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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 Multicriteria Analytical Framework for Site Selection of Mobility Hubs</title>
      <link>https://trid.trb.org/View/2711633</link>
      <description><![CDATA[Mobility hubs (MHs) have emerged as a novel concept to enhance multimodal travel. A MH provides supporting infrastructure, amenities, and services for multimodal travelers at strategic locations, facilitating seamless integration of various modes. Despite growing interest in MHs, cities and transit agencies lack an established analytical framework for selecting optimal MH locations. To address this gap, we propose a multicriteria approach to identify locations for MH development within an existing or planned transit network. Our approach consists of four steps: 1) determine key criteria and collect data, 2) compute MH index for multiple scenarios, 3) identify MHs and assign typology, and 4) determine MH sites with community engagement. Our approach differs from existing methods by using transit stop clusters (obtained using a density-based clustering algorithm) as the unit of analysis, explicitly incorporating first-/last-mile connectivity as a primary consideration, and distinguishing the typology of each hub. We demonstrate and validate the approach by conducting case studies in Gainesville, Florida, and in West Palm Beach, Florida. In both cities, we have engaged the local transportation agencies and residents, whose inputs verified the desirability of the identified MH locations and confirmed the usefulness of the proposed approach. By combing data-driven analysis with community participation, our approach offers a valuable tool for transportation planners and policymakers in MH planning and development across diverse contexts. We acknowledge several limitations of the proposed approach and emphasize the role of this method as one step in a broader, stakeholder-driven planning process.]]></description>
      <pubDate>Fri, 05 Jun 2026 11:27:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711633</guid>
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    <item>
      <title>Data-Driven Urban Mobility Planning: A GIS-Based Spatial Modeling Approach for Shared Mobility Hub Placement</title>
      <link>https://trid.trb.org/View/2673392</link>
      <description><![CDATA[Strategically planned shared mobility hubs are critical for enhancing multimodal integration, reducing car dependency, and supporting sustainable urban transportation systems. This study proposes a robust GIS-based multi-criteria decision-support framework to identify optimal locations for shared mobility hubs in İzmir, Türkiye. The framework integrates 18 spatial indicators capturing micromobility trip demand, public transportation and cycling accessibility, land-use intensity, urban activity concentration, and traffic safety conditions. To enhance methodological rigor and robustness, two fundamentally different weighting philosophies were implemented: i.) an expert-driven Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), and ii.) a data-driven Entropy-TOPSIS model. The two models produced independent suitability surfaces reflecting contrasting but complementary planning logics. The results indicated that Fuzzy-AHP approach favored locations with established micromobility demand and continuous cycling infrastructure, particularly along the İzmir Gulf coastline and university districts, whereas Entropy-TOPSIS emphasized emerging opportunity areas characterized by heterogeneous public transport accessibility and a high concentration of points of interests (POIs). These outputs were integrated through a novel Integrated Suitability Index (ISI), enabling systematic comparison and reducing method-specific bias. Using an 80% ISI threshold, followed by spatial clustering, 21 high-priority shared mobility hub locations were identified. All selected hubs lie within overlapping high-suitability zones of both models, demonstrating strong cross-model consistency and structural robustness. Overall, the findings confirm that combining expert knowledge with data-driven variability substantially strengthens spatial decision-making for shared mobility hub placement. The proposed framework provides actionable guidance for urban planners and policymakers and offers a scalable decision-support tool for advancing resilient, user-centered, and integrated multimodal transportation systems.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673392</guid>
    </item>
    <item>
      <title>PREVENTing Pilot murder-suicide: A policy-oriented review</title>
      <link>https://trid.trb.org/View/2672555</link>
      <description><![CDATA[Pilot murder-suicides represent a critical and under-addressed challenge at the intersection of human factors, safety management, and intelligent transportation systems. Recent high-profile incidents–such as Mozambique Airlines Flight 470 (2013), Germanwings Flight 9525 (2015), China Eastern Airlines Flight 5735 (2022), and preliminary discussions around Air India Flight 171 (2025)–have underscored the urgent need for evidence-based prevention strategies that align with the evolving landscape of smart and automated aviation. While substantial primary research exists, the field lacks a comprehensive, policy-oriented synthesis that bridges psychological, operational, and technological dimensions. To address this gap, we consolidate the extant literature and introduce the PREVENT framework, a multidimensional, data-driven approach to mitigating pilot murder-suicide risks. The framework spans seven prevention domains, offering actionable insights for integrating mental health support, real-time risk monitoring, and human-centered design into next-generation aviation systems. By synthesizing empirical evidence and case studies, this review provides a roadmap for policymakers, transport authorities, and industry practitioners to enhance safety in intelligent aviation environments. We identify conceptual and empirical gaps to guide future research, emphasizing the need for interdisciplinary collaboration between behavioral science, engineering, and public policy to ensure safe, resilient, and human-focused smart transportation systems.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:02:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672555</guid>
