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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>
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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>A multi-dimension method for assessing the resilience of air network</title>
      <link>https://trid.trb.org/View/2669914</link>
      <description><![CDATA[Air network (AN) is crucial to global economy, but the frequent occurrence of extreme weather events has highlighted the urgent need to enhance its resilience. Previous studies have explored the resilience of AN from a single dimension. Differently, we propose a novel assessment framework that systematically integrates both structural and functional dimensions for evaluating AN resilience. First, we model an AN and then analyze its structural and functional characteristics. Second, the multi-dimension resilience of AN is evaluated by five metrics. Finally, we investigate the patterns of structural and functional changes within AN and identify critical airports. The obtained results reveal that the attacks based on closeness centrality can cause the most severe damage to the AN. Additionally, the busy airports such as Beijing and Shanghai airports should optimize operational efficiency, while the peripheral airports can serve as emergency backup nodes within AN.]]></description>
      <pubDate>Thu, 07 May 2026 09:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669914</guid>
    </item>
    <item>
      <title>Service quality entangled dependencies in brand advocacy: A copula-augmented interpretable neural network approach</title>
      <link>https://trid.trb.org/View/2666939</link>
      <description><![CDATA[In the hyper-competitive global aviation sector, understanding the intricate drivers of brand advocacy is paramount for sustained profitability. Traditional analytical models, however, often fail to capture the complex, non-linear dependencies and non-normal distributions inherent in customer service quality data, leading to incomplete or misleading insights. This study introduces and validates a novel methodological framework, the Copula-Augmented Interpretable Neural Network (CAINN), to overcome these limitations. Analyzing 14,893 customer reviews for 12 major European airlines from Skytrax, the CAINN model integrates the deep dependency-modeling capabilities of copula functions with the predictive power of neural networks. The results demonstrate the proposed framework's profound superiority over conventional benchmarks (Logistic Regression and standard MLP), with the Student's T copula variant achieving an accuracy of 0.922 and an AUC of 0.973. Interpretable machine learning techniques (SHAP, LIME, ALE) reveal that Perceived Value for Money is the single most powerful determinant of brand referral, dwarfing the impact of other service attributes. The findings underscore the critical importance of modeling tail dependencies to account for extreme customer experiences. This research provides a dual contribution: it advances a robust new methodology for analyzing complex consumer behavior and delivers an actionable strategic directive for airlines to enhance brand equity by focusing on the pivotal role of value perception.]]></description>
      <pubDate>Thu, 07 May 2026 09:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666939</guid>
    </item>
    <item>
      <title>Beyond route-specific forecasting: An empirical test of two cross-series transfer learning strategies for airline demand with short-data constraints</title>
      <link>https://trid.trb.org/View/2666911</link>
      <description><![CDATA[Airline demand forecasting frequently faces a “short-data” problem, where individual routes have limited historical records due to new route launches, seasonal suspensions, or external shocks such as COVID-19. While this constraint impedes the performance of forecasting models trained on single-series data, the presence of numerous parallel routes—a typical characteristic of airline networks—presents a “short-but-wide” data structure, offering a clear opportunity for cross-series transfer learning.To exploit this opportunity, we propose and empirically validate two competing strategies: a feature-rich Fine-Tuned Global Forecasting Model (FT-GFM) and an adaptation-focused Adapted Model-Agnostic Meta-Learning (Adapted-MAML).Our analysis uses 28 months of data for 1203 origin–destination pairs arriving at three major U.S. hubs (ATL, DFW, DEN). The feature-rich FT-GFM improved accuracy substantially, reducing SMAPE by an average of 28.45% and outperforming local models on 86.74% of routes. The data-efficient Adapted-MAML achieved even greater gains, reducing SMAPE by 45.88% and outperforming local models on 93.81% of routes, despite using only historical passenger volumes.The results validate both strategies as effective solutions and show that, in data-constrained environments, Adapted-MAML's meta-learned initialization yields superior route-level forecasting accuracy compared with FT-GFM's feature-driven approach. These findings provide actionable guidance for airlines on selecting appropriate cross-series transfer learning strategies to mitigate the short-data problem and enhance operational resilience under uncertainty.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666911</guid>
