ACADEMIC YEAR 2016–2017
Data-Driven Modeling of Aircraft Engine Fuel Burn in Climb Out and Approach
Yashovardhan S. Chati1 and Hamsa Balakrishnan1
Fuel burn is a key driver of aircraft performance, and contributes to airline costs and emissions. Low-altitude fuel burn and emissions, such as those that occur during climb out and approach, have a significant impact on the environment in the vicinity of airports. This paper proposes a new methodology to statistically model fuel burn in the climb out and approach phases using the trajectory of an aircraft. The model features are chosen by leveraging a physical understanding of aircraft and engine dynamics. Model development is conducted through the use of Gaussian Process Regression on a limited Flight Data Recorder archive, which also provides ground truth estimates of the fuel flow rate and total fuel burn. The result is a class of models that provide predictive distributions of the fuel burn corresponding to a given aircraft trajectory, thereby also quantifying the uncertainty in the predictions. The performance of the proposed models is compared with other frequently used Aircraft Performance Models. The statistical models are found to reduce the error in the estimated total fuel burn by more than 73% in climb out and by 59% in approach.
Airport Expansion and Endangered Bats: Development and Mitigation Actions Near the Indianapolis International Airport
Timothy J. Divoll and Joy M. O’Keefe
Economic prosperity and globalization are major drivers for development of international airports, but aviation-oriented businesses and residential developments are a by-product of airport business models. Among the multitude of planning and development considerations is the habitat needs of endangered wildlife species. Foraging data were analyzed from 57 bats during three time periods (1998–1999: pre-mitigation; 2005–2006: during mitigation, and 2014–2016: post-mitigation) of a long-term study of Indiana bats (Myotis sodalis) near the Indianapolis International Airport. At this site, both developed land cover and forested land cover increased between 1998 and 2016 (34.1% and 3.3%, respectively). Mitigation actions included converting 323 ha of residential lots back to forest, and creation of a 56 ha wetland and an 85 ha multi-use park. Bat use of landscape cover types was related to changes in land cover during each period and competing hypotheses were compared to explain changes in bat foraging space use. With the addition of a major highway interchange where the colony foraged, bats increased foraging ranges, presumable in search of new habitat. In all periods, bats selected for forested habitat; as trees in replanted forest and designated parks aged, bats reduced their foraging ranges. Restoring hardwood forest and setting aside parklands were effective proactive mitigation measures for the colony of Indiana bats near the Indianapolis International Airport, and similar actions should benefit other wildlife where human development and habitat needs intersect.
Developing Machine-Learning Models to Predict Airfield Pavement Responses
Osman Erman Gungor and Imad L. Al-Qadi
Aviation promotes trade and tourism by connecting regions, people, and countries. Having a functional and efficient airport pavement network is important to improve aviation traffic and to provide safer mobility to almost 800 million passengers travelling in the U.S. per year. The Federal Aviation Administration has initiated and actively been participating in many projects to further advance pavement design and performance to meet user requirements. To accomplish that, quantitative data are needed; such data may be collected from the pavement response to gear and environment loading. In this study, responses from four instrumented taxiway concrete slabs at John F. Kennedy International Airport were analyzed. The collected data were used to develop machine-learning (ML) based prediction models to compute the temperature, curling and bending strains within pavement. The ML models were developed using the support vector machine (SVM) algorithm. The results showed that SVM based ML models can predict pavement responses with a high accuracy and low computation time. Furthermore, in the case of feeding more data from various airports, ML models have proven to be a promising technique for pavement analysis engine for future airport pavement design frameworks. This study also produces recommendations for future data collection projects to have well-designed databases for data-driven models development.
A Risk Assessment Framework for Airport Development Projects
Anna Hopper
This paper develops a risk assessment framework for airport development projects. It discusses the major types of inherent development risk, including political risk, environmental risk, financial risk, airline risk, forecast risk, and regulatory or operational risk, and it offers suggestions for risk mitigation strategies. Furthermore, it identifies and analyzes relative risk determinants, which affect the magnitude and type of risk that development projects will likely face. These include the presence of a dominant airline, the airport’s rate structure, the airport’s ownership and operating structure, local demand, and geopolitical events. These factors and their interconnected relationships are illustrated through case studies of relevant airport development projects.
Air Cargo Forecasting in an Age of Electronic Retail
Peter Hylton and Catherine L. Ross
Airports face numerous difficulties in capital planning as a result of instability and unpredictable demand. Some trends such as excess bellyhold capacity have depressed demand for dedicated cargo flights, whereas others like growth in electronic retail (e-retail) are stimulating demand unevenly among airports. If e-retail distribution is following a different pattern from general cargo, then it may reshuffle cargo volumes among airports in a way that is unforeseeable by most forecasting approaches. It has been difficult to estimate whether e-retail shipments would follow the same geospatial patterns as general cargo because of the dearth of data on e-retail distribution networks. This study examines domestically oriented e-retail logistics to test its relationship with airport cargo throughput controlling for regional variables that also influence cargo volume. The regression models show that an airport’s accessibility to landside e-retail logistics activity helps predict that airport’s cargo volume in a way that access to traditional retail logistics does not. The findings support the alternative hypothesis that e-retail may reconfigure distribution networks and boost cargo volume at some airports, opening opportunities to gain market share for airports that are new to e-retail. Depending on the extent of the reconfiguration, e-retail growth could temporarily degrade the accuracy of common forecasting methods. Airports can respond by collecting qualitative and quantitative data where possible and adopting scenario-based, adaptive policy making that hedges risks.
