe driving behaviors for drivers of dangerous goods transportation trucks based on an attribute graph interaction model</title>
      <link>https://trid.trb.org/View/2587215</link>
      <description><![CDATA[With the growing deployment of dangerous goods transportation trucks (DGTTs), ensuring driving safety has become increasingly important. Given the high disaster potential and hazardous nature of DGTTs, source-level risk control is essential. To support proactive risk management at the source, the authors propose a method for predicting unsafe driving behaviors before trips. This method leverages trajectory data and intelligent video collected from legally mandated on-board terminals. The authors adopt a recommender system (RS) approach for its capacity to capture intricate attribute interactions and provide personalized predictions. Drawing an analogy between RS components and the scenario, drivers correspond to users and alarms to items, with their respective attributes forming two sides of the model. The authors introduce a Bilateral Graph Interaction-based Collaborative Filtering (BGICF) model, enhanced with Adversarial Graph Dropout (AdvDrop). BGICF models both internal coupling and external interaction between attributes. Furthermore, to address attribute popularity bias and improve interpretability in BGICF, the authors integrate AdvDrop, which constructs bias-mitigating and bias-aware subgraphs using a bias measurement function and optimizes them through adversarial learning. The authors collected natural driving data from an active safety platform from 23 DGTT companies in Beijing, China, covering over 58 million trajectory points and 211,157 alarm records. Experimental results showed that BGICF-AdvDrop achieves macro precision, recall, F1-score, and accuracy of 0.8202, 0.8114, 0.8101, and 0.8416, respectively, outperforming other models while providing better interpretability.]]></description>
      <pubDate>Tue, 19 Aug 2025 15:29:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587215</guid>
    </item>
    <item>
      <title>Vulnerability assessment in inland waterway transportation network for hazardous materials amid demand: A case of the Pearl River Delta</title>
      <link>https://trid.trb.org/View/2557114</link>
      <description><![CDATA[The transportation of hazardous materials (hazmat) not only carries high risks but is also vulnerable to network disruptions. While most existing studies focus on risk, the vulnerability of hazmat transportation networks has received limited attention. This study proposes a novel demand-based vulnerability assessment framework for the inland waterway network for hazmat transportation (IWNHT), where transportation demand is estimated using Automatic Identification System (AIS) data from hazmat ships. The framework not only considers navigational constraints and external influences to capture realistic IWNHTs operating scenarios, but also quantifies the impact of disruptions on transportation demand and traffic conditions to enhance risk and vulnerability assessment accuracy. A risk-based vulnerability indicator is then designed. The proposed method is applied to the IWNHT of the Pearl River Delta (PRD), showing its effectiveness in identifying critical links. Moreover, a comparative analysis of the link vulnerability and risk is presented, highlighting the differences and connections between the two. The findings provide valuable insights for decision-making in IWNHTs, enhancing network reliability and transportation safety.]]></description>
      <pubDate>Fri, 18 Jul 2025 15:10:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2557114</guid>
    </item>
    <item>
      <title>Development of Methodologies for Managing Process Safety Risk Using Hazard and Operability (HAZOP) and Layers of Protection Analysis (LOPA) for Liquefied Natural Gas (LNG) Facilities</title>
      <link>https://trid.trb.org/View/2536191</link>
      <description><![CDATA[This report evaluates Process Hazard Analysis (PHA) methodologies for Liquefied Natural Gas (LNG) facilities, focusing on Hazard and Operability (HAZOP) studies and Layers of Protection Analysis (LOPA). It provides structured approaches for managing LNG hazards in production, storage, and transport. A review of approximately 100 sources identified best practices and key methodologies, including Preliminary Hazard Review, Hazard Identification (HAZID), Fault Tree Analysis (FTA), Event Tree Analysis (ETA), and Quantitative Risk Assessment (QRA). Supporting techniques such as risk evaluation, hazard scenario identification, and failure frequency analysis enhance PHA effectiveness. The report recommends tailored PHA methodologies for each project stage, from conceptual design to operation and maintenance, ensuring compliance with local regulations and industry standards. Practical insights into PHA implementation, including preparation, execution, and follow-up, are provided alongside an illustrative HAZOP/LOPA example with process flow diagrams and risk rankings. Key recommendations include updating 49 CFR Part 193 to align with modern safety standards, expanding PHA requirements, and integrating contemporary risk assessment frameworks. The LNG industry is encouraged to enhance risk assessment during Front-End Engineering Design (FEED), adopt HAZOP as a standard, and utilize multiple PHA methodologies to improve safety outcomes. By implementing structured PHA approaches, LNG operators can enhance safety, regulatory compliance, and operational efficiency, ensuring the long-term integrity of LNG infrastructure.]]></description>