    </item>
    <item>
      <title>Implementation of Karolinska sleepiness scale to improve pilots awareness and confidence in fatigue reporting</title>
      <link>https://trid.trb.org/View/2666901</link>
      <description><![CDATA[Fatigue among pilots in short-haul, multi-sector night freight operations remains a critical challenge to aviation safety. Irregular schedules and prolonged exposure to the Window of Circadian Low (WOCL) cause cognitive impairments, slower reaction times, and reduced situational awareness. Despite regulatory frameworks such as Fatigue Risk Management Systems (FRMS), cultural and organisational barriers, including fear of punitive action and lack of standardised tools, continue to limit effective fatigue reporting. This study investigates the use of the Karolinska Sleepiness Scale (KSS) as a subjective fatigue management tool in short-haul night freight operations. A mixed-methods approach was applied, combining a six-week survey with semi-structured interviews. The study provides the first systematic evaluation of the KSS not only as a fatigue measurement instrument but also as a communication tool that bridges the gap between pilot experience and FRMS reporting practices in short-haul night freight operations. Pilots used the KSS during operational duties to assess fatigue levels and evaluate its influence on awareness and reporting practices. Findings indicate that the KSS improves pilots’ ability to recognise and communicate fatigue, supports proactive workload management, and fosters collaboration within crews. However, organisational gaps remain, including the absence of integration into Standard Operating Procedures (SOPs), lack of structured training, and hesitancy among less experienced pilots without formal company endorsement. The study recommends the formal adoption of the KSS into SOPs, supported by recurrent training and feedback mechanisms, together with integration of objective measures such as biometrics. Results highlight the potential of the KSS as a cornerstone of fatigue risk management and emphasise the importance of supportive safety cultures in prioritising pilot well-being as a key component of operational safety.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666901</guid>
    </item>
    <item>
      <title>Time-series risk forecasting of airport conflict hotspot with SA-GRU</title>
      <link>https://trid.trb.org/View/2663678</link>
      <description><![CDATA[Airport surface operations increasingly confront collision risks from intricate layouts, vehicle-aircraft interactions, and dense mixed traffic flows. This study develops a predictive framework for conflict hotspot identification by integrating topological, network vulnerability, and traffic complexity metrics into a composite risk evaluation system. A hybrid method combining composite weighting and improved TOPSIS first identifies latent hotspots through node-level risk assessments. Temporal risk patterns are then extracted via principal component analysis of hotspot features, with future risk trajectories predicted using a GRU network enhanced by self-attention mechanisms. Validated through Shenzhen Bao'an International Airport simulations, the proposed SA-GRU model reduces RMSE by 9.14–11.55 % against benchmark models (HA/ARIMA/SVR/LSTM/GRU). Analysis reveals significant spatiotemporal variations in hotspot risks, where daily trends show similar risk fluctuation patterns across zones but differ substantially in intensity. High-risk areas dynamically shift across operational phases, emphasizing the necessity of time-sensitive predictions. The framework enables proactive identification of critical conflict zones through predictive risk monitoring, demonstrating practical potential for optimizing airport surface management. By translating multidimensional operational data into actionable safety insights, this methodology supports intelligent decision-making for collision prevention and resource allocation in complex aviation environments, while remaining adaptable to diverse airport configurations.]]></description>
      <pubDate>Fri, 24 Apr 2026 08:55:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663678</guid>
    </item>
    <item>
      <title>Award travel in U.S. domestic markets: An analysis of free ticket redemptions</title>
      <link>https://trid.trb.org/View/2663672</link>
      <description><![CDATA[Frequent flyer programs (FFPs) are central to airline strategy, yet little empirical work examines the routes on which free award tickets are redeemed. Using U.S. Department of Transportation Airline Origin and Destination Survey (DB1B) data accessed through Cirium's Diio Mi, this study analyzes general levels of free award travel in the U.S. domestic market from 2000 to 2025. Next, it provides a detailed cross-sectional assessment of free domestic award travel in 2024. Longitudinal results show redemption levels shifting at key industry moments but diverging sharply by carrier over time. Cross-sectional OLS analysis confirms that carrier identity is the strongest predictor of domestic free ticket redemptions, with Southwest exhibiting the highest levels, United the lowest, and Delta rising steadily. Routes with heavy imbalances in passenger origin have significantly higher award shares than those with more balanced routes. Seasonality, market competition, and carrier yield add nuance to the analysis. Our results show that award travel exhibits clear, measurable patterns within domestic markets, with implications for revenue management, competition, and regulatory oversight.]]></description>
