<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?subject=Aviation" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. National Academy of Sciences. All rights reserved.</copyright><docs>http://blogs.law.harvard.edu/tech/rss</docs><managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor><webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster><image><title>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Health and Safety Impacts of Aircraft Cabin Temperatures</title><link>http://pubsindex.trb.org/view/2709677</link><description><![CDATA[Commercial aircraft cabins expose passengers and flight attendants to a range of environmental conditions, including at times excessively hot or cold temperatures that may affect health, safety, and comfort. While aircraft systems are generally effective at maintaining acceptable cabin conditions, challenges are more likely to arise during ground operations, particularly in extreme outdoor temperatures or when equipment used for thermal control in aircraft cabins is unavailable or not functioning properly. Because passengers and flight attendants have limited ability to leave or substantially modify the cabin environment, understanding and managing temperature-related risks is an important component of safe air travel. This report examines the available evidence on how cabin temperatures and humidity conditions may influence physiological, cognitive, and behavioral outcomes for passengers and flight attendants. The report finds that serious health impacts are uncommon, but conditions causing thermal discomfort may occur more frequently and can affect factors critical to the conduct of flight attendant duties (e.g., concentration, decision-making) and passenger behavior in ways that create safety concerns. The report highlights differences in how passengers and flight attendants experience cabin temperatures because of variations in activity levels, clothing constraints, exposure frequency, age, and underlying health conditions. It also concludes that available data on cabin temperature and humidity conditions are fragmented and insufficient to reliably estimate the frequency of related health and safety events. The report provides recommendations to strengthen management of cabin temperature risks, including integrating temperature and humidity hazards into airline safety management systems, improving collection and use of aviation safety and health event data, strengthening operational practices for thermal control in aircraft cabins, supporting flight attendants in responding to unsafe conditions, and providing passengers with information about temperature-related risks. The report also recommends that the Federal Aviation Administration establish a research program to systematically collect representative data on cabin temperature and humidity conditions to better inform future mitigation strategies and safety oversight.]]></description><pubDate>Thu, 04 Jun 2026 10:58:21 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709677</guid></item><item><title>Evaluation of Critical Pavement Responses from Accelerated Pavement Testing on Airfield Flexible Pavements Surfaced with Hot and Warm Mix Asphalt</title><link>http://pubsindex.trb.org/view/2709231</link><description><![CDATA[Highway agencies frequently use warm mix additives as compaction aids. Lower production temperature of the warm mixes simultaneously entails the benefit of widening the paving window. Airport authorities can ensure significant fiscal savings with reduced downtime through the adoption of similar technologies in airfield paving. However, limited scientific information exists concerning the performance of these materials in airside flexible pavements. Aircraft gross weights and tire pressures have also been routinely increasing over the last few decades with the advent of new-generation aircraft. The Federal Aviation Administration (FAA) procured a sixth-generation heavy vehicle simulator, airfields (HVS-A) to investigate the performances of resilient pavement materials under simulated aircraft loading. Accordingly, six full-scale test lanes were constructed during Test Cycle 1 (TC1) at FAA’s National Airport Pavement and Materials Research Center (NAPMRC) using four different asphalt concrete (AC) mixes with two different binder grades. Each test lane was divided into three test sections. Asphalt strain gauges and pressure cells were installed in the test sections to monitor the critical pavement responses over the duration of traffic tests. Corresponding test sections were trafficked under different combinations of high tire pressure and temperature. This paper examines the tensile strains at the bottom of AC and compressive stresses on top of the subgrade in reference to the observed rutting performances in four TC1 outdoor test lanes. The respective hot and warm mixes exhibited comparable rutting performances, and the sensor observations corroborated the related findings.]]