<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Shared micromobility discount ride programs: Knowledge, enrollment, and travel behavior</title>
      <link>https://trid.trb.org/View/2687041</link>
      <description><![CDATA[Shared micromobility services including shared e-scooters and bikeshare can help to bridge existing gaps in the transportation system and expand mobility. Yet historically underserved travelers face barriers to shared micromobility services, including high trip prices. To alleviate cost barriers, many shared micromobility programs require that operators discount rides for people earning low-incomes, but enrollments in these programs are low suggesting a disconnect between policy and outcomes. The research examines this disconnect and asks: 1) How does the share of people who qualify for discount ride programs compare to the share who know about them and are enrolled? 2) How do qualified travelers learn about discounted ride programs? And 3) what are the associations between travel behavior and enrollment in discount rides programs? The authors explore these topics with a two-sample survey of micromobility riders and adults living in US cities with shared micromobility services. The authors find that 18% of qualified (and 4% of total) respondents are enrolled in discount rides programs. The biggest barrier to enrollment is lack of knowledge: just 33% of qualified and 16% of all riders are aware of discount ride programs. At the same time, most people who qualify for and know about discount ride programs enroll (78%). These findings suggest that sharing information of discount rides programs may offer an opportunity to parlay knowledge into added mobility. The findings offer lessons for community engagement, enrollment processes, pricing, and public subsidy to expand shared micromobility access.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:18:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687041</guid>
    </item>
    <item>
      <title>Promoting Public Transport with a Discount on City Centre Shops at Large Cities</title>
      <link>https://trid.trb.org/View/2579510</link>
      <description><![CDATA[This study advocates for promoting public transportation to alleviate traffic congestion and emissions in city centers. It introduces a zoning plan with designated parking at four points around each zone. Those using public transport from farther parking spots would get discounts at city center shops, and the parking ticket would grant access to city services. This aims to reduce congestion, pollution, and enhance public transport efficiency. The proposed model focuses on implementing this strategy in the greater area of Thessaloniki, dividing the city into three zones. Each zone will offer varying discounts of 5% (Zone 1), 10% (Zone 2), and 15% (Zone 3), with the highest discount reserved for Zone 3, situated farthest from the city center. To validate the effectiveness of our approach, the future plan is to construct a detailed model and conduct a comprehensive survey of stakeholders. The insights gained from the survey will contribute valuable data to support the efficacy of the proposed zoning strategy. By implementing this innovative solution, a positive impact on the overall urban environment is anticipated, with reduced traffic congestion, lower pollution levels, and improved public transportation utilization.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579510</guid>
    </item>
    <item>
      <title>Determinants of the willingness to accept compensation for flight delays by low-cost airlines</title>
      <link>https://trid.trb.org/View/2663752</link>
      <description><![CDATA[Flight delays are one of the most common problems in the aviation industry. This study examines the determinants of the willingness to accept compensation offered by low-cost airlines for delayed flights. The willingness to accept using the contingent valuation method is used to investigate the amount of compensation for long delays before departure. Hypothetical scenarios involving flight delays are generated, and the payment card method is used to determine the range of starting bid values. Double-bounded dichotomous choice survey data, a bivariate probit model, and data collected from the Taoyuan International Airport in Taiwan are used to estimate compensation equations. The results indicate that the willingness to accept compensation is associated with sex, age, personal income, the purpose of the trip, nationality of the passengers, and preferences for alternative travel options. As the compensation offer increases, the probability that passengers will accept it also increases. The mean values of willingness to accept compensation range from US $34–$89 for a four-hour delay to US $131–$200 for an eight-hour delay. These estimates align with existing provisions, such as the JetBlue Airways customer protection program.]]></description>
      <pubDate>Fri, 24 Apr 2026 08:55:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663752</guid>
