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Defining air traffic management as the tools, procedures, and systems employed to ensure safe and efficient operation of air transportation systems, an important objective of future air traffic management systems is to support airline business objectives, subject to ensuring safety and security. Under the current model for designing air traffic management initiatives, the central authority overseeing and regulating air traffic management in a region makes trade-offs between specified performance criteria. The research presented in this paper aims instead to allow the airline community to set performance goals and thus make trade-offs between different performance criteria directly, before specific air traffic management strategies are determined. We propose several approaches for collecting inputs from airlines in a systematic way and for combining these airline inputs into implementable air traffic management initiatives. These include variants of averaging, voting, and ranking mechanisms. We also propose multiple criteria for evaluating the effectiveness of each approach, including Pareto optimality, airline profitability, system optimality, equity, and truthfulness of airline inputs. We apply a game-theoretic approach to examine the potential for strategic (gaming) behavior by airlines. We offer a broad evaluation of each approach, first by providing some theoretical insights, and then by simulating each of the approaches for a generic system using Monte Carlo methods, sampling values for input parameters from a wide range. We also provide an indication of how the approaches might perform in a real system by simulating ground delay programs at two airports in the New York City area. We first apply a simplified model that simulates the process of selecting only planned end times of a ground delay program, using Monte Carlo methods. Next, we apply a more detailed model that simulates the process of selecting planned end times and reduced airport arrival rates. Finally, we characterize the effectiveness of each of the considered approaches on the proposed criteria and identify the most desirable approaches. We conclude that voting schemes, which score highly on all criteria (including airline profitability, system optimality, and equity), represent the most promising approaches (among those considered) to elicit airline preferences, thereby allowing the central authority to design air traffic management initiatives that optimize system performance while respecting the objectives of airlines.

Although one-way carsharing is suitable for more trip purposes than round-trip carsharing, many companies in the world operate only in the round-trip market. In this paper, we develop a method that optimizes the design of a one-way carsharing service between selected origin–destination pairs of an existing round-trip carsharing system. The goal is to supplement the established round-trip services with new one-way services and increase profitability. We develop an integer programming model to select the set of new one-way services and apply it to the case study of Boston, USA, considering only trips with one endpoint at a station in the round-trip Zipcar service network and the other endpoint at Logan Airport. The airport was chosen as a necessary endpoint for a one-way service because it is a very significant trip generator for which the round-trip carsharing is not suitable. Results show that these supplemental one-way services could be profitable. Enabling relocation operations between the existing round-trip stations and the Airport greatly improves the demand effectively satisfied, leads to an acceptable airport station size (in terms of the number of parking spots required), and is profitable; however, these benefits come with the need to manage relocation operations. Keywords: Round-trip carsharing; One-way carsharing; Integer programming model

Demand often exceeds capacity at congested airports. Airline frequency competition is partially responsible for the growing demand for airport resources. We propose a game-theoretic model for airline frequency competition under slot constraints. The model is solved to obtain a Nash equilibrium using a successive optimizations approach, wherein individual optimizations are performed using a dynamic programming-based technique. The model predictions are validated against actual frequency data, with the results indicating a close fit to reality. We use the model to evaluate different strategic slot allocation schemes from the perspectives of the airlines and the passengers. The most significant result of this research shows that a small reduction in the total number of allocated slots translates into a substantial reduction in flight and passenger delays and also a considerable improvement in airlines' profits.

The collaborative decision-making (CDM) framework introduced into ground delay programs in the late 1990s is an integral component of FAA's traffic flow management (TFM) procedures. CDM allows FAA to act as a mediator when managing TFM programs, transferring as much decision making as possible to the individual airlines. Although this approach has been highly successful in practice, it creates a new question for the research community: How should proposed enhancements to TFM be evaluated in a CDM environment? A sequential evaluation procedure, developed in this paper, addresses this question. The procedure includes airline disruption responses and a quasi-compression operation, attempting to mimic the three-stage CDM process. To model airline disruption responses, an integer optimization model was developed to balance operational and passenger considerations in determining which flights to cancel, swap, or delay. The value of this procedure is demonstrated by analyzing an optimization-based TFM approach in the CDM environment.