></description><pubDate>Mon, 01 Jun 2026 16:52:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709231</guid></item><item><title>Mechanisms to Address Off-Airport Obstructions</title><link>http://pubsindex.trb.org/view/2701283</link><description><![CDATA[This report presents the state of practice of airport methods to address obstructions located off airport property. The synthesis includes information on activities airports take to address obstructions, including outreach with landowners and other stakeholders, time and costs to resolve issues, and support and coordination from local, state, and federal authorities. Under ACRP Project 11-03/Topic S09-11, “Survey of Mechanisms to Address Off Airport Obstructions,” Embry-Riddle Aeronautical University was asked to synthesize and document the various mechanisms airports use to address obstructions, with a focus on obstructions that are outside of the airport boundary. Information used in this study was obtained through a literature review, a survey of airports, and interviews to develop in-depth case examples. This synthesis is an immediately useful document that records the practices that were acceptable within the limitations of the knowledge available at the time of its preparation. As progress in research and practices continues, new knowledge will be added to that now at hand. The audience for this synthesis is airport sponsors, local permitting authorities, state aviation officials, and non-airport stakeholders that are involved in addressing off-airport obstructions.]]></description><pubDate>Sat, 16 May 2026 12:15:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701283</guid></item><item><title>Enhancing Expressway Crash Rescue with Vertical Takeoff and Landing Vehicles: Insights from an Evolutionary Game Study</title><link>http://pubsindex.trb.org/view/2701227</link><description><![CDATA[Expressway traffic crashes often result in higher fatalities and more severe congestion compared with incidents on regular roads, creating significant challenges for timely emergency response. Vertical takeoff and landing (VTOL) vehicles offer a potential solution to bypass surface-level bottlenecks and efficiently deliver emergency personnel and supplies. This study develops a tripartite evolutionary game model to analyze the strategic interactions among crash participants, VTOL operators, and road authorities in the context of expressway rescue. The analysis identifies the most favorable equilibrium as one where point-to-point rescue is adopted, VTOL services are actively provided, and road conditions are effectively managed. This setup encourages coordination among stakeholders and enhances overall rescue efficiency. The evolutionary path is affected by factors such as road regulation costs, subsidy coefficients, and stakeholders’ initial willingness to cooperate. Notably, higher initial cooperation from crash victims and VTOL operators accelerates convergence toward stable outcomes. These findings improve understanding of the feasibility conditions for VTOL deployment in emergency scenarios and guide cost-sharing mechanisms, stakeholder alignment, and policy design to support the practical implementation of VTOL-based rescue strategies.]]></description><pubDate>Mon, 11 May 2026 12:24:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701227</guid></item><item><title>Development of an Integrated Small Aircraft Runway Length Analysis Tool</title><link>http://pubsindex.trb.org/view/2701141</link><description><![CDATA[The Federal Aviation Administration Advisory Circular (AC) 150/5325-4B provides the current method for estimating runway length requirements in airport design. The design curves and necessary runway length adjustments published two decades ago may not reflect the performance of many new-generation aircraft operating in the National Airspace System. Moreover, this AC offers limited information about the trade-offs between runway length, useful load, and mission range. This paper presents an integrated computer model called the Small Aircraft Runway Length Analysis Tool (SARLAT) to improve the existing methodology. The SARLAT incorporates 76 individual aircraft performance characteristics based on robust data processing, consolidation, and validation workflow. A regression-based model has been developed to account for nonzero runway gradients and different runway surface conditions. An analysis indicates that the current design curves are conservative for modern mid-size and super mid-size business jets, but cannot accommodate light jets, consequently constraining operators’ operational flexibility and possibly reducing airport revenue. SARLAT provides aircraft stage length and payload-range analysis to assist airport designers and improve decision-making. The stage length analysis estimates the cumulative distribution of distances flown by individual aircraft in the United States. Using a time-step numerical simulation, SARLAT includes payload-range analysis to quantify the trade-offs between the aircraft’s useful load and mission range. Finally, SARLAT determines the critical aircraft operation at the airport based on runway length and geometric design requirements. Since SARLAT leads to more accurate and cost-effective estimates of runway length requirements, the FAA plans to mandate the use of this tool in the Airport Improvement Program.]]