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5PAPERS SCHEDULED FOR PUBLICATION, 2015â2016 At publication of this Research Results Digest, final versions of papers submitted in September 2016 have been reviewed and accepted for publication in Transportation Research Record, No. 2603. Papers are now undergoing final edits and will be published in early 2017. The following copy presents abstracts for the candidate papers. For each paper, the names that follow the lead (student) author represent the studentâs advisers and others who made specific con- tributions to the paper. 1. Enhancing Safety Risk Management with Quantitative Measures Boris Claros, Carlos Sun, and Praveen Edara Abstract. 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 Sys- tem (SMS) is the FAAâs 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 construc- tion activity. The categorization and processing of data was significant because each variable used could take on multiple forms (e.g., the runway sys- tem 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 nega- tive multinomial distribution was used because it was found suitable for representing overdispersed data such as runway incursion frequency. The per- formance of the models was assessed using the goodness-of-fit measures of log-likelihood, over- dispersion, and cumulative residual plots. The mod- els 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 develop- ment of other safety models that can be integrated into the SRM to enable the quantitative analysis of safety risks. 2. Data-Driven Ground Delay Program Planning Alexander Estes and Michael Ball Abstract. 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 manage- ment initiative using the multi-armed bandit deci- sion 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. 3. Representative Weather Profiles in Airport Planning Sreeta Gorripaty and Mark Hansen Abstract. 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 limita- tions 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- portâs 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
6hour of that day. Weather profiles can be translated to a series of hourly capacity estimates using ana- lytical 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 imprac- tical 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 rep- resentative weather profiles are sufficient to yield accurate delay estimates. 4. Electric Energy Demand Forecasting for Airport Buildings Based on Flight Information for Demand-Response Applications Minkyung Kang, Burcu Akinci, and Mario Bergés Abstract. Airports have strong incentives for taking advantage of demand-response (DR) opportunities considering their large energy footprint and continu- ous 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 occu- pancy following continuous changes in daily flight schedules unlike other common commercial build- ings, such as office buildings, where their opera- tional 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 poten- tial predictors, such as time-of-day, time-of-week, outside temperature, and the number of passen- gers 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 accu- racy 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 consump- tion since it is a large size airport and a most of the energy consumption is coming from regular opera- tions such as conveyer belt and lightings. Therefore, to understand the true value of flight schedule regard- ing airport consumption, we need further investiga- tion 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. 5. Time-Lapse Photography of Runway Reconstruction for Education and Training Drake Krohn and Darcy M. Bullock Abstract. 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 uti- lized to develop training and education material per- taining 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
7construction 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. 6. Aircraft Gas Turbine Noise Reduction Utilizing New Synthetic Fuels and Sound Insulation Materials Emerald Simons and Valentin Soloiu Abstract. The need to reduce the sound and vibra- tion characteristics in the aerospace industry is con- tinuously 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 nega- tive 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 charac- teristics 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 inves- tigating 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 sus- tainability. The soy based material reduced the sound level over 20 dBA and presented better performance than the melamine at high frequencies. 7. Adaptive Airport Planning Frameworks and Techniques for a New Era of Planning Daniel Suh and Megan S. Ryerson Abstract. Airport planners, in determining the long term development of their airportâs 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 quantita- tive and qualitative methodologies can be a power- ful 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 evolv- ing 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 cri- teria. In this study, we develop, present, and test a peer identification methodology that reflects volatili- ties 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. 8. Coordinated Airport Facility Development Under Uncertainty Yanshuo Sun and Paul M. Schonfeld Abstract. This study presents an applied compu- tation 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.),
8which operate in-series or in-parallel. To roughly balance (i.e., equalize) the capacities of the compo- nents acting in-series, a network flow formulation is proposed, where interactions between user flows and facilities are modelled. In addition to the uncer- tainty 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 inter- active solution framework is proposed to solve the problem, after the exploration of the problemâs prop- erties. Numerical studies are conducted to dem- onstrate the usefulness of the proposed model and effectiveness of the solution algorithm. Prospec- tive work includes the application of the proposed develop ment approach to a real-world case study. 9. Development of a Life-Cycle Assessment Tool to Quantify the Environmental Impacts of Airport Pavement Construction Rebekah Yang and Imad L. Al-Qadi Abstract. The environmental impacts of airport pavement construction is evaluated in this study using a life-cycle analysis approach. The total pri- mary energy consumption (TPE) and greenhouse gas (GHG) emissions from material production and construction of pavement were determined using life-cycle assessment (LCA), a quantitative meth- odology described by the International Organiza- tion for Standardization 14040 series. A tool was developed to implement a probabilistic LCA anal- ysis 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 OâHare International Airport was analyzed as a case study, focusing on the mainline and shoulder pavement designs. The environmen- tal impacts from producing materials for the pave- ment 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 produc- tion 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 lead- ing 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 sig- nificant, 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). 10. The Effects of Mergers and Divestitures on Airline Fares Zhou Zhang, Federico Ciliberto, and Jonathan Williams Abstract. U.S. antitrust authorities have increasingly forced merging companies to divest assets as a con- dition 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.
