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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <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>Bridging hazard perception gaps in aviation: Design and initial evaluation of systemic training and reporting tool interventions based on the Accident Scenario model</title>
      <link>https://trid.trb.org/View/2675059</link>
      <description><![CDATA[Aviation Safety Management Systems (SMS) are often undermined by persistent gaps in hazard perception and reporting effectiveness, which conventional interventions fail to address systemically. This study confronts this challenge by developing, justifying, and initially evaluating two theory-driven interventions designed to improve hazard identification consistency and analytical capability: a systemic training program and an enhanced reporting tool. Employing design-based research, we grounded this work in needs identified within a diverse Indonesian aviation provider (encompassing flight operations, maintenance, airfield, and training) and utilized the Accident Scenario model as the theoretical framework. We designed targeted “Accident Scenario Training” program to build shared mental models and an enhanced Safety Observation Report (SOR) form integrating model elements to structure hazard analysis. Initial formative evaluation of the training (N≈20) yielded a detailed curriculum and SOR prototype. While the model showed potential for clarifying hazard concepts, severe organizational barriers (attendance <50% due to systemic workload conflicts) hampered implementation feasibility, revealing crucial context-intervention interactions. The enhanced SOR offers a structured mechanism shifting reporting from description to proactive analysis. This paper details a systematic, theory-driven design process for practical interventions targeting cognitive SMS gaps, providing rare empirical insights into implementation challenges, and offering a novel model-based SOR design.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:57:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675059</guid>
    </item>
    <item>
      <title>Cognitive Processing Disruptions Affecting Flight Deck Performance: Implications for Cognitive Resilience</title>
      <link>https://trid.trb.org/View/2680916</link>
      <description><![CDATA[The flight deck of a commercial aircraft has become progressively digitized and operates in multiple modes with displays and indicators that require increasing levels of comprehension. Examining several aspects of cognitive processing is important to understand how threats to safety might occur and what actions might be taken to reduce severity or to eliminate the threat altogether. This paper presents the elements of cognition to consider, relevant characteristics of working memory and cognitive processing speed, types of disruptions and how they are addressed, results from overload or confusion, and the need for effective cognitive resilience to recover and repair the threat. Data from Aviation Safety Reporting System (ASRS) databases indicate 30% of cases could represent a distinct threat of cognitive overload. These are evaluated to identify sources and likelihood for surprise disruptions and to assess the potential of cognitive resilience. Adaptation of the CRM-TEM model is considered for potential application in training and investigations.]]></description>
      <pubDate>Sat, 02 May 2026 15:47:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680916</guid>
    </item>
    <item>
      <title>A Deep Learning Framework for Aviation Risk Classification and High-Order Coupled Risk Modeling</title>
      <link>https://trid.trb.org/View/2660564</link>
      <description><![CDATA[Aviation risk analysis can be a useful empirical foundation using narrative incident reports gathered by the Aviation Safety Reporting System (ASRS), but due to its long-form format, class imbalance, and domain-specific semantics, automated modelling can be a challenging problem. To respond to these challenges, this study develops a domain-adapted deep learning model built upon the Robustly Optimized Bidirectional Encoder Representations from Transformers pretraining approach (RoBERTa) for multi-label identification of contributing factors in aviation safety reports. The proposed model improves multi-label classification performance by integrating four modules: instruction-based large language models (LLMs) data augmentation to reduce imbalance, a merging module to jointly model the narrative text and metadata, a composite loss to strengthen robustness in case of label imbalance, and domain adaptive pretraining on corpora. The experimental results indicate that the model achieves reliable improvements, while ablation experiments further clarify impact of each module. Based on the predicted contributing factors, an N-K model is constructed to quantify interaction strength, and a Bayesian network is used to model directed risk propagation. By accounting for both structural coupling and propagation probability, the framework identifies and ranks risk pathways that correspond to plausible accident developments. A case study demonstrates that the proposed approach can extract high-order, multi-domain propagation paths from narrative data, enabling structured interpretation of plausible accident evolution patterns. Taken together, the proposed framework provides a pipeline that converts incident narratives into actionable safety information, offering a scalable and structured basis for proactive aviation risk analysis.]]></description>
