<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>A Static Model in Single Leg Flight Airline Revenue Management</title>
      <link>https://trid.trb.org/View/1458974</link>
      <description><![CDATA[Static models on single leg airline revenue management generally consider booking limits or protection limits as the main decision variables to control reservation requests. In the current paper, the authors provide an alternative framework in which the decision variables are the closing times of fare classes. In a continuous time model with nonhomogeneous Poisson arrivals, cancellations, and no-shows, the authors study the problem of finding optimal closing times to maximize the expected net revenue from a given flight. The authors analyze the value function, point out some easy cases, and bring an easily implementable dynamic programming based solution method. The authors also illustrate this method on some numerical examples.]]></description>
      <pubDate>Mon, 01 May 2017 09:36:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1458974</guid>
    </item>
    <item>
      <title>Congestion spill effects of Heathrow and Frankfurt airports on connection traffic in European and Gulf hub airports</title>
      <link>https://trid.trb.org/View/1422574</link>
      <description><![CDATA[The authors develop two stage fixed-effects single-spill and double-spill models for congestion connection spills of London Heathrow and Frankfurt airports on 9 hub airports in Europe and the Gulf. The authors' panel data covers connection traffic from 1997 to 2013 for Heathrow and 1997 to 2011 for Frankfurt. The single-spill results support strongly that the connection spills from Heathrow’s capacity limitations do strengthen competing hub airports of major alliance groups and to a lesser degree one Gulf hub. The double-spill model for Heathrow and Frankfurt shows nearly asymmetric overall spill characteristics between the two airports. The authors' results underline the influence of airline network strategies on congestion spills as European airline networks are shaped by alliances and umbrella mergers. Thus, the airline network perspective in airport capacity expansion decisions needs to play a greater role, as indicated by the authors' asymmetric results for overall spill effects between Heathrow and Frankfurt airports.]]></description>
      <pubDate>Fri, 23 Sep 2016 11:17:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1422574</guid>
    </item>
    <item>
      <title>An airline connection builder using maximum connection lag with greedy parameter selection</title>
      <link>https://trid.trb.org/View/1302778</link>
      <description><![CDATA[This paper introduces a methodology for designing an airline connection builder (CB) and adjusting its parameter settings. The objective of the proposed CB is to construct relevant connections that attract passenger demand while avoiding operationally infeasible and commercially irrelevant connections. Using worldwide marketing information data transfer (MIDT) booking data, the authors examined the sensitivity of CB results to the setting of the standard CB parameters maximum connection time and geographical detour. The authors demonstrated that CB performance can be increased by replacing these two parameters with connection lag, a measure that combines the impact of connection time with geographical detour on the total travel time of a given connection. The authors also found that the willingness of a passenger to book slower connections and the relationship between the number of passenger bookings and generated connections strongly depends on the origin and destination (O&D) distance. Better results can thus be obtained by greedily determining the distance-specific CB parameter settings. The greedy adjustment of parameter settings reduces the number of unattractive connections generated, while keeping the number of covered passenger bookings high.]]></description>
      <pubDate>Thu, 24 Apr 2014 11:58:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1302778</guid>
    </item>
    <item>
      <title>Understanding air travellers' trade-offs between connecting flights and surface access characteristics</title>
      <link>https://trid.trb.org/View/1286111</link>
      <description><![CDATA[This paper reports on a study which seeks to improve understanding of how people choose between different kinds of flight at competing airports, and how their choices are affected by access conditions. In particular, using stated choice data collected in Scotland, it investigates whether improving surface access to regional airports that are in relatively close proximity to one another (Glasgow and Edinburgh) leads people to avoid taking indirect flights from their nearest airport in favour of direct flights from an alternative airport. In line with expectations, our estimation results from Cross-Nested Logit models show strong aversion to connecting flights, resulting in a willingness to either pay higher fares for direct flights or accept non-trivial increases in access time. For the latter, even without the potential new direct rail link between the two airports, current access times are such that a scenario where direct flights were only available at the non-home airport, a substantial share of passengers would choose to travel from the alternative airport.]]></description>