      <pubDate>Wed, 23 Apr 2025 10:18:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2536191</guid>
    </item>
    <item>
      <title>Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods</title>
      <link>https://trid.trb.org/View/2509485</link>
      <description><![CDATA[The current risk assessment methods for dangerous goods roads have the problem of being unable to cope with complex road conditions and the influence of multiple factors. This study extends 9 tertiary indicators from three secondary indicators: personnel factors, vehicle factors, and road factors, to evaluate the transportation risk of dangerous goods. After calculating the weights of each indicator, this study improves the parameters of the particle swarm algorithm using the aggregation and foraging behavior of artificial fish, and uses the improved algorithm to solve the optimal solution for the cost of dangerous goods road transportation. After experimental verification, the improved hybrid algorithm has optimized the path transportation time by 13.9 % compared to a single algorithm model. The total risk of simultaneously improving the algorithm was 0.8863, and the total transportation distance was 861 km, both lower than other algorithms. The comprehensive analysis shows that the established model is reasonable, and the designed improved hybrid algorithm can improve the efficiency of the transportation industry, while also contributing to the improvement of the current cost status of dangerous goods road transportation.]]></description>
      <pubDate>Wed, 26 Mar 2025 16:37:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509485</guid>
    </item>
    <item>
      <title>Probabilistic modelling of optimal placement strategies of hazardous materials railcars in freight trains</title>
      <link>https://trid.trb.org/View/2509603</link>
      <description><![CDATA[Hazardous materials (hazmat) cars are subject to differing probabilities of being involved in a derailment depending on their position in trains. For decades there has been discussion and debate about whether operating practices and regulations should account for this to reduce the chance of railcars carrying hazmat being involved if a train derails. This paper presents a new, position-dependent, railcar-based method to systematically analyze derailment probability of hazmat cars and identify optimal placement strategies that minimize the expected number of hazmat cars derailed. This new method iteratively accounts for train makeup, derailment speed, train length, and the fraction of hazmat cars in the train. A case study based on realistic train configurations and operational conditions with a sensitivity analysis is presented. The results indicate that there is no single placement strategy that minimizes hazmat car derailment probability under the variety of operational characteristics typical of North American freight train operation. This has implications for rail hazmat transportation safety, operations, efficiency, and regulatory policy. This research advances the understanding of the effect of hazmat car placement on operating safety and risk and enables development of holistic quantitative models to address the trade-off between hazmat train operating safety and efficiency that accounts for both mainline derailment severity and yard activities related to train make-up.]]></description>
      <pubDate>Tue, 18 Mar 2025 15:48:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509603</guid>
    </item>
    <item>
      <title>Accident analysis of waterway dangerous goods transport: Building an evolution network with text knowledge extraction</title>
      <link>https://trid.trb.org/View/2483776</link>
      <description><![CDATA[To clarify the risk factors and evolutionary characteristics affecting the safety of waterway dangerous goods transport, this study constructed an accident evolution network using unstructured data from accident reports. The authors integrated Natural Language Processing (NLP) technologies and complex network model to enhance accident analysis accuracy and efficiency. Initially, using publicly available data from the China Maritime Safety Administration and the Changjiang Maritime Safety Administration, the authors created a knowledge corpus on waterway dangerous goods transport accidents. This involved data preprocessing, annotation, and augmentation.The authors then developed a method for extracting knowledge by combining Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Long Short-Term Memory networks (BiLSTM), and Conditional Random Fields (CRF) to identify accident-related entities. The authors employed Word2vec and K-means++ to vectorize and cluster these entities, standardizing the categories to build a complex network describing accident evolution. Finally, by analyzing the topology and robustness of the network, the authors uncovered the logical pathways of accidents involving the transport of dangerous goods on waterways. The results demonstrate that het authors' methods effectively visualize risk factor interactions and their impact on accident progression, aiding in the development of preventive measures for the waterway transport of dangerous goods.]]></description>
