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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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    <item>
      <title>From voice to safety: Language AI powered pilot-ATC communication understanding for airport surface movement collision risk assessment</title>
      <link>https://trid.trb.org/View/2663713</link>
      <description><![CDATA[Surface movement collision risk is critical for airport safety. These models play a vital role in identifying and mitigating potential hazards during airport ground operations by providing warnings of near-miss incidents, thereby reducing the risk of accidents that could jeopardize human lives and financial assets. However, existing models, developed decades ago, have not fully integrated recent advancements in machine intelligence, where incorporating additional functionalities presents promising opportunities for improved risk assessment. This work provides a feasible solution to the existing airport surface safety monitoring capabilities (i.e., Airport Surface Surveillance Capability (ASSC)), namely language AI-based voice communication understanding for collision risk assessment. The proposed framework consists of two major parts, (a) rule-enhanced Named Entity Recognition (NER); (b) surface collision risk modeling. NER module generates information tables by processing voice communication transcripts, which serve as references for producing potential taxi plans and calculating the surface movement collision risk. We first collect and annotate our dataset based on open-sourced video recordings and safety investigation reports. Additionally, we refer to FAA Order JO 7110.65W and FAA Order JO 7340.2N to get the list of heuristic rules and phase contractions of communication between the pilot and the Air Traffic Controller (ATCo). Then, we propose the novel ATC Rule-Enhanced NER method, which integrates the heuristic rules into the model training and inference stages, resulting in a hybrid rule-based NER model. We show the effectiveness of this hybrid approach by comparing different setups with different token-level embedding models. For the risk modeling, we adopt the node-link airport layout graph from NASA FACET and model the aircraft taxi speed at each link as a log-normal distribution and derive the total taxi time distribution. Then, we propose a spatiotemporal formulation of the risk probability of two aircraft moving across potential collision nodes during ground movement. Furthermore, we propose the real-time implementation of such a method to obtain the lead time, with a comparison with a Petri-Net based method. We show the effectiveness of our approach through case studies, (a) the Haneda airport runway collision accident happened in January 2024; (b) the KATL taxiway collision happened in September 2024; (c) the Tenerife airport disaster in March 1977. We show that, by understanding the pilot-ATC communication transcripts and analyzing surface movement patterns, the proposed model estimates the surface movement collision probability within machine processing time, thus enabling proactive measures to possible collisions at a certain node, which improves airport safety. A study on validating the log-normal assumption of aircraft taxi speed distributions is also given. We provide the link to code and data repository HERE.]]></description>
      <pubDate>Thu, 14 May 2026 17:04:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663713</guid>
    </item>
    <item>
      <title>Resilient multi-agent reinforcement learning for centralised tactical conflict resolution under uncertain perturbations and non-cooperative traffic in urban air mobility</title>
      <link>https://trid.trb.org/View/2663711</link>
      <description><![CDATA[This research investigates tactical conflict resolution for Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM) operations under degraded conditions and in the presence of non-cooperative UAS/UAM and manned Commercial Air Transportation and General Aviation (CAT/GA) intruders. The study adopts a centralised safety-net approach within UAS Traffic Management (UTM) architectures, envisioning ground-based conflict resolution services. We propose a set of Tactical Conflict Resolution Solvers (TCRS), each built upon a Multi-Agent Reinforcement Learning (MARL) core using a shared-policy transformer architecture and executed in a decentralised manner. To assess resilience of TCRS variants, we introduce domain-specific perturbations, including positioning noise, communication loss, and sensor-related defects. The TCRS operates with partial decision-making ability in non-cooperative traffic environments, while the perturbation model increases realism by simulating varying degrees of information availability. Results show that the perturbation-trained models achieve substantial safety gains compared with the baseline TCRS trained in ideal conditions. The most resilient variant; trained under multi-perturbation exposure and evaluated in non-cooperative environments, achieves a threefold reduction in critical safety violations compared with the baseline and remains robust under mixed cooperative/non-cooperative traffic with static intent. It exhibits a modest vulnerability under fully homogeneous non-cooperative scenarios with dynamic intent. Simulations involving concurrent CAT/GA and UAS operations further indicate that integrating UAS operations within the existing airspace classification remains hazardous for ground-based tactical conflict resolution when constrained by short look-ahead horizons and insufficient time to react.]]></description>