></description><pubDate>Mon, 11 May 2026 12:24:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701141</guid></item><item><title>Short-Term Forecasting of Checked Baggage Flow in Airports Using a Hybrid Improved Particle Swarm Optimization-Back Propagation Neural Network Model</title><link>http://pubsindex.trb.org/view/2697859</link><description><![CDATA[Accurate baggage flow (BF) prediction is crucial for airport and airline operations, enabling timely decision-making and efficient resource allocation. However, current approaches often estimate BF indirectly based on passenger flow (PF), failing to adequately capture the multi-timescale dynamic correlations between the two, which limits prediction performance. To address this gap, this paper proposes a hybrid modeling framework. First, the dynamic correlation between PF and BF is examined across four time granularities: annual, monthly, weekly, and daily. Quantitative analysis reveals a clear time scale dependency, clarifying the coupling complexity and key influencing factors of BF. Second, multi-dimensional data sources are integrated to scientifically select feature vectors for short-term BF modeling. Finally, a hybrid prediction model, named “improved particle swarm optimization-back propagation neural network” (IPSO-BPNN), is developed. This model incorporates an improved particle swarm optimization (PSO) algorithm to optimize a back propagation (BP) neural network for accurate short-term forecasting. A case study conducted at a major Chinese hub airport confirms the method’s effectiveness. Multi-metric evaluations demonstrate that IPSO-BPNN significantly outperforms existing methods, that is, BP, PSO-BP, and genetic algorithm (GA)-BP, when incorporating multiple factors, improving R2 by 8.08%, 5.32%, and 5.40%; reducing mean absolute error by 25.92%, 21.56%, and 20.32%; and lowering root mean square error by 27.91%, 22.44%, and 19.14%, respectively. The findings provide practical decision support for resource management and optimization in baggage transportation systems.]]></description><pubDate>Tue, 05 May 2026 10:16:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2697859</guid></item><item><title>Buy America Requirements for Federally Obligated Airports</title><link>http://pubsindex.trb.org/view/2696859</link><description><![CDATA[The 2021 Infrastructure Investment and Jobs Act included the Build America, Buy America (BABA) Act that established new conditions for spending federal money on infrastructure. BABA stipulates that manufactured products and construction materials must be produced in the United States when federal funds are used to finance infrastructure projects. BABA expands previous legislation that required airfield infrastructure projects that used federal money, mostly Airport Improvement Program grants, to use products and materials produced domestically. However, the new BABA makes federal funds available for airport terminal projects beyond runway or taxiway projects. Under ACRP Project 11-01/Topic 16-01, “Buy America Requirements for Federally Obligated Airports,” Dempsey Aviation Consulting was asked to update the information in ACRP Legal Research Digest 18: Buy America Requirements for Federally Funded Airports (2013) to include new requirements under the Act. This includes the application of the domestic content thresholds; available waivers, other exceptions, and their practical implications; and considerations for management to achieve BABA compliance. The digest includes an appendix with a table that compares Buy America provisions in BABA in relation to prior legislation applicable to federal aviation projects and grant programs.]]></description><pubDate>Sat, 02 May 2026 15:47:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2696859</guid></item><item><title>Data Protection and Privacy Management at U.S. Airports: Guidelines</title><link>http://pubsindex.trb.org/view/2697804</link><description><![CDATA[This report provides U.S. airports with real-world use cases of data collection activities. Additionally, the report provides a thorough review of new technologies and processes, legal and regulatory trends, and the specific challenges that U.S. airports face when collecting, processing, and using data at an enterprise level. A detailed literature review includes a review of publicly available information on data management efforts of several airports. More than 20 airports were interviewed on their data collection efforts. That feedback was distilled into the use cases in this report. This report will be of interest to the staff at U.S. airports of all sizes, most notably airport leadership, legal staff, and airport information technology specialists.]]