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    <title>Transport Research International Documentation (TRID)</title>
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>The NASA Digital VGH Program - Exploration of Methods and Final Results: Volume IV - B 747 Data 1978-1980: 1689 Hours</title>
      <link>https://trid.trb.org/View/2770080</link>
      <description><![CDATA[B 747 Digital Flight Data taken in 1978 through 1980 were analyzed to provide many statistical data useful for updating airliner airworthiness design criteria.]]></description>
      <pubDate>Sat, 05 Sep 2026 11:31:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2770080</guid>
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      <title>The NASA Digital VGH Program - Exploration of Methods and Final Results: Volume V - DC 10 Data 1981-1982: 129 Hours</title>
      <link>https://trid.trb.org/View/2770143</link>
      <description><![CDATA[DC 10 Digital Flight Data Recorder data taken in 1981 through 1982 were analyzed to provide statistical data useful for updating airliner airworthiness design criteria.]]></description>
      <pubDate>Sat, 05 Sep 2026 11:31:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2770143</guid>
    </item>
    <item>
      <title>The NASA Digital VGH Program - Exploration of Methods and Final Results: Volume I - Development of Methods</title>
      <link>https://trid.trb.org/View/2769876</link>
      <description><![CDATA[Two hundred hours of Lockheed L 1011 Digital Flight Data Recorder data taken in 1973 were used to develop methods and procedures for obtaining statistical data useful for updating airliner airworthiness design criteria. Five thousand hours of additional data taken in 1978-1982 are reported in Volumes II, III, IV and V.]]></description>
      <pubDate>Sat, 05 Sep 2026 11:31:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2769876</guid>
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    <item>
      <title>The NASA Digital VGH Program - Exploration of Methods and Final Results: Volume II - L 1011 Data 1978-1979: 1619 Hours</title>
      <link>https://trid.trb.org/View/2769350</link>
      <description><![CDATA[L 1011 Digital Flight Data Recorder data taken in 1978-1979 were analyzed to provide many statistical data types useful for updating airliner airworthiness design criteria.]]></description>
      <pubDate>Sat, 05 Sep 2026 11:31:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2769350</guid>
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      <title>The NASA Digital VGH Program - Exploration of Methods and Final Results: Volume III - B 727 Data 1978-1980: 1765 Hours</title>
      <link>https://trid.trb.org/View/2770123</link>
      <description><![CDATA[B 727 Digital Flight Data Recorder data taken in 1978 through 1980 were analyzed to provide many statistical data useful for updating airliner airworthiness design criteria.]]></description>
      <pubDate>Sat, 05 Sep 2026 11:31:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2770123</guid>
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    <item>
      <title>Omega Data Bank Report (Spring and Fall 1980)</title>
      <link>https://trid.trb.org/View/2709289</link>
      <description><![CDATA[The International Bank for Airborne Omega Data continued operation at the Federal Aviation Administration (FAA) Technical Center. This report, issued by the Data Bank, is based upon 427 flight data hours covering flights in the North Atlantic, parts of the Continental United States (U.S.) and the Caribbean, South America, and Canada. These data were collected during the spring and fall of 1980; no flights were made during the summer. There were four major contributors to the Omega Data Bank during this period with three different equipment types. Operationally usable signals corresponded quite well with the Omega signal coverage prediction diagram published by the Omega Navigation System Operational Detail (ONSOD). Exceptions were noted near Ellesmere Island for the La Reunion signal, and the continental U.S. for the Argentina signal for the specific months and times of the data flights. Several operational differences were noted between two different Omega sets flown side by side in an FAA aircraft during flights in South America and the South Atlantic. Nonetheless, for both sets, Omega positions were within 2 nautical miles of the Inertial Navigation System position (95 percent probability) during normal flight conditions.]]></description>
      <pubDate>Mon, 22 Jun 2026 12:22:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709289</guid>
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    <item>
      <title>Flight Loads and Airframe Usage Analysis of Next-Generation Airtankers – CL-415</title>
      <link>https://trid.trb.org/View/2709282</link>
      <description><![CDATA[This report presents the results of an analysis of operational data from a fleet of four CL-415 Super Scooper aircraft flown in support of the United States Forest Service aerial firefighting operations. The aircraft were equipped with IONode100 digital flight data recorders supported by Latitude Technologies Corporation. Data used for this report was collected over the calendar years 2015-2019 and consisted of approximately 4,700 hours of flight time, almost equally divided among the four airframes. The analysis has been limited to ground-air-ground segments of the missions, excluding ground operations. Missions have been divided into three groups: firefighting, ferry, and maintenance/training. Firefighting missions have been further divided into ten flight phases. Airframe usage has been examined for each flight and each phase of the flight. The results have been compared with aircraft limitations