    </item>
    <item>
      <title>The Breeze Effect: Evidence on Demand Stimulation and Fare Impacts of An Emerging Low-Cost Carrier</title>
      <link>https://trid.trb.org/View/2655604</link>
      <description><![CDATA[Breeze Airways represents a new generation of low-cost carriers that strategically target secondary and underserved markets rather than competing directly with established airlines at congested hubs. This study examines how Breeze’s operations have affected air travel demand and airfares across U.S. airports. with particular attention to whether these effects extend beyond directly served locations. Using quarterly airport-level data and spatial econometric models, including the SARAR and SDM frameworks, we estimate both direct impacts at Breeze-served airports and indirect effects at neighboring facilities under varying distance thresholds. The results indicate that Breeze boosts passenger demand and lowers fares at served airports, with demand spillovers evident at neighboring airports within 40–80 miles. These indirect effects are transmitted through spatial dependence in outcomes, as demand increases at one airport diffuse to others via inter-airport linkages. By contrast, fare reductions remain primarily localized, with significant indirect effects detected only at the 40-mile threshold. Collectively referred to as the ‘Breeze effects,’ these results highlight a dual role of Breeze Airways in stimulating demand regionally while constraining fare competition to proximate markets. The findings suggest the importance for airport managers and policymakers to account for both the geographic scope and the underlying spatial mechanisms of new-entrant carriers when evaluating their impacts on regional aviation systems.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655604</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>The end of cheap flying? Decarbonization and capacity limits reshaping air travel growth in a mature market</title>
      <link>https://trid.trb.org/View/2655838</link>
      <description><![CDATA[This paper presents a long-term forecast for the Dutch aviation sector up to 2060, analyzing the impacts of scenarios involving international demographic and economic development, European and global climate policy, and local airport capacity constraints. The availability of sustainable fuels is assessed for the global market. Using four distinct scenarios, combining two dimensions of high / low economic and demographic growth and fast / delayed climate transitions, the study models future air travel demand, aircraft movements at Amsterdam Airport Schiphol, energy requirements, and resulting CO₂ emissions. The modeling is conducted using the AEOLUS model, which relies on population and economic growth, alongside the costs of flying, to determine travel demand using, among other, income and price elasticities. The analysis reveals that in high-growth scenarios, unconstrained demand could lead to a doubling of aircraft movements to approximately 1 million per year at Schiphol by 2060. However, the current flight cap of 500,000 movements at Schiphol represents the most significant limiting factor, which would be reached before 2030 in these scenarios. This scarcity incentivizes airlines to deploy larger aircraft, allowing passenger numbers to increase even after the flight limit is met. All scenarios project a sharp decline in CO₂ emissions after 2030, driven by mandated blending of Sustainable Aviation Fuels (SAF) and efficiency gains. Consequently, a decades-long trend of falling airfares is expected to reverse, with ticket prices projected to rise across all scenarios due to higher fuel and carbon costs, compounded by capacity scarcity in high-growth scenarios.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:15:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655838</guid>
    </item>
    <item>
      <title>The evolution of air transport connectivity – a case study of Cluj-Napoca Int’l Airport</title>
      <link>https://trid.trb.org/View/2666094</link>
      <description><![CDATA[The main objective of this paper is to provide an in-depth analysis of the evolution of air connectivity at a regional-class airport in South-East Europe — a topic for which scientific evidence remains limited. This study investigates the connectivity of the largest regional airport in Romania between 2019 and 2024. The connectivity model employed in our research aims to illustrate and analyze the transformation of the airport’s route network, taking into account not only quantitative data (passenger traffic and number of flights), but also qualitative indicators (such as load factor). During the examined period, passenger traffic at Cluj-Napoca Airport experienced significant fluctuations, primarily due to the COVID-19 pandemic. Following the end of the pandemic, air traffic resumed its growth, and by 2023, Transylvania’s largest airport had already surpassed its absolute record from 2019. Low-cost carriers are indispensable elements of Romania’s air transport system, accounting for nearly two-thirds of the country’s passenger traffic. Based on the results, it can be stated that low-cost airlines play a decisive role in the development of air connectivity and, consequently, in the competitiveness of the airport as well.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666094</guid>