      <pubDate>Wed, 22 Apr 2026 14:04:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2660564</guid>
    </item>
    <item>
      <title>Analysis of emergency decision-making patterns in civil aviation risk events based on the observe-decide-act model</title>
      <link>https://trid.trb.org/View/2648004</link>
      <description><![CDATA[Aviation emergencies are critical to flight safety, yet the core decision-making patterns and their dynamics remain underexplored empirically. This study leverages large-scale ASRS data to analyze decision-making patterns and their association with event outcome severity in aviation fire scenarios. Integrating the Observe-Decide-Act (ODA) decision model, this research develops a data-driven framework combining large language model (LLM)-based deep semantic extraction, multi-level clustering, and dynamic sequence analysis to systematically examine 300 ASRS fire incident reports. This multi-level analysis reveals that emergency decision-making characteristics show a strong association with the final event outcome severity. At the element level, the study identifies core ODA decision elements, demonstrating that their functional importance and coordination vary systematically with the event's severity level. At the pattern level, it uncovers and characterizes six operationally distinct ODA decision-making patterns, with their usage frequencies exhibiting a strong association with the classified severity level. At the dynamic level, it reveals that decision processes generally transition from exploratory behaviors to a subsequent convergence, with this convergence being more pronounced in high-severity event outcomes. Collectively, these findings provide a comprehensive depiction of how aviation emergency decision-making patterns are associated with scenarios of varying event severity. This study offers an empirical understanding for human decision-making in high-risk environments, which is essential for optimizing pilot training and emergency procedure design.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:15:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648004</guid>
    </item>
    <item>
      <title>Keyphrase-enhanced multi-level heterogeneous text graph construction strategy based graph convolutional neural network for imbalanced aviation report risk analysis</title>
      <link>https://trid.trb.org/View/2647900</link>
      <description><![CDATA[Due to the high safety requirements in the aviation field, accurate identification of high-risk events is crucial. However, the unsafe event reports collected in the Aviation Safety Report System (ASRS) has strong domain properties. Furthermore, the lack of data on high-risk events results in a significant sample imbalance. Those pose a great challenge for reliable risk analysis. In this paper, a novel keyphrase-enhanced multi-level heterogeneous text graph construction strategy based on graph convolutional neural networks (KEMH-GCN) is proposed for imbalanced aviation report risk analysis. First, a keyphrase-based data augmentation strategy is proposed. The keyphrases for each risk level are extracted to generate new pseudo-report by Generative Adversarial Networks (GAN). Second, a multi-level heterogeneous text graph construction strategy integrating word-level, keyphrase-level and report-level nodes is proposed. The proposed text graph construction strategy enables comprehensive and in-depth modeling of the aviation reports from multiple different granularities. Then, the GCN model with constructed text graph extracts the report features for risk analysis. Finally, to verify the superiority of the proposed method, seven benchmark models are selected for comparison. The experimental results show that the proposed KEMH-GCN significantly improves the accuracy of risk analysis which provides reliable support for identifying aviation safety risks.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:11:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647900</guid>
    </item>
    <item>
      <title>VMD-BiGRU-ATT Architecture for Aviation Failure Events Prediction Based on the Grey Wolf Optimization Algorithm</title>
      <link>https://trid.trb.org/View/2623505</link>
      <description><![CDATA[Accurately predicting aviation failure events is essential for anticipating future safety risks and preventing severe, uncontrollable accidents. However, this task is complicated by the massive volume of data, complex temporal patterns, nonlinear and highly volatile characteristics, all of which significantly increase the computational and operational burden of accurate forecasting. To address these challenges, this study proposes a hybrid model that combines Variational Mode Decomposition (VMD), Grey Wolf Optimizer (GWO), Attention Mechanism (ATT), and Bidirectional Gated Recurrent Unit (BiGRU). VMD is applied in preprocessing to reduce noise and improve the extraction of the multi-scale features of time series. ATT adaptively assigns weights to highlight critical features, while BiGRU captures global context and long-term dependencies to better model temporal patterns. Finally, GWO optimizes BiGRU’s hyperparameters, improving convergence and precision, thereby reducing model error. The proposed approach is evaluated using data from the Aviation Safety Reporting System (ASRS) and compared with several existing methods. The results demonstrate that the proposed model achieves higher prediction accuracy for failure events than individual models and exhibits superior robustness and generalization capabilities. These findings indicate that the proposed method offers improved performance in forecasting aviation failure events.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2623505</guid>