      <pubDate>Mon, 27 Jan 2014 10:45:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1286111</guid>
    </item>
    <item>
      <title>Integrating Slot Exchange, Safety, Capacity, and Equity Mechanisms Within an Airspace Flow Program</title>
      <link>https://trid.trb.org/View/1103102</link>
      <description><![CDATA[The authors augment an airspace planning and collaborative decision-making model (APCDM) to study an airspace flow program in a weather-related disruption context in this paper. In addition to selecting among alternative flight plans for affected flights, the proposed model integrates multiple ground delay program (GDP) induced slot exchange mechanisms. This should allow airlines to use mediated assigned slot bartering and simultaneous sector workload and airspace conflict related issue considerations, in addition to overall involved airline equity concerns regarding accepted slot trades and flight plans, to improve flight efficiencies. APCDM enhancement includes: slot exchange trade offer selection as suggested by airlines using their GDP allotted slots as a basis; continuing flight connections; and several concepts involving alternative equity. Realistic data service from the Federal Aviation Administration provide enhanced traffic management system data is used to develop and test both full and light versions of this model.]]></description>
      <pubDate>Tue, 21 Jun 2011 12:00:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1103102</guid>
    </item>
    <item>
      <title>Addressing and Benchmarking Variations in Airport Demand in the U.S. Domestic Market</title>
      <link>https://trid.trb.org/View/1091804</link>
      <description><![CDATA[A framework quantifies and benchmarks the factors that contribute to airport demand variations for the 100 largest airports in the U.S. domestic market. First, a time series modeling approach is proposed to address and quantify factors that contribute to airport demand variations. The airport demand variation is represented by the quarterly percentile change in the number of passengers flying to and from the airport. The quarterly demand data for each airport are used, with local traffic of the airport separated from its connecting traffic. This separation allows better analysis of demand variations and avoids a portfolio effect on variation attenuation. Factors considered in the modeling approach include seasonality, financial conditions, and incidents. Next, the parameters of the estimated models for the 100 largest airports in the U.S. domestic markets are analyzed to gain more insight into how these parameters vary by airport. Several airport characteristics are considered to explain the parameter differences, which include demand level, number of operations, and airport geographical locations.]]></description>
      <pubDate>Thu, 21 Apr 2011 07:26:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1091804</guid>
    </item>
    <item>
      <title>Taxing for Takeoff: Estimating Airport Tax Incidence Through Natural Experiments</title>
      <link>https://trid.trb.org/View/910232</link>
      <description><![CDATA[We view the varied start dates of airport departure taxes as a series of natural experiments, in each of which plane tickets are subject to different levels of taxes.  We show that airlines are able to raise fares of nonstop tickets by more than the taxes applied while leaving fares of connecting tickets little changed.  These results provide empirical support for two propositions in economic theory: First, suppliers may ``over-shift'' the tax burden onto consumers under imperfect competition.  Second, when two products are taxed, the tax-inclusive price is higher for the product that has a less elastic demand (i.e., nonstop tickets).  We argue that there are efficiency and equity gains to be made by revising the current tax schedule.  One potential adjustment is to allow some taxes to vary by demand and other direct costs; the revenues can then be used for investments that lower long-run costs and for subsidies to airports and airlines serving smaller communities.]]></description>
      <pubDate>Sun, 30 May 2010 07:44:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/910232</guid>
    </item>
    <item>
      <title>Depeaking Strategies for Improving Airport Ground Operations Productivity at Midsize Hubs</title>
      <link>https://trid.trb.org/View/880608</link>