    </item>
    <item>
      <title>The Distributional Consequences of Tax Pass-Through: The Case of Germany’s Fuel Tax Discount</title>
      <link>https://trid.trb.org/View/2640930</link>
      <description><![CDATA[Exploiting exogenous variation in retail fuel prices from a temporary fuel tax discount in Germany, we explore the distributional consequences emerging from differential pass-through rates over space and time. We draw on daily gasoline prices of virtually all gas stations in Germany and neighboring France, with France serving as a control site, and estimate an event study model covering the full period of the discount from June to August 2022. We find average pass-through rates on the order of 96% for diesel and 82% for petrol, but with substantial variability by regional income and station density. Our results additionally reveal heterogeneity over time: The magnitude of the pass-through rate dissipates sharply for both fuel types over the three months in which the discount was in effect, dropping to 46% for diesel and 74% for petrol by the final month, a pattern consistent with retailer responses to short-term changes in consumer attention. Taken together, our results indicate that average pass-through estimates may obscure spatial and temporal heterogeneity that bears upon the assessment of distributional effects: A back-of-the-envelope calculation indicates that 62% of the discount’s financial relief accrues to households with above-median incomes.]]></description>
      <pubDate>Tue, 17 Mar 2026 09:47:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640930</guid>
    </item>
    <item>
      <title>Global airline productivity analysis for the year 2022</title>
      <link>https://trid.trb.org/View/2646883</link>
      <description><![CDATA[Investigating the productivity of airlines in these times is crucial to compare airlines’ performance against peers during a period marked by significant changes in travel demand, operational protocols, and market dynamics. In this study, we present the results of an airline benchmark for the year 2022. Specifically, we collected data for 76 out of 200 largest airlines according to the number of transported passengers in the year 2022. To perform a comprehensive productivity analysis among these airlines, we use eight indicators from the literature, including aggregated ones, such as Total Factor Productivity (TFP) and Residual Total Factor Productivity (RTFP), as well as more specific indicators, such as labor productivity and fuel productivity. As a result of our investigation, we report on the outperforming airlines in two distinct categories: Full-service carriers and low-cost airlines. We believe that this benchmark is a natural complement to existing work on airport benchmarking and will help researchers as well as policy makers to guide airlines towards efficient and sustainable air transportation.]]></description>
      <pubDate>Mon, 16 Mar 2026 16:47:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646883</guid>
    </item>
    <item>
      <title>Impact of Generative AI Models on Labor Utilization and TFP Growth in the U.S. Airline Industry: An Exploratory Analysis</title>
      <link>https://trid.trb.org/View/2639407</link>
      <description><![CDATA[The airline industry may stand at a transformative juncture as generative AI models reshape labor utilization and operational efficiency. While AI technologies promise productivity gains through automation, optimization, and enhanced decision-making, their impacts may vary across airline business models due to differences in operating strategies, staffing structures, and service complexity. This study provides a quantitative assessment of AI’s influence on Total Factor Productivity (TFP) growth in the airline industry by incorporating AI exposure into workforce dynamics. Using data from U.S. airlines between 2000 and 2019, we simulate the impact of AI adoption on labor utilization. Our results suggest that AI could improve labor utilization by 30.2%, contributing to an average industry-wide TFP increase of 0.1%. Ultra-Low-Cost Carriers (ULCCs) are projected to experience the largest gains, averaging 0.3%, driven by their streamlined workforce structures, simplified operations, homogenous passenger bases, and reliance on labor efficiency to sustain low-cost models. Low-Cost Carriers (LCCs) experience modest improvements of 0.1%, reflecting their balance between cost-efficiency practices and operational complexity. Full-Service Airlines (FSAs) show intermediate growth of 0.2%, constrained by their diverse task requirements and complex hub-and-spoke operations. These findings are consistent with the broader literature, which finds that AI’s productivity gains are most pronounced in labor-intensive sectors, while complexity tempers efficiency gains.]]></description>