      <pubDate>Fri, 24 Apr 2026 08:55:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663672</guid>
    </item>
    <item>
      <title>Airline industry transformation: Does cost efficiency reflect business model convergence?</title>
      <link>https://trid.trb.org/View/2659413</link>
      <description><![CDATA[This study investigates the convergence of cost efficiency among Full-Service Airlines (FSAs) and Low-Cost Carriers (LCCs), focusing on whether alignment in operational strategies extends to both persistent and transient cost efficiency. While evolving economic pressures such as fluctuating fuel prices, labor costs, and market deregulation have encouraged convergence in airline business models, the effects on cost efficiency remain uncertain. Using data from U.S. airlines spanning 2000 to 2019 and applying a four-component stochastic frontier analysis, our findings indicate convergence in transient cost efficiency, reflecting short-term operational adjustments, but not in persistent cost efficiency, where FSAs consistently outperform LCCs. This structural advantage provides FSAs with greater long-term cost discipline and strategic flexibility. Analysis of inefficiency determinants further shows that network structure plays a dominant role in shaping persistent inefficiency, i.e., denser networks increase long-run inefficiency, whereas greater reliance on nonstop flights enhances structural efficiency. On the transient side, capacity utilization significantly reduces short-run inefficiency, while higher ancillary revenue share is associated with increased short-run inefficiency. These findings highlight that convergence across business models is limited to short-run operational responses; meaningful convergence in long-run cost efficiency would require fundamental and sustained changes to network architecture and business model design.]]></description>
      <pubDate>Tue, 21 Apr 2026 08:28:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659413</guid>
    </item>
    <item>
      <title>Sustainability and financial performance: How efficiency mediates the ESG-financial performance relationship in the Airline industry</title>
      <link>https://trid.trb.org/View/2659381</link>
      <description><![CDATA[The growing emphasis on environmental, social, and governance (ESG) performance has raised critical questions about its integration into business strategy and its implications for firm performance. Despite the increasing prevalence of ESG metrics in corporate reporting, empirical evidence linking ESG adoption to strategic value remains limited. This study investigates whether and how ESG performance enhances business operational and financial performance in the U.S. airline industry. Leveraging panel data from major U.S. airlines between 2006 and 2022, we employ Data Envelopment Analysis (DEA) and fixed-effects panel regression to assess the relationship between ESG performance, operational efficiency, and financial performance. Our findings reveal that improvements in ESG performance lead to higher efficiency and better financial results, with efficiency playing a mediating role. Additionally, among the ESG dimensions, enhancements in environmental and governance practices exert a stronger influence on efficiency than social initiatives. This research highlights the strategic importance of ESG integration, offering actionable insights for managers and policymakers aiming to foster long-term value through sustainable business practices. By bridging the gap between ESG performance and business outcome, this study contributes to a deeper understanding of how sustainability can be leveraged as a source of competitive advantage.]]></description>
      <pubDate>Tue, 21 Apr 2026 08:28:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659381</guid>
    </item>
    <item>
      <title>Efficiency of Peruvian regional airports: Does the PPP framework make a difference?</title>
      <link>https://trid.trb.org/View/2655996</link>
      <description><![CDATA[This paper examines whether Public-Private Partnership (PPP) frameworks have improved the technical efficiency of Peruvian regional airports. Using panel data from 17 airports between 2004 and 2017, we estimate a Latent Class Stochastic Frontier Model (LCSFM) based on an input-oriented Cobb-Douglas distance function, which is explicitly controlling for unobserved technological heterogeneity. Airports are classified into two technological groups, with concessionaire status (AdP) serving as a separating variable. Results reveal that airports managed under PPP schemes achieve higher efficiency levels and are more likely to operate with superior technology, with both groups exhibiting increasing returns to scale. Evidence of technological progress, biased towards operational expenditures, suggests that efficiency gains have been driven by labour innovations, outsourcing, and digitalization. These findings highlight the positive role of PPPs in fostering operational improvements and underline the risks of ignoring heterogeneity in regulatory benchmarking. We argue that incorporating heterogeneity-adjusted models into regulatory frameworks could strengthen incentive-based policies, guide infrastructure investments more effectively, and support sustainable development of regional air transport networks.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655996</guid>