      <pubDate>Thu, 14 May 2026 17:04:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663711</guid>
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    <item>
      <title>Proposition of an Ontology Supporting Context Modeling in Operational Scenario Development</title>
      <link>https://trid.trb.org/View/2663576</link>
      <description><![CDATA[Air traffic control systems are complex sociotechnical systems that do not comprise solely technical elements but also have human and organizational dimensions. Recent approaches to systems engineering, and in particular human systems integration, are placing increasing emphasis on incorporating these nontechnical considerations into the design processes of complex sociotechnical systems. In particular, one crucial aspect in design is to understand the operational context of the system. There is a lack of tools and languages expressive enough to include contextual information into system models. This is especially true for the air traffic control field, as the operational context of a control tower and the tower itself are deeply intertwined and interdependent. This paper is a step toward the achievement of better integration of context-related knowledge into system design processes. We conducted a case study analysis based on a literature review and feedback from civilian and military air traffic control practitioners, and we propose an ontology of the contextual elements that characterize the context of air traffic control operations.]]></description>
      <pubDate>Wed, 06 May 2026 08:54:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663576</guid>
    </item>
    <item>
      <title>Use of Trajectory Option Sets to Support Collaborative Constraint Propagation</title>
      <link>https://trid.trb.org/View/2680964</link>
      <description><![CDATA[Air traffic flow management is supported by a highly distributed work system in which airline dispatchers and Federal Aviation Administration (FAA) traffic managers must coordinate. To support asynchronous coordination between a dispatcher and a traffic manager, the FAA has developed software that allows the flight operators to submit multiple, prioritized alternative flight plans. This set of alternative flight plans, submitted along with a filed route, is referred to as a Trajectory Option Set (TOS). And some airlines have now developed initial versions of software capable of generating and submitting such TOSs. This paper reports on cognitive walkthroughs with 5 dispatchers and 3 traffic managers on 5 scenarios designed to evaluate the operational concept, procedures and supporting FAA and airline software. The findings provide guidance for application of the concept of collaborative constraint propagation to support distributed work, as well as 42 recommendations for enhancing associated procedures and supporting software designs.]]></description>
      <pubDate>Sat, 02 May 2026 15:47:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680964</guid>
    </item>
    <item>
      <title>Remote Tower Air Traffic Controller Multimodal Fatigue Detection</title>
      <link>https://trid.trb.org/View/2691613</link>
      <description><![CDATA[Remote tower (rTWR) operations are reshaping air traffic control but introduce significant human-factor risks, notably cognitive fatigue induced by prolonged screen-based visual surveillance. To mitigate these risks in a safety-critical domain where missed detections can be catastrophic, we propose a non-intrusive, multimodal fatigue detection framework fusing ocular and cardiac signals. A high-fidelity simulation study with 36 controllers was conducted to collect eye-tracking and electrocardiogram (ECG) data, from which a 12-dimensional feature vector-integrating gaze entropy and heart rate variability (HRV)-was extracted. Addressing the severe class imbalance and scarcity of fatigue samples in physiological data, we developed a cost-sensitive XGBoost classifier combining SMOTE oversampling with a dynamically weighted loss function. Experimental results show that the proposed framework performed well under mixed-subject evaluation and improved sensitivity to fatigue events. Although a marked performance drop was observed under LOSO evaluation, personalized calibration partially alleviated this limitation, indicating the potential of the framework for real-time fatigue monitoring in remote tower operations.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691613</guid>
    </item>
    <item>
      <title>Two-Stage Scheduling Optimization Model for Taxiways on the Basis of Time Slot Derivation</title>
      <link>https://trid.trb.org/View/2691597</link>
      <description><![CDATA[The continuous growth in air traffic at civil airports has placed significant economic pressure on surface operations. Consequently, strategic adjustments to departure schedules and optimization of taxiing routes have become essential to reduce operational costs. This study proposes a two-stage optimization framework aimed at minimizing surface operation expenditures. In the first stage, a dynamic pushback slot control (DPSC) strategy is employed to regulate departure sequences. The second stage enables preplanning of taxi routes for both arriving and departing aircraft by optimizing the taxiway control threshold, thereby refining the pushback slots for departing flights. To support route planning, multiple taxiing configurations are generated for different departure intervals. To improve solution quality and mitigate ground conflicts, an improved ant colony algorithm (IACA) incorporating a negative feedback mechanism is developed. Experimental results show that, compared to a baseline scenario without departure control, the proposed framework reduces taxiing costs by 17.8%, yielding an optimized total cost of USD 8,163.44. Furthermore, relative to strategies without the negative feedback mechanism, the proposed approach achieves an average cost saving of USD 1,412.71. These results demonstrate that the proposed framework provides superior economic benefits while simultaneously improving operational safety and efficiency.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691597</guid>