></description><pubDate>Sat, 02 May 2026 15:47:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2697804</guid></item><item><title>Ripple Effects of Slot Deregulation: Evidence from Newark Liberty’s 2016 FAA Reclassification</title><link>http://pubsindex.trb.org/view/2691004</link><description><![CDATA[This study is an evaluation of the effect of the FAA’s 2016 decision to remove slot controls at Newark Liberty International Airport by reclassifying it from Level 3 to Level 2. Using flight-level data from 2014–2018, synthetic difference-in-differences, difference-in-differences, and synthetic control are applied in the analysis to estimate the policy’s effects on absorbed, propagated, and generated delays. Results show a clear trade-off. Absorbed delays increased by 6–8 min relative to the counterfactual, while propagated delays rose by 3–4 min, indicating greater tolerance of inbound disruptions and heightened transmission to subsequent flights. In contrast, generated delays declined modestly, by 0.5–1 min, suggesting some improvement in turnaround efficiency. Overall, deregulation reduced the creation of new inefficiencies but amplified existing disruptions across the network. The findings indicate the importance of balancing regulatory flexibility with measures to preserve resilience in congested airport systems.]]></description><pubDate>Fri, 10 Apr 2026 16:00:15 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691004</guid></item><item><title>Exploring the Impact of Artificial Intelligence on the Airport Industry</title><link>http://pubsindex.trb.org/view/2685046</link><description><![CDATA[This report aims to provide an overview of artificial intelligence (AI) technologies and applications and their connections with airports, including the opportunities and challenges of implementing AI to support airport business and operations. These business and operations include airside, terminal, landside, and cross-domain areas. This paper begins with a brief introduction to AI, including its history, and evolution, followed by a discussion of the current needs and challenges faced by airports. This paper then explores technological advancements that may improve airport business and operations, with a focus on operational efficiency, safety, and passenger experience. Consideration is also given to regulatory requirements and other factors that are relevant to initiating AI implementation. This paper further identifies future research needs, highlighting gaps in technology applications within the airport industry. It also explores potential risks, failure points, and operational challenges associated with examples of AI adoption discussed in this paper. The First Look provides an initial overview of AI in Nexus with the airport industry and helps set the stage for deeper discussion on these topics during the ACRP Insight Event Exploring the Impact of Artificial Intelligence on the Airport Industry on May 19-20, 2026. In addition, it gives examples of AI applications across other sectors, such as smart buildings, healthcare, manufacturing, logistics, finance, and retail. These examples may offer insights that are transferable into aviation. The appendix includes a more detailed overview of AI, covering different types of AI, public perceptions, and common practical applications, thereby providing readers with a foundational understanding of AI.]]></description><pubDate>Mon, 30 Mar 2026 11:10:36 GMT</pubDate><guid>http://pubsindex.trb.org/view/2685046</guid></item><item><title>UAS Flight Proficiency Examination: Proctor Guide for PROPS Test</title><link>http://pubsindex.trb.org/view/2682213</link><description><![CDATA[This report provides instructions and scoring procedures for applying the standardized evaluation criteria for the Pilot Readiness and Operational Proficiency Standardized (PROPS) Test. Both the guide and the test were developed through iterative design phases, independent reviews, and structured feedback from state department of transportation (DOT) personnel. The guide and PROPS Test will be of particular interest to state DOTs, aviation program managers, and policymakers responsible for unmanned aircraft system (UAS) operations and workforce certification.]]></description><pubDate>Sun, 22 Mar 2026 17:18:11 GMT</pubDate><guid>http://pubsindex.trb.org/view/2682213</guid></item><item><title>Transforming Aviation Technical Authoring with Generative Artificial Intelligence: Toward Automation and Efficiency</title><link>http://pubsindex.trb.org/view/2681196</link><description><![CDATA[Over the coming decades, the aviation sector is expected to witness substantial growth driven by increasing global demand for air travel, necessitating efficient and precise technical documentation to manage the growing complexity of maintenance. As technical authoring processes remain labor-intensive and prone to inconsistencies, this study investigates the potential of Generative artificial intelligence (GenAI) to automate the creation of Engineering Orders (EOs), which are derived from Airworthiness Directives (ADs) and Service Bulletins (SBs). A three-phase approach is adopted to generate EOs from ADs and SBs, enabling a structured evaluation of GenAI’s performance in technical authoring. Expert reviews are integral to refining AI outputs, emphasizing the importance of integrating AI capabilities with human expertise. This study validates the effectiveness of GenAI in aviation technical authoring and introduces a scoring tool to evaluate the quality of AI-generated documentation across several dimensions: (1) technical knowledge; (2) accuracy; (3) comprehensiveness; and (4) usability and flexibility. The findings highlight that the synergy between AI-generated content and expert review significantly improves documentation quality by mitigating AI limitations, reducing the time required to produce technical documentation and ensuring practical applicability. The proposed approach provides a scalable framework that can be adapted for use in various industries requiring precise technical documentation.]]