This paper summarizes research trends and opportunities in the area of managing air transportation demand and capacity. Capacity constraints and resulting congestion and low schedule reliability currently impose large costs on airlines and their passengers. Significant capacity increases that would solve these problems are not expected in the near-or medium-term. This paper outlines first a number of directions for effecting improvement through marginal capacity increases and better management of demand and available capacity. It then describes strategic initiatives that airlines and civil aviation authorities might undertake over time horizons of months to years as well as tactical measures that may be adopted on a daily basis in response to dynamic, ''real-time'' developments like poor weather or schedule disruptions. Research challenges in these areas are identified and classified in terms of specifying, allocating, and utilizing capacity. The first two categories reflect challenges faced by infrastructure providers, the last category challenges faced by airlines.
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 discrete choice 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.Draft completed August 2 nd , 2010. IntroductionOver the past two years, flight and passenger delays have been on the decline due to reduced demand for air travel as a result of the recent economic crisis. As the economy rebounds, demand for air travel in the United States is also expected to recover (Tomer & Puentes, 2009). Thus, after a brief reprieve, the U.S.will once again face a looming transportation crisis due to air traffic congestion. In calendar year 2007, the last year of peak air travel demand before the economic downturn, flight delays were estimated to methodologies, the huge discrepancy between these estimates suggests the need for a more transparent and rigorous approach to measuring passenger delays. Accurately estimating passenger delays is important not only as a means to understand system performance, but also to motivate policy and investment decisions for the National Air Transportation System.Another important consideration is that neither of the passenger delay cost estimates listed above includes the delays associated with itinerary disruptions, such as missed connections or cancellations. Analysis performed by Bratu and Barnhart (2005) suggests that itinerary disruptions and the associated delays represent a significant component of passenger delays. Their analysis was performed using one month of proprietary passenger booking data from a legacy carrier. The challenge in extending this analysis system-wide is that publicly available data sources do not contain passenger itinerary flows. For example, on a given day, there is no way to determine how many passengers planned to take the 7:05amAmerican Airlines flight from Boston Logan (BOS) to Chicago O'Hare (ORD) followed by the 11:15amflight from Chicago O'Hare (ORD) to Los Angeles (LAX), or even the number of non-stop passengers on each of these flights. Instead, the passenger flow data that is publicly available is aggregated over time, either monthly or quarterly, and reports flows based only on...
Accurate calibration of demand and supply simulators within a Dynamic Traffic Assignment (DTA) system is critical for the provision of consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as Automatic Vehicle Identification (AVI) technology provide a rich source of disaggregate traffic data. This thesis presents a methodology for calibration of demand and supply model parameters using travel time measurements obtained from these emerging traffic sensing technologies.The calibration problem has been formulated in two different frameworks, viz. in a statespace framework and in a stochastic optimization framework. Three different algorithms are used for solving the calibration problem, a gradient approximation based path search method (SPSA), a random search meta-heuristic (GA) and a Monte-Carlo simulation based technique (Particle Filter). The methodology is first tested using a small synthetic study network to illustrate its effectiveness. Later the methodology is applied to a real traffic network in the Lower Westchester County region in New York to demonstrate its scalability. The estimation results are tested using a calibrated Microscopic Traffic Simulator (MITSIMLab). The results are compared to the base case of calibration using only the conventional point sensor data. The results indicate that the utilization of AVI data significantly improves the calibration accuracy.
Demand often exceeds capacity at the 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.
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