on airspeeds, altitudes, and load factors pertaining to individual flap deflections. Unreliable pitch and roll angles have prevented examination of flights in unusual attitudes. All aircraft are shown to have been flown well within the operation altitude limits. Incidents of excessive vertical acceleration and indicated airspeeds, for the corresponding flap deflection, are shown to have been common. Lack of clear indicators, such as weight on wheels, for water landings have prevented clear identification of points of contact with, and departure from, water landings. For airborne phases, vertical load factors due to gust and maneuver have been separated using the two-second rule. Frequency of occurrence of each type has been determined using the method of peaks-between-means. The results have been presented in the form of exceedance spectra per 1000 hours and per nautical mile for various altitude bands. The report is concluded with some recommendations for improved data acquisition for further efforts.]]></description>
      <pubDate>Tue, 09 Jun 2026 10:56:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709282</guid>
    </item>
    <item>
      <title>Flight Loads and Airframe Usage Analysis of Next-Generation Airtankers – BAe-146 and RJ-85</title>
      <link>https://trid.trb.org/View/2683233</link>
      <description><![CDATA[This report addresses in-flight recorded data from eight BAe-146s and eight RJ-85s flown in support of the United State Forest Service aerial firefighting operations. All flight data was recorded at 32 Hz by IONode100 units supported by Latitude Technologies, Corp. All data was examined for integrity, and anomalies and inaccuracies were identified and outlined. Similarities between BAe-146 and RJ-85 were used to rationalize combining their results. Flights were divided into firefighting, ferry, and maintenance/training missions. Firefighting flights were divided into five separate phases. Statistical results of the airframe usage were presented and compared with aircraft limitations. This information includes altitude, airspeed, duration and distance, number of retardant drops per flight, maximum and minimum vertical load factors, flap cycling frequency, and takeoff and landing weights. Unreliable pitch-and-roll angle and cabin pressure recordings prevented examination of these parameters. In several cases, the vertical accelerations were shown to exceed the limit load factors with flaps extended. Detailed examination of the data revealed that these occurred mostly during the drop phase. Also, in a number of cases, the maximum indicated airspeed was shown to be slightly above the prescribed limits. The recorded normal accelerations were shown to contain frequencies due to structural vibration. Results of the effect of different filters on flight loads spectra are presented in an appendix. Based on these results, a low-pass, eighth-order Butterworth filter with an 8-Hz cutoff frequency was used to attenuate the structural frequencies. After filtering, vertical load factors were divided into gust and maneuver loads using the two-second rule. Using the method of peaks-between-means, exceedance spectra for gust and maneuver loads were developed for ground-air-ground cycles, as well as for specific flight phases. These results were compared to those of the legacy airtankers and other aircraft flown in support of firefighting missions and as civil transport. The gust load factor spectra are shown to be similar to other United States Forest Service aircraft being flown in the same environment. The maneuver load factor spectra are shown to indicate smaller loads than those of legacy airtankers, but considerably exceeding in frequency those of civil transport. Derived gust velocities were extracted for BAe-146 aircraft and their cumulative occurrences were presented. Erroneous recording of the aircraft weight prevented inclusion of data from RJ-85 airframes. The report concludes with some recommendations for improved data acquisition for future efforts.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:12:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683233</guid>
    </item>
    <item>
      <title>Fine-Grained Time and Hidden Feature Learning for Interpretable Hard Landing Prediction Based on QAR Data</title>
      <link>https://trid.trb.org/View/2617684</link>
      <description><![CDATA[Hard landings, as a common type of aviation incident, have consistently attracted the attention of airlines and aviation authorities. In recent years, the widespread adoption of Quick Access Recorder (QAR) systems has led numerous researchers to focus on predicting hard landing events through the analysis of QAR data. However, most studies treat QAR data as standard time series without fully accounting for its unique characteristics. Unlike typical time series, QAR data exhibits limited periodicity and trends, making it challenging for traditional modeling approaches to capture its complex patterns. Furthermore, model interpretability, as an essential aspect for practical deployment and decision-making, remains insufficiently explored. To address these issues, we propose a Fine-Grained Time and Hidden Feature Learning model for Interpretable Hard Landing Prediction based on QAR Data (TF-QAR). Specifically, we introduce a novel fine-grained temporal aggregation module, which dynamically extracts the importance of each time step through learnable parameters, to efficiently model the temporal dependencies in QAR data. Additionally, we develop a feature aggregation module that introduces a learnable adjacency matrix to model the significance of flight features and their interrelationships, revealing not only key parameters that directly influence hard landings, but also hidden parameters that are indirectly related. We conducted extensive experiments using a dataset of 37,929 real A320 flight segments in China. The results demonstrate that our model outperforms existing state-of-the-art baselines. Moreover, by visualizing the learnable parameters, TF-QAR provides interpretable insights valuable for pilot decision-making, offering practical support for the prevention and management of hard landing events.]]></description>