    </item>
    <item>
      <title>Enhanced small-sample aviation accident prediction via an improved WCGAN incorporating NeuralProphet and gradient penalty</title>
      <link>https://trid.trb.org/View/2585771</link>
      <description><![CDATA[Aviation accidents are characterized by their low frequency and high destructive power, making accurate prediction crucial for enhancing safety measures. Existing aviation safety prediction methods often struggle to extract meaningful trend information from limited accident data, which impedes effective risk management and decision-making. In response to these challenges, this paper proposes a novel small-sample aviation accident prediction method based on an improved Wasserstein distance conditional generative adversarial network (WCGAN). The generator integrates NeuralProphet for its hybrid architecture that combines Prophet's interpretability with neural networks' adaptability, effectively modeling sparse aviation accident data while mitigating overfitting common in time series prediction methods. Additionally, to address the mode collapse issue prevalent in traditional generative adversarial networks, the discriminator incorporates a gradient penalty (GP) mechanism, enhancing robustness and training stability. Using the publicly available Aviation Safety Reporting System (ASRS) dataset, the proposed method was rigorously evaluated against various existing aviation safety prediction techniques. The experimental results demonstrate remarkable improvement in accuracy, with the proposed method outperforming all baseline models by over 16%. This significant enhancement underscores the model’s capability to provide more accurate and actionable insights for safety decision-making.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2585771</guid>
    </item>
    <item>
      <title>Factors that Affect Pilot Response Times to Alerts: Findings From a Literature Review and Aviation Safety Reporting System (ASRS) Reports</title>
      <link>https://trid.trb.org/View/2479923</link>
      <description><![CDATA[Factors affecting the time it takes a human to respond to an alert may include alerting system design, operator characteristics such as age or experience, or the operating environment. Using the aviation domain as one example, this variety of factors makes it difficult to specify what “acceptable” pilot response times are, as one cannot assign a value that will be consistent across systems, people, and situations. Further, characteristics of the pilot population, operations, and flight deck technologies change over time, so that average pilot response times derived in the past may not represent accurately expected response times for pilots flying modern aircraft. This paper compiles the findings from both a literature review and a survey of NASA Aviation Safety Reporting System (ASRS) reports, documenting that the confluence of factors affecting pilot responses to alerts results in response times that are often longer and more variable than might otherwise be expected.]]></description>
      <pubDate>Fri, 18 Jul 2025 09:05:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2479923</guid>
    </item>
    <item>
      <title>Evaluating near midair collision reporting systems using aircraft surveillance data: A case study at a university airport</title>
      <link>https://trid.trb.org/View/2431840</link>
      <description><![CDATA[A near midair collision (NMAC) is defined by the Federal Aviation Administration as an event in which the crew of an aircraft perceives a situation that could lead to a midair collision or an event in which the separation between two aircraft is less than 500 feet (Federal Aviation Administration, 2018). NMAC reports collected by safety reporting systems have long been used to study and mitigate midair collision risk. However, reports submitted are subjective and require the reporter to voluntarily provide this information. With the implementation of Automatic Dependent Surveillance-Broadcast, aircraft temporal and positional data are easily collectible. Using internal safety system, NASA Aviation Safety Reporting System, and FAA Near Midair Collision System reports together with ADS-B data, the effectiveness of different safety reporting systems to collect NMAC data around a towered university airport were compared over a six-month period. Unreported events were identified by utilizing ADS-B data to calculate aircraft separation events of less than 500 feet. While 10 events were reported to the internal safety system, only one was reported to the NASA ASRS, and none to the FAA NMACS within the study’s scope. Sixteen events in which aircraft were within 500 feet of each other were found using ADS-B data, with none of these events having been reported to any safety reporting system. The findings of this study highlight the need for increased sharing in aviation safety data and show the potential of ADS-B data as a tool in studying near midair collisions and midair collision risk. NMAC safety reporting systems may be less effective than expected. The addition of other sources of data such as ADS-B may be necessary to identify and investigate NMACs and other relevant near miss safety events.]]></description>