      <description><![CDATA[Today, most large airlines operate using a hub-and-spoke strategy. At hub airports, the carriers attempt to optimize their flight schedules to minimize passenger transfer times for connecting flights. The result is a high number of flight arrivals and departures during the peak periods but only a few movements in off-peak periods. This imbalance means that infrastructure and personnel are used inefficiently. Depeaking, which consists of spreading flights more evenly throughout the day, allows airlines and ground handlers to use their resources more efficiently. Depeaking can reduce energy consumption by reducing airport congestion. Although depeaking has been applied successfully at major hubs to relieve congestion constraints, it has not been applied specifically to reduce costs and energy use. Therefore, the goal of this study was to evaluate the cost savings potential of depeaking. The focus was on cost savings for the ground handler, because obtaining airline data was difficult, given the highly competitive state of the industry. Several depeaking scenarios were evaluated with data from Zurich Airport (Switzerland). The research shows that depeaking reduces extreme workload peaks, enables staff and equipment to be used more efficiently and thereby reduces costs. The results for Zurich Airport show that a small amount of schedule adjustment can reduce ground handling costs by approximately 8%. Under the maximum schedule adjustment scenario, depeaking could reduce costs by up to 20%. The research can be extended to include additional peak-oriented costs such as airline company costs and costs of energy, as well as revenue impacts of reduced flight connectivity.]]></description>
      <pubDate>Thu, 19 Feb 2009 14:43:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/880608</guid>
    </item>
    <item>
      <title>A New Methodology to Measure Hubbing Practices in Airline Networks: Avoiding Common Pitfalls</title>
      <link>https://trid.trb.org/View/876413</link>
      <description><![CDATA[Benefits and costs associated to hubbing practices of airlines are still subject to much debate. In previous literature, some standard spatial concentration indices have been proposed to measure it. However, this paper will show that these indices are “ill-defined” because they do not take into account the salient characteristic of hubbing: connecting passengers. The purpose of this paper is to present a new methodology which avoids the pitfalls of other methods. The new methodology also analyzes the level of concentration of the connecting passengers studying two different dimensions: hub airports and routes. Finally, the paper applies the methodology to some US carriers.]]></description>
      <pubDate>Tue, 30 Dec 2008 12:32:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/876413</guid>
    </item>
    <item>
      <title>Air Travel Consumer Protection: Metric for Passenger On-Time Performance</title>
      <link>https://trid.trb.org/View/801845</link>
      <description><![CDATA[The raison d’être for the national air transportation system (ATS) is the movement of passengers and cargo. Thus, passenger trip time performance is positively correlated with passenger satisfaction, airfare elasticity, and airline profits. Regulatory consumer information available to airline passengers provides measures of trip performance by using the percentage of on-time flights or on-time percentage (OTP) (e.g., 15-OTP metric). Researchers have shown that these flight-based metrics are poor proxies for passenger trip time performance. First, these metrics do not include the trip delays accrued by passengers rebooked because of canceled flights (which account for 40% of the overall passenger trip delays). Second, the metrics do not quantify the magnitude of the delay (only the likelihood) and thus fail to provide the consumer with a useful assessment of the impact of a delay (such as missed connections on next mode of transportation). A new consumer protection metric, expected value of passenger trip delay (EV-PTD), is described; it accounts for (a) canceled flights and (b) both the probability of delay and the magnitude of the delay. The EV-PTD for all 1,030 routes between 35 Operational Evolution Plan airports in 2005 ranged from 11.5 min (best) to 155 min (worst). The average route EV-PTD was 35 min. By treating passenger trip delay as a random variable it can be shown that the transportation process is not a fair game and that passengers and service providers (e.g., airlines, air traffic control, airports) cannot beat the system until the variance is significantly reduced. The implications of these results and the use of the EV-PTD metric by consumers for purchasing tickets and for consumer protection are discussed.]]></description>
      <pubDate>Tue, 08 May 2007 12:42:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/801845</guid>
    </item>
    <item>
      <title>Planning for Robust Airline Operations: Optimizing Aircraft Routings and Flight Departure Times to Minimize Passenger Disruptions</title>
      <link>https://trid.trb.org/View/780781</link>