      <pubDate>Thu, 12 Mar 2026 14:02:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639407</guid>
    </item>
    <item>
      <title>Economic pragmatism and strategic coherence: A conjoint analysis of hybridization trade-offs in low-cost airlines</title>
      <link>https://trid.trb.org/View/2640945</link>
      <description><![CDATA[Low-cost carriers (LCCs) face a critical tension when pursuing growth: whether to prioritize economic pragmatism or strategic coherence in adopting hybrid activities. We employ conjoint analysis (CA) with 125 senior airline managers to quantify decision trade-offs over four strategic attributes. Results reveal that cost control (46.8 %) and revenue generation (28.1 %) dominate hybridization choices, while customer satisfaction (13.9 %) and activity alignment with the low-cost model (11.2 %) exert lesser influence. The distribution of accepted and rejected CA profiles shows that managers claim activity alignment and customer satisfaction matter, but in practice, costs and revenues dominate their choices. The results show that managerial background matters as LCC-experienced senior managers enforce cost discipline to a greater extent than hybrid/full-service airline backgrounds, who favor additional revenue/customer satisfaction activities to a greater extent. Overall, the results reveal that, in practice, the process of hybridization, in response to growth constraints, is driven to a larger extent by economic pragmatism than by coherent strategic frameworks.]]></description>
      <pubDate>Wed, 11 Mar 2026 16:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640945</guid>
    </item>
    <item>
      <title>Revealing social weights in reduced fare programs</title>
      <link>https://trid.trb.org/View/2627430</link>
      <description><![CDATA[Fare transit trip discounts are offered for preferential groups in most cities, presenting a wide variation across places. Transit and higher-level authorities give a variety of qualitative justifications for those reduced fares, which suggests that observed reduced transit prices are the result of a cumulative process of political will. As this is a quantitatively unexplored field, an approach is proposed to reveal the implicit social weights behind the fares applied to the different groups. The method rests upon a theoretical model that yields socially optimal fares as if weights on the welfare of the various groups had been applied. By inverting the results, those weights turn into the unknowns that happen to be a function of observed fares, demand elasticities, and operators’ marginal costs. Two examples show that revealed social weights can be lower or higher than what the discounts suggest when compared to full fares. The method can be used to inform decision makers of the (in)consistency between the implemented fares and their declared intentions.]]></description>
      <pubDate>Thu, 26 Feb 2026 09:14:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627430</guid>
    </item>
    <item>
      <title>Low-cost carriers vs. high-speed rail? Difference in passenger's travel preferences in Shanghai and Chengdu</title>
      <link>https://trid.trb.org/View/2633477</link>
      <description><![CDATA[High-speed rail (HSR) and low-cost carriers (LCCs) have emerged as increasingly prominent modes of intercity travel, particularly in rapidly urbanizing regions. Understanding the determinants of passengers' mode choices is essential for informing transportation policy, optimizing infrastructure investments, and enhancing the overall travel experience. This study employs a stated preference (SP) survey to investigate these determinants in two distinct urban contexts: Shanghai and Chengdu. A total of 494 valid responses were collected in Shanghai and 524 in Chengdu, capturing data on sociodemographic attributes, attitudinal dispositions, and travel-related characteristics. To analyze this dataset, we integrated machine learning techniques with the SHAP (Shapley Additive Explanations) algorithm, enabling both high predictive accuracy and interpretability. Three models—random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost)—were evaluated, with the RF model demonstrating superior performance. This model was subsequently used to interpret the relative importance of influencing factors. The findings reveal that factors associated with HSR travel, such as service frequency, ticket price, and in-vehicle travel time, play a vital role in passengers' mode choice. Regional contrasts also emerged: passengers in Shanghai exhibited a stronger preference for LCCs, while those in Chengdu were more inclined toward HSR, particularly among price-sensitive travelers. Interestingly, travelers who prioritize safety, comfort, and convenience tended to favor LCCs in both regions, suggesting a shifting perception of LCC quality and reliability. Finally, this study presents targeted recommendations for both government and operators, focusing on enhancing market transparency, maintaining fare stability, adopting region-specific strategies, and improving safety, comfort, and convenience. The findings offer theoretical insights into the mechanisms driving passengers' choices between HSR and LCCs, along with practical implications for policymaking and strategic optimization.]]></description>