      <pubDate>Tue, 24 Mar 2026 17:01:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617684</guid>
    </item>
    <item>
      <title>Comparative study of aviation nvPM emissions using quick access recorder data</title>
      <link>https://trid.trb.org/View/2633060</link>
      <description><![CDATA[The rapid expansion of global aviation transportation has amplified the environmental, climatic, and health effects of aviation emissions. As an important component of the aviation emission, the mass and number emissions of non-volatile particle matter must be evaluated accurately. This study employs seven emission index calculation methods to predict the nvPM emissions throughout flight using quick access recorder data, analyzes calculation methods’ uncertainties via Monte Carlo simulation. The impacts of fuel types and engine models on the nvPM emissions were investigated as well. The results reveal that FOX method exhibits poor stability and yields significantly higher EIₘ estimates during the LTO cycle. The EIₙ estimated by APMEP-CNN and Zhang methods are close, ranging from 10¹⁴ to 10¹⁵. Sustainable aviation fuels, such as FTS, can reduce nvPM mass emissions by 87.34%. Advanced engines reduce nvPM mass by 5–6 times and number by 4–5 times compared to conventional engines.]]></description>
      <pubDate>Wed, 10 Dec 2025 11:19:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633060</guid>
    </item>
    <item>
      <title>Real-Time Early Identification of Atmospheric Turbulence Using Flight Sensor Data</title>
      <link>https://trid.trb.org/View/2548891</link>
      <description><![CDATA[Severe atmospheric turbulence is the leading cause of in-flight injuries in civil air transport. Currently, there is no precise and reliable method for short-term in-flight turbulence alerts. To address this issue, we propose Functional Shape Feature for Real-Time Turbulence Alerting (FUTURA), a data-driven approach for real-time turbulence prediction that relies solely on existing onboard sensor data. To detect turbulence, which evolves rapidly in both time and space, FUTURA combines a steady-state Kalman filter with functional shape feature extraction and applies a functional isolation forest to detect upcoming turbulence. Due to its incremental nature and low computational complexity, FUTURA not only enables turbulence prediction in real time but also captures dynamic relationships between multiple variables that are crucial for identifying and predicting turbulence. Experimental results show that FUTURA predicts 40% of severe turbulence cases (true-positive rate) 30 s in advance while maintaining a zero false-positive rate. This meets the critical requirement of a zero false alarm to enhance passenger experience and ensure aircraft operational reliability.]]></description>
      <pubDate>Fri, 20 Jun 2025 11:58:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548891</guid>
    </item>
    <item>
      <title>What can we learn from severity index on flight data monitoring? Analysis of safety resilience in flight operations during COVID-19 disruptions</title>
      <link>https://trid.trb.org/View/2406748</link>
      <description><![CDATA[The unexpected spread of the pandemic raised concerns regarding pilots’ skill decay resulting from the significant drops in the frequency of flights by about 70%. This research retrieved 4761 Flight Data Monitoring (FDM) occurrences based on the FDM programme containing 123,140 flights operated by an international airline between June 2019 and May 2021. The FDM severity index was analysed by event category, aircraft type, and flight phase. The results demonstrate an increase in severity score from the pre-pandemic level to the pandemic onset on events that occurred on different flight phases. This trend is not present in the third stage, which indicates that pilots and the safety management system of the airline demonstrated resilience to cope with the flight disruptions during the pandemic. Through the analysis of event severity, FDM enables safety managers to recommend measures to increase safety resilience and self-monitoring capabilities of both operators and regulators.Practitioner summary: The onset of the pandemic led to a rise in the severity of flight data monitoring events in a large airline, likely linked to a lack of operational practice and skills decay. This was demonstrated across different flight phases and aircraft types. In the settled pandemic period, the severity index returned to pre-pandemic levels, indicating that the resilience of individual pilots and safety management systems is critical to operational safety.HIGHLIGHTSThe FDM event severity scores significantly increased following the pandemic onset, especially for event categories involving pilot core competencies.The FDM event severity scores stagnated or decreased during the later pandemic stage indicating resilience among the airline pilots and the airline’s safety management system.The airline and pilots demonstrated resilience by effectively mitigating the effects of proficiency decay which took place as the pandemic started.FDM analysis has shown to be effective in establishing a proactive SMS programme to mitigate the negative impacts of the pandemic on aviation safety.]]></description>