      <pubDate>Fri, 04 Oct 2024 09:18:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2431840</guid>
    </item>
    <item>
      <title>Flight Deck Perspectives on Performance-Based Navigation (PBN) Departure Procedures</title>
      <link>https://trid.trb.org/View/2414316</link>
      <description><![CDATA[This project examines flight deck human factors considerations for Performance Based Navigation (PBN) departure procedures (DPs), focusing on issues relevant to the Multiple Airport Route Separation (MARS) concept. MARS will expand the use of PBN routes in the terminal area to improve the flow of traffic in busy areas with multiple airports. The authors collected data from three sources to address research gaps identified from an earlier literature review. The authors learned about current issues with DPs from interviews with technical pilots and an analysis of 20 records curated from the Aviation Safety Reporting System (ASRS) database. The authors gathered data about factors that contribute to flightpath deviations on DPs from the ASRS records and from discussions with nine line pilots. The authors also gathered data on assessment of traffic threat by showing the line pilots simulated traffic overlaid on static navigation display images. Results indicate that there are many explanations for why pilots might deviate from a planned PBN departure route, some intentional and some unintentional. The limited data on traffic-threat assessment indicate that there are conditions under which pilots may seek more information, or even prepare to take an action to avoid traffic by modifying their flightpath if traffic is in an unexpectedly close relative position.]]></description>
      <pubDate>Sun, 18 Aug 2024 11:28:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2414316</guid>
    </item>
    <item>
      <title>Application of text mining and coupling theory to depth cognition of aviation safety risk</title>
      <link>https://trid.trb.org/View/2343553</link>
      <description><![CDATA[Aviation accidents have attracted a lot of attention due to the fatalities and considerable property loss. The complexity underlying these accidents is often the result of multiple risk factors coupling together, thereby increasing the difficulty of risks identification and comprehensive controlling. It is necessary to enhance proactive safety management capabilities and aircraft operational reliability through deeply cognitive risk information. This paper explores the application of Latent Dirichlet Allocation (LDA) model of text mining and coupling theory in two aviation safety datasets. The results of applying LDA topic modeling categorize risks across different datasets. Subsequently, the N-K model is employed to measure various coupling modes of risk factors, and the coupling effects between different risk factors were further analyzed. The results indicate that the two datasets identify different risk categories, and the coupling values of the risk factors usually increase with the number of factors. The results also reveal a higher degree of coupling among three risk factors: pilot manipulation, aircraft systems, and aircraft engines in the NTSB dataset. The proposed methodology shows significance in enhancing the accuracy of aviation risk analysis and the efficiency of accident prevention.]]></description>
      <pubDate>Mon, 15 Apr 2024 08:38:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2343553</guid>
    </item>
    <item>
      <title>Multiple machine learning modeling on near mid-air collisions: An approach towards probabilistic reasoning</title>
      <link>https://trid.trb.org/View/2320916</link>
      <description><![CDATA[This work presents a mathematical model to predict and explain Near Mid-Air Collisions (NMACs) based on the NASA Aviation Safety Reporting System (ASRS) database. The ASRS database contains more than 200,000 aviation incidents, which are used to learn how the combination of risk influencing factors (RIFs), such as crew size and component fatigue, affects the safety of airspace operations. Bayesian Networks (BNs) combine theory of probabilities with theory of graphs and are considered one of the most effective theoretical models in the fields of knowledge representation and reasoning with uncertainty. The resulting model allows to calculate the posterior probabilities of some targeted outputs, therefore providing a mathematically consistent framework to quantify and to compute with uncertainty the likelihood of incident occurrence over time when some factors are known. Furthermore, the bidirectional reasoning technology of BNs can calculate the posterior probabilities of its variables under the system incident condition, and find out the most likely combination that caused a NMAC. Finally, the resulting probabilistic models are compared with sixteen Machine Learning Algorithms, and advantageous properties were critically evaluated, such as a white-box reasoning and probability as a measure of certainty about the state of unobserved variables.]]></description>
