ACADEMIC YEAR 2015–2016
Enhancing Safety Risk Management with Quantitative Measures
Boris Claros, Carlos Sun, and Praveen Edara
The airfield in hub airports is a place where a large number of activities occur involving a wide range of participants. These participants include air- craft that can carry hundreds of passengers, ground vehicles, and workers. The Safety Management System (SMS) is the FAA’s approach to systematically manage aviation safety. A major component of SMS is Safety Risk Management (SRM), which entails the analysis, assessment, and control of safety risks, including risks on the airfield. The current SRM has few specific safety models to estimate likelihood or frequency of risks. This paper presents an example for the development and incorporation of safety models into the SRM. Specifically, safety models for runway incursion are discussed which utilize the variables of total and general aviation operations, length of runway by type (single, parallel, crossing, or mixed), number of taxiway intersections, snow- fall, precipitation, number of hotspots, and construction activity. The categorization and processing of data was significant because each variable used could take on multiple forms (e.g., the runway system can be quantified in many ways), and some types of data involved the review of airfield diagrams. The data used were from 137 hub airports from the U.S. for the years 2009 to 2014. For modeling, the negative multinomial distribution was used because it was found suitable for representing over-dispersed data such as runway incursion frequency. The performance of the models was assessed using the goodness-of-fit measures of log-likelihood, over- dispersion, and cumulative residual plots. The models were developed for different runway incursion severity categories (A, B, C, D, and total) and type of surface events (operational incident [OI], pilot deviation [PD], and vehicle pedestrian deviation [VPD]). The safety modeling approach presented in this paper can serve as a foundation for the development of other safety models that can be integrated into the SRM to enable the quantitative analysis of safety risks.
Data-Driven Ground Delay Program Planning
Alexander Estes and Michael Ball
We provide a model-based approach to planning ground delay programs. Previous research on automated planning of ground delay programs has involved the use of mathematical programming techniques. We propose a data-driven method that models the problem of choosing a traffic management initiative using the multi-armed bandit decision problem framework. This approach makes more complete use of the available data, and suggestions made by this procedure can be shown along with data that informed the decision. This allows decision makers to more easily evaluate suggested decisions. We provide simulations of our procedure based on data from Newark International Airport to evaluate its effectiveness.
Representative Weather Profiles in Airport Planning
Sreeta Gorripaty and Mark Hansen
Given the expensive and protracted nature of airport improvement projects, it is important to plan them using careful forecasting and reliable estimates of demand and capacity. The processes aimed at improving airports and satisfying the time and financial constraints constitute airport planning. In airport planning, when considering the limitations of an airport and potential improvements, one needs to consider the daily capacity variations due to local weather and how those compare with the air- port’s demand profile. The current representation of capacity in the planning process does not capture this variation and dependence on local weather. A weather profile of a given day is defined as a series of weather conditions observed or predicted for every hour of that day. Weather profiles can be translated to a series of hourly capacity estimates using analytical and empirical models. When combined with representative demand profiles, queuing delay can be estimated. To capture the capacity variations at an airport, it is very resource intensive and impractical to represent every day using its own weather profile in planning processes. It would be useful to find a few representative weather profiles from airport data that strike a balance between capturing weather variability and reducing complexity of the planning process. The objective of this research is to develop a method for identifying representative weather profiles, and demonstrate this method for selected airports in the U.S. Our results indicate that, for the three airports considered, less than 10 representative weather profiles are sufficient to yield accurate delay estimates.
