ACADEMIC YEAR 2012–2013
An Analytic Model of Airport Security Checkpoint Screening Times
Derek Doran, Swapna Gokhale, and Nicholas Lownes
Security checkpoints at airports across the United States are essential to prevent passengers from boarding airplanes with dangerous weapons, explosives, and other threats, but the multiple screening technologies and different speeds of passengers lead to unpredictable and sometimes long waiting times. Security agencies and airport man- agers must thus find ways to minimize checkpoint screening times without compromising the security of aviation transportation. This paper introduces an analytic model that derives the distribution of completion times for passengers through a security checkpoint given its architecture, passenger profiles, and expected service times at different checkpoint components. By varying the model’s parameters and checkpoint architecture, security agencies and airport managers can quickly understand how the end-to-end completion times of passengers are affected by policy changes and checkpoint reconfigurations. The model can also be used to forecast the performance of future checkpoint architectures utilizing new components and policies. The utility of the model is demonstrated by analyzing a prototypical security checkpoint.
Identification of Causal Paths and Prediction of Runway Incursion Risk Using Bayesian Belief Networks
Benjamin Jeffry Goodheart
In the United States and worldwide, run- way incursions are acknowledged as a critical concern for aviation safety. Nonetheless, the rate at which these events occur in the United States has steadily risen. Analyses of runway incursion causation have been made, but these are frequently limited to discrete events and do not address the dynamic interactions that lead to breaches of runway safety. This paper emphasizes the need for cross-domain methods of causation analysis applied to runway incursions in the United States. A holistic modeling technique using Bayesian belief networks to interpret causation in the presence of sparse data is out- lined, with intended application at the systems level. Further, the importance of investigating runway incursions probabilistically and incorporating information from human factors and technological and organizational perspectives is supported. A method for structuring Bayesian networks using quantitative and qualitative event analysis in conjunction with structured expert probability estimation is outlined and results are presented for propagation of evidence through the model as well as causal analysis. The model provides a dynamic, inferential platform for future evaluation of runway incursion causation. The results in part confirm what is known about runway incursion causation, but more importantly they shed light on multifaceted causal interactions in a modeling space that allows causal inference and evaluation of changes to the system in a dynamic setting. Suggestions for future research are discussed, most prominent of which is that this model allows for robust and flexible assessment of mitigation strategies within a holistic model of run- way safety.
The Role of Competitor Pricing on Multi-Airport Choice
Susan L. Hotle and Laurie A. Garrow
This paper investigates how competitors’ low fare offerings in multi-airport regions influence customers’ online search behavior at a major carrier’s website. Clickstream data from a major U.S. airline is combined with detailed information about competitors’ low fare offerings for 10 directional markets. A truncated negative binomial model was used to predict the number of searches on the carrier’s website as a function of low fare offerings in the same airport pair, as well as competing airport pairs in the region. The number of searches was found to decrease as the difference between the carrier’s lowest fare and competitors’ lowest fare increases. Trip characteristics, however, were found to have a larger impact on search behavior than the fare variables. Overall searches on the carrier’s website were limited, with less than 5% of customers searching for fares across multiple airports. The findings pro- vide insights into the role of competitor pricing on multi-airport choice, as it relates to customers’ online search behaviors.
Congestion Mitigation at JFK: The Potential of Schedule Coordination
Alexandre Jacquillat and Amedeo R. Odoni
With the large growth in air traffic experienced over past decades, airport capacity has become an increasingly costly constraint. Flight delays reached record-high levels in 2007, with a nationwide impact estimated at over $30 billion for that calendar year. At airports where capacity expansion and improvements in operational efficiency are not feasible, congestion could be mitigated in the short- and medium-term through the implementation of schedule coordination mechanisms. Such measures essentially reduce peak-hour scheduling levels. On the other hand, they have also been criticized for the constraints they might create on airline scheduling. This paper presents a schedule coordination model that reduces flight delays while minimizing interference with airlines’ scheduling, then applies the model to one of the most congested U.S. airports, John F. Kennedy (JFK) International Air- port. The analysis suggests that it may be possible to reduce peak arrival and departure delays by over 30% and 50%, respectively, without eliminating any flights, any aircraft connections, and any passenger connections, and without modifying the scheduled time of any flight by more than 30 minutes. This underscores the potential of schedule coordination as a means of achieving substantial congestion cost savings at the busiest U.S. airports. The paper dis- cusses the opportunities and challenges associated with the implementation of such a mechanism.
Methods for Curbing the Exemption Bias in Ground Delay Programs Through Speed Control
James C. Jones and David J. Lovell
Ground delay programs allow flights originating beyond a specified distance to become exempt from any delay imposed by the program. This exemption leads to a biased allocation that favors longer flights over shorter flights and alters an otherwise fair allocation. This paper presents two algorithms to reduce the exemption bias through speed control. The first algorithm attempts to assign the maximum possible delay achievable through speed control to the exempt flights. The second algorithm begins by prescribing the maximum possible delay to exempt flights but works to improve on this allocation by acting to fill holes in the schedule with speed controlled exempt flights whenever possible. Both algorithms demonstrated considerable delay transfer relative to distance-based ration-by-schedule; however, the second algorithm also revealed some ability to improve throughput.