    </item>
    <item>
      <title>Resilience in the skies: Modeling domestic and international air passenger demand in multiple Indian airports</title>
      <link>https://trid.trb.org/View/2655987</link>
      <description><![CDATA[This study presents a comparative analysis of univariate time-series (ARIMA, SARIMA, ETS) and deep learning models (RNN and LSTM) for forecasting post-COVID domestic and international air passenger demand at eight major Indian airports: Ahmedabad, Bengaluru, Mumbai, Kolkata, Delhi, Hyderabad, Chennai, and Pune. Utilizing quarterly data from 2016 to 2023, performance of time-series and deep learning models is evaluated against actual 2024 air traffic data using MAPE, MAE, and RMSE indices. Results demonstrate that model efficacy is highly context-specific. SARIMA consistently outperforms ARIMA in capturing seasonality, while LSTM excels at modeling non-linear complexities, and ETS proves robust for airports with clear trends. Crucially, a SARIMAX model integrating exogenous drivers, including net domestic product, network connectivity, and operational metrics, significantly enhanced forecasting accuracy, particularly for international travel, underscoring the importance of these drivers. The coefficients reveal several interesting policy scenarios, such as enhancing domestic and international connectivity, particularly at emerging hubs, stimulates passenger growth, while densely populated catchments require investments in multimodal integration to counter negative demand. The findings challenge the presumption of a universal forecasting framework and underscore the inefficiency of relying solely on univariate models, advocating for a tailored approach that incorporates key exogenous variables for resilient air traffic management.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655987</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>Evolution of China’s intercontinental air network affected by the COVID-19 pandemic and geopolitical disruptions</title>
      <link>https://trid.trb.org/View/2652350</link>
      <description><![CDATA[This study investigates the heterogeneous responses of Chinese and foreign airlines to the compound disruptions from the COVID-19 pandemic and geopolitical tensions, focusing on the restructuring of China’s international direct air transport network. Utilizing airline-route-level data from 2019 to 2024, the research employs a probit model to analyse route service decisions in the China–North America and China–Europe markets. The empirical findings reveal divergent recovery patterns shaped by distinct underlying drivers. In the China–Europe market, Chinese airlines, benefiting from sustained access to Russian airspace, assumed a dominant role, whereas foreign airlines faced a slower recovery constrained by costly operational detours. In contrast, the recovery in the China–North America market remained suppressed for all carriers due to persistent geopolitical tensions. Strategically, foreign airlines became more likely to serve more competitive routes and to avoid routes already operated by alliance partners, compared to the pre-disruption period. Conversely, Chinese airlines exhibited a greater likelihood of serving less competitive routes and routes already operated by other alliance members. These findings underscore how asymmetric operational constraints and geopolitical factors reshape aviation networks through carrier-specific strategies, offering critical insights for policymakers and managers.]]></description>
      <pubDate>Thu, 26 Mar 2026 16:59:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652350</guid>
    </item>
    <item>
      <title>Promoting transition towards sustainable air transport systems: A hybrid decision support system for effective national-level performance evaluation</title>
      <link>https://trid.trb.org/View/2652343</link>