      <description><![CDATA[Airline schedules are typically developed assuming every flight will depart and arrive as planned, a scenario that rarely happens.  Airlines incur significant expenses because of these flight delays, schedule disruptions, and passenger delays.  A more robust plan can reduce the occurrence and impact of these delays, thereby reducing costs.  This paper presents two new approaches to minimize passenger disruptions, one involving aircraft routing and the other involving retiming flight departure times.  Because aircraft usually fly a sequence of flight legs, a delay in one leg propagates through the system and causes further delays downstream, and the authors suggest a new approach by intelligently routing aircraft using an algorithmic approach.  The problem is formulated as a mixed-integer programming problem with stochastically generated inputs.  Data from a major U.S. airline show that this approach can reduce delay propagation, improve on-time performance, and reduce passenger disruptions.  The second approach involves passengers who miss connecting flights due to insufficient connection time, and the authors show that retiming flight departures within a small time window can significantly reduce those missed connections without significantly increasing airline costs.]]></description>
      <pubDate>Fri, 28 Apr 2006 08:11:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/780781</guid>
    </item>
    <item>
      <title>Evolution of U.S. Domestic Airline Route Networks Since 1990</title>
      <link>https://trid.trb.org/View/777363</link>
      <description><![CDATA[This paper examines how the networks of both legacy airlines and low-cost carriers evolved and adjusted to the changing competitive and economic environment between 1990 and 2003. The route networks of AirTran, America West, American, Continental, Delta, JetBlue, Northwest, Southwest, United, and US Airways are examined. Several measures of operational characteristics are developed and applied to the principal hubs and other dominant airports of each of these carriers. Particular attention is paid to the strategic use of regional jets operated by affiliated carriers and the extent to which passengers used connecting flights as opposed to direct flights. The analysis focuses on how these airlines adapted their use of hubs and route networks during periods of economic growth and recession and how they adapted to other changes in the competitive environment. The legacy airlines have not backed away from the traditional hub-and-spoke network, nor is there evidence of reduced emphasis on connecting traffic. Instead, the dominant change was a dramatic increase in the role of regional jets flown by affiliated carriers in their primary hub airports. The use of regional jets allowed the major airlines to increase the number of aircraft departures and the number of cities served from their hubs between 2000 and 2003 when faced with a reduced number of passengers.]]></description>
      <pubDate>Fri, 28 Apr 2006 08:08:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/777363</guid>
    </item>
    <item>
      <title>HOW DO CARRIERS PRICE CONNECTING FLIGHTS? EVIDENCE FROM INTERCONTINENTAL FLIGHTS FROM EUROPE</title>
      <link>https://trid.trb.org/View/721507</link>
      <description><![CDATA[The hub-and-spoke (HS) networks employed by all large carriers require many travelers to use connecting flights.  This paper examines the price formation of connecting flights by focusing on the fares of connecting intercontinental flights with one stop and at least one competing direct flight.  A simple model of an HS network is developed, with imperfect substitutability between direct and indirect flights and Cournot competition on every route in the network.  The model is tested empirically, using reported fares for flights from seven European origins, including the five major airports in Europe, to five non-European destinations.  Results indicate that the fare of a connecting flight can be expressed as the weighted average of the fare of its direct competitor, the travel times of both flights, and the elements of its own monopoly fare.  The weight of the direct fare decreases as product diversity (measured by the relative extra travel time involved in the indirect flight) increases.  Findings also suggest that a flight lasting about twice as long as its direct competitor can no longer be considered a substitute for that competitor.]]></description>
      <pubDate>Sat, 27 Jul 2002 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/721507</guid>
    </item>
    <item>
      <title>AIRLINE SCHEDULING. AIRLINE PRACTICES IN ESTABLISHING AND MAINTAINING CONNECTING TIMES</title>
      <link>https://trid.trb.org/View/287001</link>
      <description><![CDATA[This report examines how airlines decide how much time to allow for passengers and their baggage to transfer to connecting flights.  It also reviews the steps that airlines take to minimize the likelihood of missed connections. Specific objectives include:  how airlines establish, publicize, and enforce connecting times between flights; whether current scheduled connecting times are too short and, therefore, responsible for growing consumer complaints about missed connections; if there are carrier policies regarding (1) holding outgoing flights when incoming connecting flights are late and (2) keeping spare aircraft in reserve to replace those that develop mechanical problems; and if there are any unusual circumstances that would increase the likelihood of missing connections at the three airports reviewed.]]></description>
      <pubDate>Sat, 31 Dec 1988 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/287001</guid>
    </item>
  </channel>
</rss>