    </item>
    <item>
      <title>MM-STFlowNet: A Transportation Hub-Oriented Multi-Mode Passenger Flow Prediction Method via Spatial-Temporal Dynamic Graph Modeling</title>
      <link>https://trid.trb.org/View/2658887</link>
      <description><![CDATA[Accurate and refined passenger flow prediction is essential for optimizing the collaborative management of multiple collection and distribution modes in large-scale transportation hubs. Traditional methods often focus only on the overall passenger volume, neglecting the interdependence between different modes within the hub. To address this limitation, we propose MM-STFlowNet, a comprehensive multi-mode prediction framework grounded in dynamic spatial-temporal graph modeling. Initially, an integrated temporal feature processing strategy is implemented using signal decomposition and convolution techniques to address data spikes and high volatility. Subsequently, we introduce the Spatial-Temporal Dynamic Graph Convolutional Recurrent Network (STDGCRN) to capture detailed spatial-temporal dependencies across multiple traffic modes, enhanced by an adaptive channel attention mechanism. Finally, the self-attention mechanism is applied to incorporate various external factors, further enhancing prediction accuracy. Experiments on a real-world dataset from Guangzhounan Railway Station in China demonstrate that MM-STFlowNet achieves state-of-the-art performance, with an average improvement of 52.56% in MSE and 36.38% in MAE. Especially during peak hours, it demonstrates excellent forecasting performance, providing valuable insights for transportation hub management. Our model is also demonstrated strong generalization in low-resource scenarios and different traffic scenarios. Our code is available at https://github.com/BMRETURN/MM-STFlowNet]]></description>
      <pubDate>Thu, 28 May 2026 17:09:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658887</guid>
    </item>
    <item>
      <title>Estimating the societal value of airport slots</title>
      <link>https://trid.trb.org/View/2669966</link>
      <description><![CDATA[This research integrates discrete choice modelling and game theory to estimate the societal value of airport slot capacity. We first estimate heterogeneous passenger preferences for fares, frequencies and flight directness and then embed these estimates into a game-theoretic framework of competing airlines. Our framework captures the propagation effects of slot regulation across networks, the distributional impacts on passengers and airlines and environmental externalities from global emissions and local pollution. We apply this framework to assess the implications of tightening slot capacity at hub airports in North America and Europe, regions that collectively account for around 50% of worldwide revenue passenger kilometres in 2024. Social welfare declines when slot capacity is reduced because diminished connectivity and higher fares erode consumer surplus. Airline profitability is only marginally affected under mild slot reductions because increased market power raises per-passenger revenues, which offset lower passenger volumes. The environmental gains from slot reductions do not outweigh the loss in consumer surplus. However, from the perspective of a local regulator, slot reductions may increase social welfare if global environmental impacts are internalized and the social cost of carbon is sufficiently high, particularly at hubs with a large share of long-haul routes. Our findings highlight the importance of accounting for hub location and network effects in the cost-benefit analysis of slot capacity management.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669966</guid>
    </item>
    <item>
      <title>Stable Hierarchy, Dynamic Composition: Sectoral Evolution in Hong Kong Shipping Hub from an Ecosystem Perspective</title>
      <link>https://trid.trb.org/View/2694885</link>
      <description><![CDATA[With global shipping becoming increasingly complex, port-centric approaches are no longer sufficient to explain how modern shipping hubs evolve. This study pioneers a quantitative ecosystem-level analysis of shipping hubs, providing strategic insights amid market transformations. Based on intersectoral interdependencies, this study innovatively integrates the ecological Lotka-Volterra model with complex network analysis to capture sectoral impacts, connectivity, and resource transmission capabilities over time, thereby revealing sectoral evolution of shipping hubs. Using Hong Kong (2001–2023) as a case study, this study examines the shifting roles and interactions across eleven core sectors. Findings challenge conventional views: Hong Kong’s container terminal serves more as a stabilizing anchor than an active engine, while sea shipping, though vital for revenue and resource flow, has limited sectoral impact. Conversely, cargo forwarding generates greater industry-driven impact by leveraging digital convenience to expand clients’ geographic reach and reconfigure cargo information flows across air-sea and mainland interfaces. Interestingly, even modest-scale sectors (e.g., shipping insurance) can exert influence comparable to that of larger sectors, particularly during financial crises. Results suggest that enhanced integration of port and shipping services, facilitated by growing cargo forwarding and sea shipping connectivity, could transform ports’ limited reach into broader growth opportunities, revitalizing the Hong Kong shipping ecosystem. This study advances knowledge of shipping ecosystems through empirical evidence and a replicable methodology, informing policy and industry decision-making.]]></description>
      <pubDate>Mon, 11 May 2026 17:11:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694885</guid>
    </item>
    <item>
      <title>Selecting urban mobility hub locations using data envelopment analysis and geographic information systems</title>
      <link>https://trid.trb.org/View/2664092</link>