      <pubDate>Fri, 01 Mar 2024 08:55:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2320916</guid>
    </item>
    <item>
      <title>Flightcrew Response to Aircraft System Failures, Malfunctions, and Systems Not Functioning as Expected</title>
      <link>https://trid.trb.org/View/2289147</link>
      <description><![CDATA[The primary goal of this research was to analyze operational data to study flightcrew response to system failures, malfunctions, and systems not functioning as expected. Data from normal flight operations show that pilots are exposed to such situations regularly. The authors reviewed 20 records from the Aviation Safety Reporting System (ASRS) public database and five public accident reports (Qantas 72, US Airways 1549, Qantas 32, Lion Air 610, and Ethiopian Airlines 302). Data from the accident reports are presented at three levels of detail. First, a short summary of basic facts introduces the reader to the malfunctions and pilot responses that occurred. Second, a bulleted summary focuses on the flightcrew perspective, what they experienced, and how they responded to the malfunction and overall situation. Finally, detailed quotes from the accident reports that support the summaries are provided in an appendix. The authors highlight key observations from both the ASRS analysis and the review of accidents. In addition to the data analysis, this report has a review of associated literature covering research on aircraft system problems, alerts and checklists, pilot training, and pilot response. The literature review illustrates the broad scope, variety, and depth of research connected to pilot response.]]></description>
      <pubDate>Mon, 20 Nov 2023 09:10:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2289147</guid>
    </item>
    <item>
      <title>Flight Demonstration of High Altitude Aircraft Navigation With Cellular Signals</title>
      <link>https://trid.trb.org/View/2211572</link>
      <description><![CDATA[This article presents the first demonstration of navigation with cellular signals of opportunity (SOPs) on a high-altitude aircraft. An extensive flight campaign was conducted by the Autonomous Systems Perception, Intelligence, and Navigation Laboratory in collaboration with the U.S. Air Force to sample ambient downlink cellular SOPs in different regions in Southern California, USA. Carrier phase measurements were produced from these signals, which were subsequently fused in an extended Kalman filter along with altimeter measurements to estimate the aircraft’s state (position, velocity, and time). Three flights are performed in three different regions: 1) rural, 2) semiurban, and 3) urban. A multitude of flight trajectories and altitudes above ground level (AGL) was exercised in the three flights: 1) a 51-km trajectory of grid maneuvers with banking and straight segments at about 5,000 ft AGL, 2) a 57-km trajectory of a teardrop descent from 7,000 ft AGL down to touchdown at the runway, and 3) a 55-km trajectory of a holding pattern at about 15,000 ft AGL. The estimated aircraft trajectory is computed for each flight and compared with the trajectory from the aircraft’s onboard navigation system, which utilized a GPS receiver coupled with an inertial navigation system and an altimeter. The cellular SOPs produced remarkable sustained navigation accuracy over the entire flight trajectories in all three flights, achieving a 3D position root mean-squared error of 10.53 m, 4.96 m, and 15.44 m, respectively.]]></description>
      <pubDate>Fri, 21 Jul 2023 15:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2211572</guid>
    </item>
    <item>
      <title>Operational Complexity in Performance-Based Navigation Arrival and Approach Flight Operations</title>
      <link>https://trid.trb.org/View/1973025</link>
      <description><![CDATA[We studied how pilots handle operational variations during arrival and approach instrument flight procedures (IFPs), focusing on factors that may be related to performance-based navigation (PBN). PBN is a key enabler of the Next Generation Air Transportation System (NextGen). We developed a factor rubric based on an iterative review of events in the Aviation Safety Reporting System (ASRS) public database and prior research. We coded 164 ASRS reports selected for relevance to PBN. We identified where each event occurred relative to the route of flight, tallied the coded factors and event outcomes, and gathered data on crew actions that indicated resilience to operational variations such as unexpected behavior of aircraft automated systems. We conclude that PBN appears to magnify the effects of operational complexity for these events. Pilots would benefit from training that provides opportunities to experiment with new situations they could encounter in PBN scenarios.]]></description>
      <pubDate>Wed, 22 Feb 2023 09:57:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1973025</guid>
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