Electric Energy Demand Forecasting for Airport Buildings Based on Flight Information for Demand-Response Applications
Minkyung Kang, Burcu Akinci, and Mario Bergés
Airports have strong incentives for taking advantage of demand-response (DR) opportunities considering their large energy footprint and continuous operations. An accurate energy baseline model, which calculates what the power demand would have been without curtailment, is crucial for realizing these opportunities as it assesses the DR potential and the effectiveness of DR strategies. Such baseline models are specific to building types and operational characteristics, and airports need special attention due to their varying operations and the level of occupancy following continuous changes in daily flight schedules unlike other common commercial buildings, such as office buildings, where their operational schedules are relatively regular and constant over time. Therefore, we set our goal of this study to develop an airport-specific energy baseline model to help airport operators utilize DR opportunities especially by incorporating the varying operational schedule represented by the number of passengers of departure and arrival flights. For this purpose, first, we perform a visual inspection to analyze the relationships between the power demand and potential predictors, such as time-of-day, time-of-week, outside temperature, and the number of passengers of departure flights and arrival flights. Then, we develop airport-specific energy baseline models through linear regression analysis with ten different combinations of explanatory variables. Finally, we analyze the regression coefficients of each model to understand the impact of variables on the airport power demand. The results show that the model with time-of-week and outside temperature has the lowest mean absolute percentage error (MAPE) of 2.72% (305.87 kW) and using time-of-week rather than time-of-day reduces the error by about 4.1 ~ 4.8 kW. However, both departure and arrival flight schedules did not significantly increase the prediction accuracy contrary to our initial assumption that flight schedule is closely related to energy consumption patterns at airports. This result can be interpreted as that, for the particular airport used in this case study, the influx and outflow of occupants have a relatively small impact on the whole airport energy consumption since it is a large size airport and a most of the energy consumption is coming from regular operations such as conveyer belt and lightings. Therefore, to understand the true value of flight schedule regarding airport consumption, we need further investigation on other airports in different sizes and climate zones. However, the method suggested in this study to identify such impact and develop airport-specific energy baseline models still holds and can be applied in universal cases.
Time-Lapse Photography of Runway Reconstruction for Education and Training
Drake Krohn and Darcy M. Bullock
Airfield construction projects have unique construction challenges due to their close proximity to aircraft operations. Emphasis on communication, safety areas, airspace protection zones, and foreign object damage is stressed. Incursions and incidents are of significant concern to the FAA, the airport operator, and the contractor. This paper discusses and illustrates how time-lapse photography can be utilized to develop training and education material pertaining to the construction practices on airfields. This is illustrated through two case studies documenting runway reconstruction at two airports in Indiana. The paper is supplemented with several images and links to example YouTube videos to illustrate these concepts. This material is beneficial for training construction workers on airfield awareness and an overall understanding of the complexities of airside construction. Though this paper focuses on general aviation airports, it can be of value to all sizes and types of airports. With some modifications and sup- port, the proposed techniques could be applied to larger airports.
Aircraft Gas Turbine Noise Reduction Utilizing New Synthetic Fuels and Sound Insulation Materials
Emerald Simons and Valentin Soloiu
The need to reduce the sound and vibration characteristics in the aerospace industry is continuously growing in order to meet FAA regulations, lower noise pollution, and customer satisfaction. In order to meet customer satisfaction, aircraft and engine manufacturers must work to control sound and vibration levels, so that the passengers are not experiencing discomfort during their flight. Sound and vibration characteristics of a fixed-wing aircraft with jet engines are comprised of complex frequency contents, challenging engineers to develop quiet engine designs, aerodynamic bodies, and advanced sound and vibration attenuating materials. One of the noisiest sources of an aircraft, a gas turbine, will be analyzed in this research. In Part I of this project, the use of alternative fuels in a gas turbine engine was investigated to determine whether there are negative effects on the sound and vibration levels. Three types of fuels were used: Jet A as the reference fuel, natural gas derived S-8, and coal derived IPK. The alternative fuels, S-8 and IPK, are Fischer-Tropsch process fuels. Overall sound and vibration characteristics of the alternative fuels presented a similar pattern across the frequency spectrum to those of the reference fuel, with the alternative fuels being slightly quieter. In Part II, the sound path was treated by introducing sound absorbing materials and investigating their acoustic performances. A melamine based foam and soy based foam were used in this research. Melamine is very lightweight, has excel- lent thermal endurance, and is hydrophobic. The soy based foam was selected for its potential application in the aerospace industry to work towards a greener aircraft, in an effort to promote environmental sustainability. The soy based material reduced the sound level over 20 dBA and presented better performance than the melamine at high frequencies.