></description><pubDate>Mon, 16 Mar 2026 19:09:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2681196</guid></item><item><title>Preparing U.S. Airport Infrastructure for Weather Events</title><link>http://pubsindex.trb.org/view/2680124</link><description><![CDATA[“Preparing U.S. Airport Infrastructure for Weather Events,” held May 7–8, 2025, at the Keck Center of the National Academies of Sciences, Engineering, and Medicine in Washington, DC, focused on preparing airport facilities, infrastructure, and human resources for unexpected weather events. This Insight Event report is a compilation of the presentations and a factual summary of what occurred at the event. The event spanned 2 days and featured a variety of formats, including panel discussions, small group discussions, and facilitated group activities. The increase in the frequency and intensity of weather events requires new approaches to airport infrastructure planning, operations, and investment. The objectives of the event for attendees included the following: Understanding and sharing best practices for planning and responding to weather events; Understanding gaps in existing weather preparedness planning for airport personnel; Sharing research at the intersection of weather preparedness and airports; and  Identifying new research topics and research gaps at the intersection of weather, resilience, and airports.]]></description><pubDate>Sat, 14 Mar 2026 18:18:29 GMT</pubDate><guid>http://pubsindex.trb.org/view/2680124</guid></item><item><title>Public Acceptance of Autonomous Electric Vertical Take-Off and Landing Aircraft: Analysis Based on the Extended Technology Acceptance Model</title><link>http://pubsindex.trb.org/view/2675945</link><description><![CDATA[Urban air mobility (UAM) has been emerging as a promising direction in future transportation. The autonomous electric vertical take-off and landing (eVTOL) aircraft, as the core vehicle enabling urban air transportation, represents a promising technological trend because of its low operational costs and safety advantages. However, though public acceptance of UAM has been widely investigated, the potential users’ acceptance of autonomous eVTOL systems remains under-investigated. This research extends the technology acceptance model by incorporating perceived risk and trust factors and explores how it may explain variations in users’ acceptance of autonomous eVTOL aircraft. Thus, a survey study was conducted to guide the design and future deployment of autonomous eVTOL aircraft. Based on 412 responses from China, we analysed public acceptance of autonomous eVTOL aircraft using structural equation modeling. The study revealed that perceived usefulness was a primary determinant of potential users’ behavioral intention of using autonomous eVTOL aircraft. Further, trust demonstrates significant positive effects on perceived ease of use and perceived usefulness of autonomous eVTOL aircraft. Finally, respondents’ income and familiarity with eVTOL technologies can also moderate users’ psychological constructs and thus affect their willingness to use autonomous eVTOL aircraft. These insights provide valuable decision-making references for policymakers, eVTOL aircraft manufacturers, and service operators, contributing to both theoretical understanding of innovative transportation technology acceptance and practical implementation of autonomous eVTOL systems.]]></description><pubDate>Wed, 04 Mar 2026 09:15:50 GMT</pubDate><guid>http://pubsindex.trb.org/view/2675945</guid></item><item><title>Total Rewards for Airport Sponsor Employees</title><link>http://pubsindex.trb.org/view/2672717</link><description><![CDATA[This report presents the state of practice regarding airport total rewards programs that help recruit and retain individuals who are essential to keeping an airport running, with a focus on frontline and supervisory staff. This synthesis includes information on challenges and opportunities that airports have found when developing or revising total rewards, and information on ways airports recruit and retain frontline and supervisory employees using total rewards. Total rewards include health benefits, well-being programs, learning and development, and recognition programs, as well as compensation. Under ACRP Project 11-03/Topic S06-09, “Total Rewards for Airport Sponsor Employees,” Embry-Riddle Aeronautical University was asked to synthesize information to describe total rewards that airport sponsors use to attract and retain frontline and supervisory employees. Information used in this study was obtained through a literature review, a survey of airports, and interviews to develop case examples. Chapter 4 provides six case examples that highlight total rewards programs from airports with different governance structures, including airports governed by a port authority, airport authority, city, and county.]]></description><pubDate>Sun, 22 Feb 2026 14:57:19 GMT</pubDate><guid>http://pubsindex.trb.org/view/2672717</guid></item></channel></rss>