    </item>
    <item>
      <title>U-Aerodrome: Data-driven and risk-bounded airspace reconfiguration for safe integration of urban air mobility at aerodrome</title>
      <link>https://trid.trb.org/View/2652742</link>
      <description><![CDATA[Urban Air Mobility (UAM) offers promising solutions for alleviating urban congestion and enabling seamless air transportation. However, its integration near aerodromes is limited by static no-fly zones and traditional airspace management practices. Existing boundary-setting methods often depend on oversimplified assumptions about trajectory distributions or apply rigid spatial constraints, which can lead to safety risks and inefficient airspace utilization. To address these limitations, this study introduces U-Aerodrome, a data-driven and risk-bounded airspace reconfiguration framework designed to support the safe and flexible integration of UAM operations near controlled aerodromes. The approach employs procedure-based trajectory classification and equal-altitude sampling to ensure equitable and non-biased representation of flight patterns. It further incorporates probabilistic boundary estimation that accommodates both Gaussian and non-Gaussian distributions, as well as a time-dependent boundary update mechanism responsive to dynamic traffic demand. The framework is validated using real-world data collected from Singapore Changi Airport. Results show that U-Aerodrome reduces missed detections and conservative volume compared to a purely Gaussian baseline, yielding 30.95 % average safety improvement and 15.25 % higher availability. The time-dependent mechanism further reduces unnecessary restrictions by an additional 20.02 % on average compared with baselines assuming static boundaries. The framework supports flexible and statistically grounded planning for safe UAM access near aerodromes.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652742</guid>
    </item>
    <item>
      <title>Overcoming computational challenges in air transportation: A quantum computing perspective of the status quo and future applicability</title>
      <link>https://trid.trb.org/View/2647669</link>
      <description><![CDATA[Recent research breakthroughs in quantum computing, such as Microsoft’s topological qubits, hold the promise of revolutionizing complex optimization problems, particularly in the air transportation industry. This study aims to estimate the mid-term scalability of quantum computing in air transportation, focusing on prevalent optimization problems including network design, airline scheduling, and gate assignment. These problems are computationally intensive and often intractable for classical computers due to their highly combinatorial nature. We develop a framework to assess the potential scalability of quantum algorithms for these problems, considering factors such as qubit count and error rates. Our findings suggest that significant advancements in quantum hardware and algorithms are necessary before quantum computing can outperform classical methods in this domain. Therefore, while quantum computing offers a promising tool for solving complex optimization problems in air transportation, its real-world application remains a distant goal. We believe that our work helps guiding researchers and industry professionals in their pursuit of quantum-enhanced air transport solutions.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647669</guid>
    </item>
    <item>
      <title>Prioritizing Safety-Critical Information in the National Airspace System: A Four-Phased Human Factors Methodology and Its Future Applications</title>
      <link>https://trid.trb.org/View/2689428</link>
      <description><![CDATA[This report describes a methodology for sorting and prioritizing safety-critical aeronautical information, developed in response to a National Transportation Safety Board recommendation. The methodology integrates human factors and risk assessment principles to identify systemic vulnerabilities in information management and to align information delivery with operational, cognitive, and contextual demands. The approach applies the bow-tie risk model to represent human factors constructs as threats that weaken preventive barriers. Information characteristics, including volume, relevance, timeliness, and modality, are modeled as drivers of these threats and are explicitly linked to preventive and mitigative controls. The resulting framework supports operationally realistic filtering, sequencing, and delivery strategies. The methodology is executed in phased activities, including expert knowledge elicitation, scenario-based simulation, and development and validation of a decision support tool. Certified professional controllers and other operational roles complete realistic scenarios varying in complexity, traffic load, and environmental conditions. Data include event-linked performance metrics, post-scenario interviews, and standardized measures of workload, Situation Awareness, and trust. Integrated quantitative and qualitative analyses identify patterns in information use, decision making, and operational outcomes. Outputs include evidence-based recommendations for training, interface design, and policy and procedural improvements to reduce operational risk and support resilient operations. The scope is limited to the contiguous United States, with future research recommended for non-contiguous regions.]]></description>