      <pubDate>Mon, 27 Jan 2025 08:55:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2483776</guid>
    </item>
    <item>
      <title>On the application of Valuation-Based Systems in the assessment of the probability bounds of Hazardous Material transportation accidents occurrence</title>
      <link>https://trid.trb.org/View/2451475</link>
      <description><![CDATA[An important issue in Hazardous Material (hazmat) transportation risk assessment is to evaluate the probability bounds of accidents occurrence, whose values are difficult to be estimated due to its low frequency and the related lack of statistical data. This paper presents an original approach to integrate uncertainty in the quantitative analysis of hazmat transportation accidents. The proposed approach is based on the use of Valuation-Based Systems (VBSs) and belief functions theory. Furthermore, the authors propose to identify the factors for which the reduction of epistemic uncertainty (imprecision) gives the greatest impact on the uncertainty of the final results by using some proposed measures. The applicability and generality of the proposed approach is demonstrated on a case study.]]></description>
      <pubDate>Thu, 12 Dec 2024 17:07:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2451475</guid>
    </item>
    <item>
      <title>An Exact A*-Based Tree Search Algorithm for TSP With Sequence-and-Load Dependent Risk</title>
      <link>https://trid.trb.org/View/2425253</link>
      <description><![CDATA[The hazardous material transportation requires extensive care owing to the disastrous consequences of accidents, such as chemical spills or radioactive exposures. Consequently, a minimum risk delivery plan that is dynamically decided by the cargo load of the vehicle at each customer must be scheduled. The authors introduce a traveling salesman problem (TSP) with a sequence-and-load dependent risk, which differs from the conventional TSP as the arc costs are determined by the hazardous cargo load at each decision epoch. The authors define their problem in a dynamic programming formulation and present mixed-integer linear program with a nonlinear objective function. To efficiently retrieve exact optimal solutions, the authors propose an iterative-deepening A*-based tree search algorithm using admissible lower and efficient upper bound algorithms for guaranteed optimality. Numerical experiments indicate that the proposed algorithm outperforms a current state-of-the-art solver. An ablation study and sensitivity analysis demonstrate the effectiveness of the proposed algorithm and derive managerial insights.]]></description>
      <pubDate>Wed, 27 Nov 2024 13:42:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2425253</guid>
    </item>
    <item>
      <title>Determining the Maximum Permissible Temperature Drops for Steel When Exposed to Cryogenic Liquid</title>
      <link>https://trid.trb.org/View/2448903</link>
      <description><![CDATA[For this research program, the authors will investigate, test, and determine the maximum permissible rate of temperature changes for various types of steel when exposed to cryogenic liquid. Representative tank and pipe configurations and Liquefied Natural Gas (LNG) spill scenarios will be selected from the team's extensive project database. Detailed finite element (FE) models will be developed for the analyses and tests will be performed at the in-house laboratory. The authors will also perform API 579 fitness-for-service (FFS) assessment of typical containment tank and pipe-in-pipe systems at various temperatures based on toughness testing results.  Anticipated Results: The authors believe that the approach outlined in the proposal will meet the objective of this study to determine the maximum permissible rate of temperature changes for various representative alternative types of steel when exposed to cryogenic liquid. This will, in turn, inform Department of Transportation/Pipeline and Hazardous Materials Safety Administration (DOT/PHMSA) about the risks and mitigation methods to increase the safety of LNG production in the U.S. The study will fill the knowledge gaps in terms of material properties for manufactured steel components at cryogenic temperatures and will provide mitigation recommendations to reduce brittle fracture risks due to cryogenic liquid exposure. The findings of the study will be presented at conferences and code committee meetings to inform the industry.  Potential Impact on Safety: The study will increase awareness and inform the industry about potential failure risks due to cryogenic exposure of commonly used steel types and details at LNG plants. Informing the key stakeholders about the behavior of steel and manufactured components at low temperatures, which are not fully covered in the design codes, is expected to reduce component failure risks.]]></description>