      <description><![CDATA[Urban Mobility Hubs serve as strategic locations that integrate sustainable transportation modes, enhance Jakarta’s connectivity, and promote sustainable urban development. Selecting optimal hub sites is challenging, given the interdependencies among transport systems, data limitations, and competing policy priorities. This study proposes an integrated framework that combines Data Envelopment Analysis (DEA) with a Game Theory approach, Tobit regression, and Geographic Information Systems (GIS) to address these complexities. The framework captures competitive cooperative interactions among modes and derives spatial weights to minimise subjectivity in hub assessment. Results indicate that the Light Rail Transit (LRT) system achieves the highest efficiency, while Transjakarta performs less effectively. Spatial analysis highlights Pasar Senen as the most suitable hub, reflecting its multimodal access, dense population, and alignment with Jakarta’s development priorities. The findings underscore the significance of LRT in Jakarta’s public transportation and demonstrate that the proposed framework offers a decision support tool applicable to other megacities seeking equitable, low-emission, and livable mobility solutions.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664092</guid>
    </item>
    <item>
      <title>From structure to function: A comprehensive benchmark on the estimation of hubs in air transportation through complex network metrics</title>
      <link>https://trid.trb.org/View/2667044</link>
      <description><![CDATA[Hubs play a critical role in air transportation systems by switching/sorting/collecting and providing a consolidation/breakbulk function. Previous research has extensively employed network centrality measures, including degree, betweenness, and closeness, as primary indicators for identifying hubs within air transportation networks. These metrics serve to quantify an airport’s significance based on its local/global connectivity and influence within the network. However, relying on topological indicators can be misleading due to the lack of empirical flow data, potentially resulting in an inaccurate assessment of an airport’s true importance within the network. In this study, we provide a comprehensive evaluation of how useful complex network metrics are in the task of identifying hubs. Integrating worldwide aircraft movement and passenger origin–destination data, our study offers a comprehensive benchmark for hub identification through structural complex network techniques. The benefits of this research include a more informed usage of the quality obtained from complex network indicators as proxy for indicating the extent of hubs as well as, downstream, an enhanced network planning and optimized resource allocation.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:17:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667044</guid>
    </item>
    <item>
      <title>Analyse, Design, Implement, Evaluate: Introducing the Zero Emission Multimodal Mobility Hub Cycle to Support the Development of Hubs Serving Freight and Passenger Mobility</title>
      <link>https://trid.trb.org/View/2579521</link>
      <description><![CDATA[In recent years, passenger and freight mobility are no longer addressed separately. As European cities are hardly pressed to transmit to smart and zero emission functional urban areas, mobility solutions and services are designed under one lens, supporting a strong inter-modal and inter- sectoral passenger and freight integration, in accordance with the “Green Paper on Urban Mobility” suggestions. Under this framework, the novel concept of “multimodal mobility hubs” has been put forward, proposing an innovative approach according to which, various sustainable transport modes are connected in a single location, for serving “last mile” delivery of people and goods, satisfying also needs of local communities. But, how can cities design, implement and operate successful and long-term multimodal hubs? Are there general design rules to be followed? Which is the appropriate location to be put in place? How accessibility and security are guaranteed? What governance and financing issues should be addressed? How the appropriate synergies can be achieved? In an effort to address these inquiries, the current study presents the Zero Emission Multimodal Mobility Hub (ZEMM-Hub) cycle, developed within the framework of MOVE21 EU2020 Horizon project, where different multimodal hubs were implemented and evaluated in three urban nodes of the Scandinavian TEN-T corridor. Based on insights and experience gained, the paper presents a methodology set up mainly inspired by the well-known Sustainable Urban Mobility Plan cycle. ZEMM-Hub cycle, aims to guide cities in designing and implementing hubs to reach their goals and transmit to smart and zero emission functional urban areas.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579521</guid>
    </item>
    <item>
      <title>A Spatial Relation Perspective on the City-Station Link: Cases Study on Florence S.M.N and Madrid Atocha</title>
      <link>https://trid.trb.org/View/2579953</link>
      <description><![CDATA[Train stations as public spaces representing the city, have become gathering points for the city and its inhabitants, acting almost as “gateways to the city” in addition to serving as intersections connecting different rail networks. High-speed rail stations have become multi-storey buildings with different functions; they have become social centers of the city, catering to different needs beyond transportation, with stores, restaurants, hotels and offices where people can spend their time outside of transportation. The Santa Maria Novella (SMN) train station in Florence and the Atocha train station in Madrid are two representative train stations in Europe that play an important role in their respective cities. Using Santa Maria Novella Train Station in Florence, Italy, and Madrid Atocha Train Station in Madrid, Spain, as case studies, this study explores the multifaceted relationship between cities and train stations, how these central train stations have become landmarks and key nodes in their respective cities, and how train stations can be analyzed in terms of their architectural, social, cultural, aesthetic, and landscape dimensions, which can shed light on the future development of transportation hubs and cities provides insights into the future development of transportation hubs and cities, and serves as a reference for urban planning and the construction of transportation facilities.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579953</guid>