Adaptive Airport Planning Frameworks and Techniques for a New Era of Planning
Daniel Suh and Megan S. Ryerson
Airport planners, in determining the long term development of their airport’s infrastructure, estimate future use of their airports using two loose categories of methods: forecasting and peer-group learning. In peer-group learning, airport planners compare their airports with those of like airports and engage in peer-to-peer exchange of information about lessons learned from past experiences and technical and planning guidance. The blend of the quantitative and qualitative methodologies can be a powerful tool for airport planners because they can validate their forecasts with the actual happenings of similar airports. In the current state of the aviation industry it is critical for airports to engage in peer-group learning because the environment in which they are planning their airports is more volatile; however, while fore- casting has been well studied by scholars and airport planners, peer-group learning has attracted relatively less attention. Given the role of peer-group learning in airport planning, it is essential that we understand how airport planners define their peers in ever evolving economy and industry environments. Since the deregulation of the airline industry in the 1970s, air- port system has become much more nuanced and no longer clean-cut while airport planners often resort to single metric such as enplanements as a peer criteria. In this study, we develop, present, and test a peer identification methodology that reflects volatilities in the economy and the airport industry by using an expanded list of both static and dynamic metrics. Our methodology highlights an important lesson that metrics matter in how airports identify their peers and benchmark their performance and help improve their plans.
Coordinated Airport Facility Development Under Uncertainty
Yanshuo Sun and Paul M. Schonfeld
This study presents an applied computation tool for coordinating various airport facility expansions with explicit considerations of impacts of various uncertainties. An airport is modelled as a system consisting of components (e.g., runways, air cargo terminals, maintenance facilities, etc.), which operate in-series or in-parallel. To roughly balance (i.e., equalize) the capacities of the components acting in-series, a network flow formulation is proposed, where interactions between user flows and facilities are modelled. In addition to the uncertainty in air traffic forecasts, the effect of uncertain aircraft mix is also considered. The final model is a stochastic mixed integer nonlinear program, which is converted to its deterministic equivalent, because the number of stochastic scenarios is finite. An interactive solution framework is proposed to solve the problem, after the exploration of the problem’s properties. Numerical studies are conducted to demonstrate the usefulness of the proposed model and effectiveness of the solution algorithm. Prospective work includes the application of the proposed development approach to a real-world case study.
Development of a Life-Cycle Assessment Tool to Quantify the Environmental Impacts of Airport Pavement Construction
Rebekah Yang and Imad L. Al-Qadi
The environmental impacts of airport pavement construction is evaluated in this study using a life-cycle analysis approach. The total primary energy consumption (TPE) and greenhouse gas (GHG) emissions from material production and construction of pavement were determined using life-cycle assessment (LCA), a quantitative methodology described by the International Organization for Standardization 14040 series. A tool was developed to implement a probabilistic LCA analysis using the Monte Carlo Method. This allows for the consideration of uncertainty from life-cycle inventory data. The construction of runway 10R- 28L at Chicago O’Hare International Airport was analyzed as a case study, focusing on the mainline and shoulder pavement designs. The environmental impacts from producing materials for the pavement increased from lower to upper layers, while the asphalt layers had a relatively higher TPE than the upper Portland cement concrete layer and vice versa for GHGs. The impacts from material production overshadowed that from construction, which contributed less than 2% of the TPE and GHGs. A further breakdown of the processes showed that the production of asphalt binder and Portland cement were the leading contributors (45.3% and 29.2%, respectively) of TPE, while the latter was the leading contributor (73.4%) of GHGs. A probabilistic analysis was performed for the original 10R-28L runway design as well as a modified design with- out the use of recycled materials or warm-mix asphalt technology. The results from 1000 Monte Carlo simulations showed that the environmental impacts from the two cases were statistically significant, with the original design having lower TPE (482 versus 693 MJ/yd2 for TPE) as well as GHGs (37.5 versus 53.9 kg CO2e/yd2).
The Effects of Mergers and Divestitures on Airline Fares
Zhou Zhang, Federico Ciliberto, and Jonathan Williams
U.S. antitrust authorities have increasingly forced merging companies to divest assets as a condition for merger approval, with a goal of creating a more competitive post-merger environment. We study the effectiveness of this strategy in the context of the airline industry, where forced divestitures have occurred in recent consolidations. We use unique data on critical assets to airport facilities involved in the divestitures to first document the reallocation of the assets to low-cost carriers. We then estimate the impact of the divestitures on pricing. We find that at the affected airports, fares for merging carriers fall by 3% and fare for non-merging carriers fall by 1%, relative to airports where there is no divestiture. This provides evidence that the divestiture strategy used by antitrust authorities is effective in this setting in mitigating market power.