Simulation Modeling of a Prototype Designed to Address the Congestion of Passengers and Items at the Composure Area of Security Checkpoints
Maria Luisa Janer Rubio and Manuel D. Rossetti
Discrete event simulation is used to evaluate a novel configuration of the exit roller in the composure area of security checkpoints, which is designed to address the failures of the current exit roller configuration. The paper presents in detail the conceptual modeling of a two-lane system with the current configuration, and an additional model, featuring the new design in one of the lanes. The second model is used to evaluate statistically whether there is any reduction in the passengers’ system time from directing the passengers to the lane having the new design, based on the number of items passengers carry. Lastly, we perform an analysis to examine the range of arrival rates for which a single lane, featuring the new design, could replace a traditional two-lane system.
Modeling Airport Business Risks, Enplanement Volatility, and Valuation of Flexibility Options in Airport Expansion Projects
Ilker Karaca
Airport capital improvement programs involve considerable flexibility in investment timing and engineering design. Even though the valuation of flexibility options may depend on several factors, the volatility of future airport activity levels, which largely defines the business risk for airport operators, makes up the focus of the present paper. As such, the paper proposes a model that can be used to value two types of flexibility options common in airport expansion projects. The first type of option—flexibility in investment timing decisions—creates value by conditioning capacity expansion decisions on trends in airport activity levels. The second, flexibility in engineering design, permits operators to influence their demand composition and to reconfigure airport facilities if their business environment changes unfavorably. A Monte Carlo simulation example also demonstrates the application of the proposed valuation model. The results show that flexibility options add economic value by reducing downside exposures and by providing the ability to increase capacity if enplanements stay on a rising trajectory. Moreover, the paper provides a comparison of enplanement growth rates by airport size for the largest 140 U.S. airports from 1990 to 2016. The analysis shows that medium airports may be uniquely positioned to benefit the most from flexible design approaches. For these airports, results imply higher exposures to excess capacity risks because of the increased persistence of losses, despite the higher year-on-year volatility of small airports.
Airfield Pavement Damage Evaluation Due to New-Generation Aircraft Wheel Loading and Wander Patterns
Priyanka Sarker and Erol Tutumluer
This paper presents a stress-history-based approach to predict the deformation basins of airport pavements subjected to heavy aircraft loading applied in sequential wanders. Multi-depth deflectometer data from full-scale aircraft landing gear tests conducted at the National Airport Pavement Test Facility built by the Federal Aviation Administration are used to create individual pass residual deformation transverse profiles. The computed residual deformation profiles are further corrected for stress-history effects to predict rut in the selected test sections. The developed model focuses on using the previous load location and stress history of the soil element to develop the deformations in that element. Despite the unavailability of the surface transverse profile data measured in the field at different passes, the initial attempt of the model can closely predict the deformation profile similar to width and shape expected in the field. And after the stress-history effects are accounted for, the initially calculated rut depth decreases significantly to match the final contour basin of the test sections extracted from the post traffic trenching. The advantage of using the stress-history-effects-based rut prediction tool is that it can allow any combination of wander positions and sequences of load applications to be accounted for their effects on the final surface rut development.
Germs on a Plane: The Transmission and Risks of Airplane-Borne Diseases
Nereyda L. Sevilla
This research explored the role of air travel in the spread of infectious diseases, specifically severe acute respiratory syndrome (SARS), H1N1, Ebola, and pneumonic plague. Air travel provides the means for such diseases to spread internationally at extraordinary rates because infected passengers jump from coast to coast and continent to continent within hours. Outbreaks of diseases that spread from person to person test the effectiveness of current public health responses. This research used a mixed methods approach, including use of the Spatiotemporal Epidemiological Modeler, to model the spread of diseases, evaluate the impact of air travel on disease spread, and analyze the effectiveness of different public health strategies and travel policies. Modeling showed that the spread of Ebola and pneumonic plague is minimal and should not be a major air travel concern if an individual becomes infected. H1N1 and SARS have higher infection rates and air travel will facilitate the spread of disease nationally and internationally. To contain the spread of infectious diseases, aviation and public health authorities should establish tailored preventive measures at airports, capture contact information for ticketed passengers, expand the definition of “close contact,” and conduct widespread educational programs. The measures will put in place a foundation for containing the spread of infectious diseases via air travel and minimize the panic and economic consequences that may occur during an outbreak.
Aviation Connectivity Impacts on Regional Economies in the United States
Jason C. Y. Wong
This paper uses applied microeconomics techniques to investigate the impact of aviation connectivity on 548 regional economies in the United States. Using lagged socioeconomic variables to instrument for future aviation connectivity, the paper finds a significant impact of connectivity on long-run economic growth. An increase of 100 in the city’s Global Connectivity Index is associated with an increase in long-term total personal income of the city by up to $254,350,000, and up to 613 more jobs. For a city like Myrtle Beach, SC, with a connectivity index close to the mean connectivity levels of core-based statistical areas, a 100-point increase in the index represents a 1.03% increase in air connectivity. The paper also finds evidence suggesting that the impact of connectivity on regional economies is significantly more pronounced in the largest 100 cities, whereas these effects vanish in smaller cities.