      <pubDate>Wed, 18 Feb 2026 08:24:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633477</guid>
    </item>
    <item>
      <title>Optimizing driver’s discount order acceptance strategies: A policy-improved deep deterministic policy gradient framework</title>
      <link>https://trid.trb.org/View/2652808</link>
      <description><![CDATA[The rapid expansion of platform integration has emerged as an effective solution to mitigate market fragmentation by consolidating multiple ride-hailing platforms into a single application. To address heterogeneous passenger preferences, third-party integrators provide Discount Express service delivered by express drivers at lower trip fares. For the individual platform, encouraging broader participation of drivers in Discount Express services has the potential to expand the accessible demand pool and improve matching efficiency, but often at the cost of reduced profit margins. This study aims to dynamically manage drivers’ acceptance of Discount Express from the perspective of an individual platform, incorporating the spatiotemporal demand-supply patterns. The lack of historical data under the new business model necessitates online learning. However, early-stage exploration through trial and error can be costly in practice, highlighting the need for reliable early-stage performance in real-world deployment. To address these challenges, this study formulates the decision regarding the proportion of drivers accepting discount orders as a continuous control task. In response to the high stochasticity, the opaque matching mechanisms employed by third-party integrator, and the limited availability of historical data, we propose an innovative policy-improved deep deterministic policy gradient (pi-DDPG) framework. The proposed framework incorporates a refiner module to boost policy performance during the early training phase, leverages a convolutional long short-term memory network to effectively capture complex spatiotemporal patterns, and adopts a prioritized experience replay mechanism to enhance learning efficiency. A customized simulator based on a real-world dataset is developed to validate the effectiveness of the proposed pi-DDPG. Numerical experiments demonstrate that pi-DDPG achieves superior learning efficiency and significantly reduces early-stage training losses, enhancing its applicability to practical ride-hailing scenarios.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:32:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652808</guid>
    </item>
    <item>
      <title>Development adjustment of airport passenger terminals for low-cost carriers: An overview</title>
      <link>https://trid.trb.org/View/2627800</link>
      <description><![CDATA[The impact of low-cost carriers (LCCs) on operational processes in airport passenger terminals necessitates adapting existing infrastructure and organizational solutions to meet the specific demands of their business model. Core features of the LCC model prioritize cost reduction and efficiency maximization, directly shaping the operational requirements for airports. This paper provides a comprehensive analysis of LCC operations, with particular focus on the European and Croatian markets. It examines key development directions for airports with the increasing market penetration of LCCs, including optimization of existing infrastructure capacity, construction of dedicated terminals for LCCs, and investment in digital transformation and process automation. Based on established market trends and operational requirements, the essential characteristics of passenger terminals designed for LCCs are outlined to ensure competitiveness, operational efficiency, and sustainable growth of the low-cost aviation sector. This paper is based on a qualitative analysis of selected academic and sectoral sources relevant to the LCC business model and its impact on airport infrastructure and operations. The research approach includes a targeted review of scientific literature, case studies, and statistical data related to LCC presence in European and Croatian markets. Key publications were selected based on their relevance to airport terminal planning, level of service optimization, and LCC strategic requirements. Insights from these sources have been synthesised and interpreted with the aim of identifying the infrastructural and operational adjustments necessary for Croatian airports, given the growing share of low-cost carrier traffic.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627800</guid>
    </item>
  </channel>
</rss>