      <pubDate>Wed, 27 Nov 2024 13:41:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2448903</guid>
    </item>
    <item>
      <title>Dangerous Zone During Transportation of Dangerous Goods by Rail</title>
      <link>https://trid.trb.org/View/1973003</link>
      <description><![CDATA[The provision of chemical, biological, radiation, and fire safety for people, property, infrastructure facilities, and natural environment is the focus of attention of the UN specialized agencies, national governments, managers, and specialists of industrial complexes that manufacture and operate hazardous facilities. According to the analysis, during transportation of dangerous goods by all modes of transport does not ensure an adequate level of risk, as a result of which abnormal situations, various breakdowns, incidents, and emergencies continue to occur. The manifestation of risk causes considerable damage to the transport workers, transportation infrastructure, transported goods and freight units, natural environment, residents of neighboring communities, and people who happened to be in a dangerous zone. The topicality of research is dictated by the necessity of further improving the methods for protection and damage prevention, reduction in social and economic costs and environmental damage. The purpose of research is to develop elements of an integrated security system for zone of dangerous goods by federal railway transport with the possibility to employ them on international scale and in other modes of transport. Improving the concept of dangerous zone during transportation of dangerous goods stands in as the first-priority work stage of the development process. Solving the main tasks of the research was based on such proven and fruitful theoretical and experimental methods as computer modeling, comparative typology and circular expert estimations, statistical analysis, probability theory, theory of similarity, and others. The most meaningful results of the development are both of theoretical and practical importance. The theoretical result consists in development of a new approach to the concept of dangerous zone during transportation of dangerous goods by rail. The practical result involves development of proposals for improvement of the Railway Transport System of Emergency Prevention and Response; introduction of amendments into the Safety Rules for Response to Emergencies related to Dangerous Goods during Their Transportation by Rail; emergency cards for dangerous goods and a number of other technical guidance documents. Processing of the experimental data has established that the expected effectiveness of the development is 7.5–10%.]]></description>
      <pubDate>Wed, 06 Nov 2024 16:48:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1973003</guid>
    </item>
    <item>
      <title>Developing a Safety Management System including Hazardous Materials for Highway-Rail Grade Crossings in Region VII</title>
      <link>https://trid.trb.org/View/2437352</link>
      <description><![CDATA[Highway-rail grade crossings (HRGCs) rank among the leading locations for fatal crashes on the railroad network in the United States, and safety at HRGCs is a top priority for the railroads and the Federal Railroad Administration (FRA). This project studied HRGC safety needs by investigating crash data and HRGC inventory characteristics, and then developed a systematic framework for HRGC safety management in three steps. First, the project started with preparing a comprehensive database that included 1) HRGC crashes with geographic coordinates, 2) HRGC inventory data, 3) highway and train traffic operations data, and 4) HRGC crash-related hazardous materials release data. These data were obtained from the FRA and spanned across the four states in Region VII, i.e., Nebraska, Kansas, Iowa, and Missouri. Second, different accident prediction models for HRGC crash prediction were compared. These models included the accident prediction and severity (APS) model recommended by the FRA and other commonly used crash prediction models such as general linear regression models with fixed or random effects, zero-inflated models, and hurdle models. The APS model was found the best fit for the HRGC data in Region VII area. Therefore, the APS model was calibrated and validated including the index of hazardous materials released and its impact on the surrounding areas resulting from HRGC crashes. A risk score model was developed to rank the HRGCs. Finally, a prototype HRGC Safety Management System (SMS) was developed. The prototype underwent testing utilizing crash data from Nebraska and was initially implemented specifically for the state of Nebraska. The prototype SMS structure was designed so that it could be adopted by state Departments of Transportation (DOTs) in Region VII and across the United States. This project benefits the quality of information provided to decision-makers and enhances the statewide safety management of HRGCs. In particular, the development of this SMS can assist HRGC managers in being proactive to safety and risk situations at HRGCs.]]></description>
      <pubDate>Mon, 07 Oct 2024 08:38:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2437352</guid>
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