      <pubDate>Thu, 16 Apr 2026 16:54:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689428</guid>
    </item>
    <item>
      <title>Signal Design Handbook: A New Framework for Designing Alarms, Alerts, and Warnings in Air Traffic Control</title>
      <link>https://trid.trb.org/View/2685589</link>
      <description><![CDATA[The Federal Aviation Administration’s air traffic control organization (ATO) encompasses a variety of facilities that include towers, terminal radar approach control facilities (TRACONs), and air route traffic control centers (ARTCCs). Air traffic controllers use alarms, alerts, and warnings (collectively, signals) as an aid to situation awareness and to reduce cognitive workload. We have written a handbook that offers guidance for the creation or modification of air traffic control system alarms, alerts, and warnings using a process that encourages collaboration between controllers, system designers and human factors experts. The novel Signal Design Framework described in this handbook uses a structured interview with operational subject matter experts (i.e., air traffic controllers) and an objective scoring sheet to develop a series of specifications that can be used to modify an existing signal or design a new signal. This framework provides a common language that subject matter experts, system designers, and engineers can use to describe, classify, and objectively evaluate signals. We tested and validated the Signal Design Framework and its associated structured interview during Phase 4 when we developed a new microburst alarm and a trajectory conformance alarm. This project provides the ATO with the tools to develop signals to keep the United States’ National Airspace System the safest in the world.]]></description>
      <pubDate>Thu, 09 Apr 2026 13:41:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685589</guid>
    </item>
    <item>
      <title>Early Warnings and Envelope Adjustment-Based Safety Flight Control With Application to Hypersonic Vehicles</title>
      <link>https://trid.trb.org/View/2591334</link>
      <description><![CDATA[This article is dedicated to safety flight control of hypersonic vehicles that serve as long-range strategic transport aircraft, with the aim of alleviating the inherent fragility defect in existing prescribed performance control (PPC) schemes. In pursuit of this goal, we first establish a safety boundary that offers early warnings for fluctuations in hypersonic tracking errors and serves as a crucial mechanism for envelope adjustment. Building on this groundwork, we further develop a universal sensing-adjustment system with dynamically activated states triggered by the defined safety boundary, enabling active readjustment of the prescribed envelope boundaries. This results in a new PPC scheme capable of promptly detecting error fluctuations and smoothly readjusting prescribed envelopes, effectively addressing the fragility defect associated with existing protocols while ensuring hypersonic flight safety. Moreover, our proposed hypersonic controller does not necessitate approximators or learning parameters required for current fuzzy/neural control approaches, showcasing a low-computational design framework. Finally, we assess the efficiency of our approach by conducting comparative simulations.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591334</guid>
    </item>
    <item>
      <title>A two-stage stochastic optimization approach for mega-airport departure metering under data-driven taxi-time uncertainty predictions</title>
      <link>https://trid.trb.org/View/2655841</link>
      <description><![CDATA[Over the past decade, mega-airports have experienced a surge in air traffic demand, physical expansion, and increased complexity in apron layouts, leading to a high level of aircraft taxi-time uncertainty and shifting the airport surface management from integrated tower control to dedicated apron control. In this study, a two-stage stochastic optimization framework is developed for mega-airport departure metering (DM), which specializes apron-centric and tower-centric optimization in different stages. Moreover, a data-driven Mixture Density Network (MDN) is built to predict the aircraft taxi-time distribution and characterize the uncertainty levels. A large-scale trajectory dataset is collected from a representative mega-airport in China to illustrate the procedure. The results indicate that the developed two-stage stochastic optimization framework distinguishes tower control and apron control in the DM process, improving the overall flexibility of airport airside operations. The data-driven neural network could better predict the taxi-time uncertainty levels through multimodal probability distributions especially at mega-airport with volatile traffic situations. Furthermore, compared with state-of-the-art DM methods, the two-stage stochastic optimization framework could achieve more robust performance of airport departure management and better trade-off between gate-holding and runway throughput.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:15:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655841</guid>