Long in-vehicle travel times resulting from frequent stops make bus service an unattractive choice for many commuters. Limited-stop bus services however have the advantage of shorter in-vehicle times experienced by passengers. In this work, we seek to modify a given bus service by optimally reassigning some number of bus trips, as opposed to providing additional trips, to operate a limited-stop service. We propose an optimization model to determine a limited-stop service route to be operated in parallel with the local service and its associated frequency to maximize total user welfare. A few theoretical properties of the model are established and used to develop a solution approach. As a proof of concept, we present numerical results obtained using real-world data together with comprehensive discussions of solution quality, computational times and the model’s sensitivity to different parameters. Finally, we solve the optimization model for 178 real-world bus routes with different characteristics in order to demonstrate the impacts of some key attributes on potential benefits of limited-stop services., Singapore-MIT Alliance for Research and Technology (SMART) (Future Urban Mobility research program), Singapore. National Research Foundation

Many of the existing methods for evaluating an airline's on-time performance are based on flight-centric measures of delay. However, recent research has demonstrated that passenger delays depend on many factors in addition to flight delays. For instance, significant passenger delays result from flight cancellations and missed connections, which themselves depend on a significant number of factors. Unfortunately, lack of publicly available passenger travel data has made it difficult for researchers to explore the nature of these relationships. In this paper, we develop methodologies to model historical travel and delays for U.S. domestic passengers. We develop a multinomial logit model for estimating historical passenger travel and extend a previously developed greedy reaccommodation heuristic for estimating the resulting passenger delays. We report and analyze the estimated passenger delays for calendar year 2007, developing insights into factors that affect the performance of the National Air Transportation System in the United States., United States. Federal Aviation Administration. National Center for Excellence for Aviation Operations Research

In this paper we present a novel approach addressing airline delays and recovery. Airline schedule recovery involves making decisions during operations to minimize additional operating costs while getting back on schedule as quickly as possible. The mechanisms used include aircraft swaps, flight cancellations, crew swaps, reserve crews, and passenger rebookings. In this context, we introduce another mechanism, namely flight planning that enables flight speed changes. Flight planning is the process of determining flight plan(s) specifying the route of a flight, its speed, and its associated fuel burn. Our key idea in integrating flight planning and disruption management is to adjust the speeds of flights during operations, trading off flying time (and along with it, block time) and fuel burn; in combination with existing mechanisms, such as flight holds. Our goal is striking the right balance of fuel costs and passenger-related delay costs incurred by the airline. We present both exact and approximate models for integrated aircraft and passenger recovery with flight planning. From computational experiments on data provided by a European airline, we estimate that the ability of our approach to control (decrease or increase) flying time by trading off with fuel burn, as well as to hold downstream flights, results in reductions in passenger disruptions by approximately 66%–83%, accompanied by small increases in fuel burn of 0.152%–0.155% and a total cost savings of approximately 5.7%–5.9% for the airline, may be achieved compared to baseline approaches typically used in practice. We discuss the relative benefits of two mechanisms studied—specifically, flight speed changes and intentionally holding flight departures, and show significant synergies in applying these mechanisms. The results, compared with recovery without integrated flight planning, are because of increased swap possibilities during recovery, decreased numbers of flight cancellations, and fewer disruptions to passengers.

A myriad of uncontrollable factors in airline operations makes delays and disruptions unavoidable. To limit complexity, most conventional scheduling models, however, ignore the occurrence of unplanned events during operations. This leads to schedules that are vulnerable to delays and disruptions. In this work, we propose a flight schedule adjustment model that strategically re-allocates existing schedule slack to achieve a more robust schedule. Using data from an international carrier, we evaluate the resulting schedules using various performance metrics, including delay propagation and passenger delays. The results show that minor schedule adjustments to the original schedule can significantly improve overall schedule performance. In addition to the modeling contribution, we provide managerial insights obtained through extensive computational experiments. Unlike earlier works on slack re-allocation that focus primarily on demonstrating the effectiveness of the methods, our paper is, to our knowledge, the first one that also addresses a fundamental question pertaining to how airline characteristics and operations influence the strategy for robust scheduling., Jeppesen (Firm)