    </item>
    <item>
      <title>Travel Preferences Among Car Drivers At Urban And Regional Multimodal Mobility Hubs: A Stated Choice Experiment</title>
      <link>https://trid.trb.org/View/2682106</link>
      <description><![CDATA[Multimodal mobility hubs are emerging as an effective solution for integrating various transport modes and reducing reliance on private cars. Their successful design and implementation require a thorough understanding of factors affecting user preferences and behaviors concerning the hubs and the transport modes they offer. This study used a stated choice experiment to examine the travel preferences of car drivers in the Netherlands, distinguishing between urban and regional hubs. The design included hub characteristics, mode attributes, and contextual variables. The results from an error component multinomial logit models show that private cars remain the preferred mode when offered either type of hubs. Factors like sunny weather, shorter travel distances, and free parking at hubs increased hub attractiveness. Buses and trains were preferred in urban and regional hubs, respectively, compared to shared e-bike, share e-scooter and shared car. Younger, highly educated, and middle-income respondents show greater inclination toward shared modes, indicating a promising target group for mobility hub adoption.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682106</guid>
    </item>
    <item>
      <title>Decoding the Vertiport: Planning for Urban Air Mobility</title>
      <link>https://trid.trb.org/View/2655723</link>
      <description><![CDATA[The potential of advanced air mobility (AAM) has recently been receiving a tremendous amount of attention in both the media and within the research literature. In particular, service in urban environments, referred to as urban air mobility (UAM), has received the overwhelming majority of focus. This nascent field, centered around electric vertical takeoff and landing flying vehicles (eVTOLs), is capturing imaginations and investments at an impressive pace. Yet, guidance on the siting and development of vertiports, the interface between ground transit and UAM, is trailing the pace of the forthcoming service. Vertiports are expected to operate like bus stops, transit stations, helipads, and airports. Still, this new public transportation infrastructure has the potential to dramatically alter the urban landscape due to their space requirements, safety requirements, and pace of low-altitude flight operations. Therefore, current and long-range plans must acknowledge this potential, and planning stakeholders must utilize participatory geographic information systems (GIS) to select safe, efficient, and accessible locations for vertiports to ensure future capital investment. Because of its unique attributes and requirements, the site selection process for UAM infrastructure demands a unique perspective that varies from the planning criteria for traditional transit hubs. This article summarizes the current UAM literature and offers stakeholders an informed yet simple workflow process for urban, suburban, and exurban site suitability selection that can be implemented during preliminary community engagement and early master planning processes. An example case in which this workflow was applied is provided using the San Francisco Bay Area as recent literature has shown this area to be a prime candidate for a UAM transportation network of vertiport locations being integrated into a regional multi-modal transit system.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655723</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>Respect pricing in the US airline industry</title>
      <link>https://trid.trb.org/View/2684542</link>
      <description><![CDATA[This paper finds empirical evidence suggesting that US airlines, particularly legacies, do not aggressively compete within each other's nonstop markets. Instead, they appear to respect each other by pricing their competing connecting services higher. This mutual respect increases as the number of legacy carriers increases, especially at their hub markets. While Southwest has some influence in diminishing this respect, the observed pricing behavior persists.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:15:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684542</guid>
    </item>
    <item>
      <title>Optimizing diverse stakeholders’ objectives in a real-time multimodal mobility hub system</title>
      <link>https://trid.trb.org/View/2659389</link>
      <description><![CDATA[Ridesharing could offer a solution to urban mobility challenges by delivering affordability and convenience while reducing congestion and environmental impact. This paper presents a system for multimodal mobility hubs with parking facilities, integrating personal vehicles with carpooling options and shuttle services into a cohesive network designed to accommodate real-time inbound and outbound ride requests. To address diverse interests and priorities within urban transportation, a column generation approach is employed to optimize the objectives of key stakeholders. An extensive computational study is performed using real-world public transit and ridesharing datasets from Québec City. The emission, user, and system objectives favor shared transportation options, while the operator objective shows a more substantial reliance on solo vehicles. Additionally, the results indicate that a transition to fully electric vehicles powered by a renewable energy mix can reduce average emissions by 78.1% compared to a scenario with gasoline-fueled vehicles. Rigorous evaluation across diverse scenarios demonstrates the proposed system’s efficacy in addressing the complexities of large-scale transportation systems.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659389</guid>
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