      <pubDate>Thu, 22 Aug 2024 15:11:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2406748</guid>
    </item>
    <item>
      <title>Neural Network Modeling of Black Box Controls for Internal Combustion Engine Calibration</title>
      <link>https://trid.trb.org/View/2401809</link>
      <description><![CDATA[The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development.The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities.This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation. The ECU is operated on a Hardware-in-the-Loop (HiL) rig for measurement data generation.To build surrogate models of these ECU functions, Neural Network model inputs are allocated categorized into two categories: function inputs as perceived by the logic level (White Box) software function, and curve/map fitting features representing the adjustment variables of the ECU function.Factors influencing surrogate model accuracy such as, Neural Network hyperparameter optimization, input space amount and distribution as well as the parameter adjustment is investigated. Results show an increase in accuracy with the increasing number of implemented parameters, as well as the scalability of ECU function model representation with measurement data.In addition to calibration purposes, the presented function representation method facilitates the use of plant models to replace time-consuming function construction and validation.]]></description>
      <pubDate>Mon, 08 Jul 2024 16:26:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2401809</guid>
    </item>
    <item>
      <title>SDTAN: Scalable Deep Time-Aware Attention Network for Interpretable Hard Landing Prediction</title>
      <link>https://trid.trb.org/View/2237906</link>
      <description><![CDATA[Hard landing, as one of the most frequent flight safety incidents during the landing stage, is highly concerned by the aviation industry. Recently, the popularization of Quick Access Recorder (QAR), a modern flight data recording system, has made it possible to collect large volume of flight parameters and incorporate state-of-the-art AI technologies to improve flight safety. However, due to the complex, multivariate, and highly specialized nature of QAR data, most existing studies either suffer from information loss caused by rough feature extraction methods, or rely solely on black-box models with no interpretations, making themselves difficult to achieve satisfactory performance in terms of prediction and explainability. To address this issue, the authors propose a novel attention-driven model named SDTAN (Scalable Deep Time-Aware Attention Network), which can accurately predict hard landing events and provide interpretable insights to help reveal the possible reasons leading to the events. Specifically, SDTAN fully captures information to learn the local representations of parameters, and leverages the time-interval attention mechanism to focus on the entire temporal pattern of flight over the relevant time intervals. It further re-encodes the representations of parameters in a global view and learns the global effect of parameters on the predicted output to uncover the ones which strongly indicate the flight safety status, enabling both high prediction accuracy and qualitative interpretability. They conduct experiments on real-world QAR datasets of 37,920 Airbus A320 flight samples. Experimental results demonstrate that SDTAN outperforms other state-of-the-art baselines and provides effective interpretability by visualizing the importance of parameters.]]></description>
      <pubDate>Tue, 24 Oct 2023 09:37:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2237906</guid>
    </item>
    <item>
      <title>Classification of Manifold Learning Based Flight Fingerprints of UAVs in Air Traffic</title>
      <link>https://trid.trb.org/View/2173307</link>
      <description><![CDATA[As the number of UAVs (Unmanned Aerial Vehicles) and the market size have been expanding rapidly in recent years, projects such as NextGen and SESAR aim to include UAVs in air traffic. Therefore, different perspectives on understanding flight patterns can contribute to more effective management of future air traffic. Analysis of flight data offers an important insight into the operations of a UAV. In this study, it is aimed to extract a flight fingerprint using different machine learning techniques by means of a public dataset and the data obtained from our experimental flights. To get the individual flight pattern, multidimensional UAV sensor data has been reduced using manifold learning methods. By comparison, the most proper manifold method that allows highest classification accuracy (CA) has been investigated. Their performances are compared using both different manifold types and different classification methods. Then, the obtained manifold is used as flight fingerprints and validated by classification techniques. Various unsupervised manifold learning techniques such as t-Distributed Stochastic Neighbor Embedding (t-SNE), Locally Linear Embedding (LLE), Isometric Feature Mapping (ISOMAP) were tried for dimension reduction. For flight fingerprint classification, supervised machine learning techniques such as k-Nearest Neighbors (k-NN), Adaboost, Neural Network, Bayes, etc., were tested. It has been observed that the highest classification accuracy is achieved with the t-SNE manifold and k-NN classification pair. The extracted fingerprint can find many application areas such as performance tests in production lines, air traffic control, risk analysis, anomaly detection, observing pilot performance, drone efficiency over time.]]></description>
      <pubDate>Fri, 22 Sep 2023 09:06:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2173307</guid>
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