Prediction of Terminal-Area Weather Penetration Based on Operational Factors
Yi-Hsin Lin and Hamsa Balakrishnan
Convective weather is known to reduce airspace capacity, but the extent of the impact is not well understood. Understanding how weather impacts terminal are capacity is essential for quantifying the uncertainty in weather forecasts, determining how accurately the weather needs to be forecast for developing an optimal mitigation strategy. Prior research has focused on the overlap between convective weather cells and air routes, but has not sufficiently analyzed the differences that arise due to factors such as aircraft types and pilot behavior. This paper examines the interactions between convective weather and aircraft trajectories in the arrival airspace surrounding Chicago O’Hare International Airport. Case studies based on operational data are used to determine potentially relevant operational factors, and a predictive model is built using these factors to forecast if a flight will pass through hazardous weather. The results of the analysis suggest that these operational factors are secondary compared to the weather itself in determining whether a pilot will deviate from or penetrate hazardous weather.
Ground Delay Program Performance Evaluation
Yi Liu and Mark Hansen
GDPs are frequently used to keep the U.S. air transportation system safe and efficient. Most previous research on GDPs has focused on optimal design and implementation but retrospective performance evaluation has garnered little attention. This research fills this gap by identifying GDP performance criteria, developing associated performance metrics, and evaluating the GDP performance metrics across airports and over time. GDP performance criteria are established and associated performance metrics are specified for five performance goals: capacity utilization, efficiency, predictability, equity and flexibility. By defining multiple performance metrics, this research enables FAA traffic managers and flight operators to review GDP performance after the fact in a comprehensive way and uncover GDP performance trends across airports and over time. Using ADL and ASPM data, historical GDP performance is assessed for SFO and EWR for 2006 and 2011. For both air- ports, capacity utilization and efficiency scores are high, on average, reflecting the importance that the FAA and flight operator community attach to making effective use of available capacity and keeping air transport efficient and safe. In contrast, predict- ability performance is weaker and more variable. Lack of consensus on how predictability should be measured or valued could have diminished the importance of predictability in GDP decision making. On average, SFO GDPs have higher capacity utilization and predictability, whereas EWR GDPs are more efficient, equitable, and flexible. Comparing results for 2006 and 2011, GDPs were found to be more predictable, but capacity was less effectively utilized in the later year.
Low-Hanging Fruit? The Costs and Benefits of Reducing Fuel Burn and Emissions from Taxiing Aircraft
Aircraft are powered by their main engines while taxiing. This paper estimates the cost and emissions reductions that could be achieved by using tugs, or an electric motor embedded in the landing gear, to propel the aircraft on the ground. The use of tugs would result in a savings of $20 per tonne of carbon dioxide emissions avoided, if the measure were adopted for all domestic flights. Estimates of average net savings for airlines vary from $100 per flight at JFK to a loss of $160 per flight at Honolulu. Electric taxi would save between $30 and $240 per tonne of carbon dioxide emissions avoided. Either approach could reduce carbon dioxide emissions from domestic flights in the United States by about
1.5 million tonnes each year, or about 1.1% of the total emissions in 2006. If the switch were limited to large narrow body aircraft on domestic service at the busiest airports in the United States, the total reduction in emissions would be 0.5 million tonnes of carbon dioxide annually, accompanied by a savings of $100 per tonne. Air quality benefits associated with lower main engine use were monetized using the Air Pollution Emission Experiments and Policy (APEEP) model, and ranged from over $500 per flight in the New York area to just over $20 per flight in the Dallas/Fort Worth area. The analysis also demonstrates that emissions reductions from different interventions (e.g., single-engine taxi and the use of tugs) are often not independent of each other, and therefore cannot be combined in a simple way.
Exploring the Use of Egocentric Online Social Network Data to Characterize Individual Air Travel Behavior
Thomas A. Wall, Gregory S. Macfarlane, and Kari Edison Watkins
The rapid growth of online social networking over the past decade has generated tremendous amounts of data about individuals and their social relationships. Recent research studies investigating social relationships and travel behavior have sought connections between individuals’ social networks and social-related travel; however, the authors’ review of the literature revealed none that has pursued the use of online social networking data to do so. This paper explores the use of online social net- work data in characterizing individuals’ air travel behavior. Data were collected using a web-based survey that gathered information about individuals’ air travel history and online social network information, specifically participants’ Facebook networks. The data were then analyzed to address a series of hypotheses about the association between online social network characteristics (specifically Face- book) and air travel behavior; in particular, travel distance, leisure-related travel, and trip generation. This study found a positive relationship between the size and distribution of individuals’ Facebook social networks and their engagement in air travel, and also the odds that their air travel would be leisure-related or include a leisure component.
Airport Capacity Enhancement and Flight Predictability
Amber Woodburn and Megan Ryerson
Justifications for airport capacity enhancements are often framed in terms of delay reductions, but improvements to flight predictability also offer substantial benefit to the health of the aviation system. This paper defines predictability as block time adherence and measured as the difference between scheduled and actual block time. This research quantifies, using historical data, the impact of one airport’s infrastructure capacity enhancement on flight predictability. A case study utilizing statistical methodologies, including cluster analysis of NAS days and quantile regression of flights, was used to identify how deployment of the fifth runway at Hartsfield-Jackson Atlanta International Airport impacted arrival flight predictability. In four scenarios, defined according to the level of national airspace strain and terminal airspace weather disruption, inclusion of the fifth runway in the runway con- figuration was associated with either predictability improvement or predictability degradation. If broad gains are to be made in predictability improvements for the national airspace, then capacity enhancements may offer a limited contribution to what must be a multifaceted solution.