    </item>
    <item>
      <title>TiI: A Novel Framework for Predicting Pre-Tactical Multiple Airports Traffic Flows by Integrating Flight Plans and Weather Data</title>
      <link>https://trid.trb.org/View/2610627</link>
      <description><![CDATA[The accurate prediction of traffic flows, defined as the aggregate forecasting of combined departure and arrival volumes across multiple airports within a designated air traffic control zone during the pre-tactical phase, is crucial for effective air traffic flow management, particularly given the rapid growth of aviation transportation. However, existing methods face significant challenges in modeling the complex spatial-temporal dependencies due to difficulties in constructing appropriate graph structures, as well as limitations inherent in the algorithms and periodic trend-based prediction mechanisms. To address these challenges, a novel framework termed Transformation into Image (TiI) is proposed in this study. Initially, data from multiple airports are transformed into images, effectively compressing spatial-temporal features into grid-based distribution representations. Subsequently, the proposed multi-kernel convolution module and parallel patch-transformer module are integrated into each resolution stream of the U-Net architecture to accurately capture spatial-temporal dependencies at both coarse-grained and fine-grained levels. Finally, the predicted images are converted and partitioned into traffic flow data for individual airports, yielding the desired results. To the best of our knowledge, TiI represents the first approach to synchronously extract both spatial and temporal dynamics by encoding traffic flow data into image representation. Extensive experiments validate the feasibility of TiI, demonstrating that it significantly outperforms comparative methods. Furthermore, an ablation study confirms the effectiveness of the two core components within the TiI regression model.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610627</guid>
    </item>
    <item>
      <title>Conceptualizing UAM: Technologies and methods for safe and efficient urban air transportation</title>
      <link>https://trid.trb.org/View/2655665</link>
      <description><![CDATA[The effective integration of Urban Air Mobility (UAM) hinges upon the adoption of a comprehensive approach that harmoniously melds various network components, placing paramount importance on the pillars of safety, sustainability, and efficiency. Existing technologies are currently undergoing a transformative evolution to cater to the distinctive requirements of UAM, with an unwavering commitment to enhancing safety, sustainability, and efficiency. This paper meticulously elucidates the extant technologies and methodologies that pertain to the safe and efficient realm of air transportation while delving into the perspective of key UAM network components: (1) Aircraft classification, range, and operational technology; (2) Airspace typology and structural intricacies; and (3) Air Traffic Management (ATM) services. In conclusion, this paper culminates by offering insights into prospective research directions in this burgeoning field.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:43:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655665</guid>
    </item>
    <item>
      <title>Autonomous Multi-rotor Unmanned Aerial Vehicles for Tactical Coverage</title>
      <link>https://trid.trb.org/View/2581022</link>
      <description><![CDATA[This chapter presents an original guidance system for autonomous multi-rotor unmanned aerial vehicles (UAVs) equipped with forward-facing cameras and tasked with creating maps of unknown environments while operating in a tactical manner and at very low altitudes. The few existing guidance systems for UAVs operating in potentially hazardous environments essentially assume direct information on the location and the kind of potential threat to the aircraft, do not account for the UAV’s dynamics, and usually assumes that the UAV operates at high altitudes. The proposed guidance system, on the contrary, assumes no prior information on the environment and does not rely on external sources of information. Furthermore, to enable operations at low altitudes and in cluttered environments, the proposed guidance system includes a fast trajectory planner. For these features, UAV employing this guidance system can be employed by first responders and other emergency units to collect real-time data about a given location. Several unique features distinguish the proposed guidance system, including an original algorithm to cover connected set, which allows users to prioritize accuracy over flight time, an original algorithm to produce convex constraint sets in real time from voxel maps, and original approaches to induce tactical behaviors both in the optimization-based path planner and the model predictive control-based trajectory planner underlying the proposed guidance system. Numerical simulations validate the applicability and the effectiveness of the proposed guidance system.]]></description>
      <pubDate>Tue, 24 Mar 2026 17:01:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581022</guid>
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