      <description><![CDATA[Air transport plays a pivotal role in enhancing economic development by supporting trade, tourism, and regional competitiveness. The growing environmental concerns and social expectations have necessitated the transition towards sustainable air transport systems. Sustainable air transport refers to aviation activities that balance environmental, economic, and social objectives, aiming to minimize carbon emissions, promote renewable energy usage, and enhance socio-economic welfare. In this study, a novel multi-criteria decision-making (MCDM)-based decision support system (DSS) is proposed to evaluate the sustainable air transport performance of the European countries. The main objective of this research is to develop a comprehensive and integrative framework for measuring and ranking the sustainable air transport performance of nations. A hybrid method, termed fractional fuzzy–ranking comparison-response to criteria weighting (RANCOM)–response to criteria weighting (RECA)–ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS), is introduced. DSS consists of five main stages: expert-based subjective weighting using fractional fuzzy RANCOM, objective weighting via RECA, aggregation of weights, and final performance ranking through the RATGOS method. The results indicate that Germany ranks highest, while Cyprus has the lowest sustainable air transport performance among the evaluated countries. The criterion "commercial aircraft fleet by age of aircraft" is determined to have the highest importance among the sustainable air transport performance indicators. The study provides a comprehensive, replicable framework for policymakers and stakeholders aiming to monitor and improve sustainable aviation systems. It contributes to the literature by addressing the gap in national-level sustainable air transport performance evaluation.]]></description>
      <pubDate>Thu, 26 Mar 2026 16:59:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652343</guid>
    </item>
    <item>
      <title>Transit oriented development under the influence of urban air mobility: A public transit-based vertiport siting method</title>
      <link>https://trid.trb.org/View/2647513</link>
      <description><![CDATA[The emergence of electric vertical takeoff and landing (eVTOL) technology has enabled large-scale implementation of urban air mobility (UAM) within cities. This study proposes a vertiport-siting optimization method based on public transit stations to better utilize existing urban resources for UAM development. The study also maximizes the combined strengths of UAM's flexibility and efficiency with the high-capacity characteristics of traditional transit systems, addressing future mobility demands that feature both decentralization and urban agglomeration. The proposed method minimizes travel costs between public transit stations and vertiport locations while incorporating population density as a weighting factor to enhance the objective function. Two siting strategies were compared regarding travel costs and spatial service coverage. Using Shenzhen's Bao'an District as a case study, results indicate that the population-weighted optimization approach reduces distance-based travel costs by 12.6 % and improves coverage in core functional zones by approximately 20 %, significantly enhancing spatial service efficiency. This study diverges from traditional infrastructure-led siting approaches by emphasizing the foundational role of public transit systems in shaping UAM networks. It introduces a three-dimensional urban planning concept that integrates transit-oriented development (TOD) with UAM, offering a new pathway for reshaping urban spatial structures and improving spatial efficiency in future cities.]]></description>
      <pubDate>Fri, 20 Mar 2026 17:00:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647513</guid>
    </item>
    <item>
      <title>An improved optimization algorithm for solving arrival aircraft scheduling problem in the Terminal Maneuvering Area</title>
      <link>https://trid.trb.org/View/2646886</link>
      <description><![CDATA[The Terminal Maneuvering Area (TMA) is one of the most complex and congested airspace segments, where tools like the Arrival Manager (AMAN) are used to manage inbound traffic and provide accurate, efficient scheduling for each flight. The associated optimization problem is NP-hard, requiring advanced algorithms to meet performance demands in both computational time and solution quality. Heuristic algorithms, such as Simulated Annealing (SA), are known for their ability to provide fast, near-optimal solutions in large and complex state spaces. In our previous work, simulation-based optimization using SA was employed, where information of all flights was integrated into each simulation, resulting in a computationally intensive evaluation process. In this study, we propose a more efficient method by leveraging the inherent safety dependencies between neighboring flights in the operation.By focusing on the performance of individual flights and their immediate impact on adjacent flights, the optimization process becomes more targeted, eliminating the need to integrate all flight data at once. This improves both efficiency and flexibility. To demonstrate the advantages of a selective structure in Simulated Annealing, we introduce Selective Simulated Annealing (SSA) and compare it to the Standard Simulated Annealing algorithm (OSA), highlighting their distinct features. A case study at Paris-Charles de Gaulle (CDG) Airport is used to analyze the performance of both algorithms. Key parameter adjustments are examined to gain insights into their optimization behaviors. The comparison reveals that SSA significantly outperforms OSA, delivering faster computation and reducing delays by 50%.]]></description>
      <pubDate>Fri, 20 Mar 2026 17:00:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646886</guid>
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