Transportation Research Record: Journal of the Transportation Research Board

Over the past few decades, the European Union's (EU's) aviation industries have experienced a dwindling of their long-standing capacity to attract and retain the most promising students. A misalignment or gap between the competencies needed at work and those earned at school is just one of the several potential causes already identified. Such a gap may be eroding the person–job fit, namely, the difference between the perceived applicant–job fit and the actual employee–job fit, and thus contributing to making these industries less attractive. The purpose of the research was to evaluate the person–job fit along two dimensions: perceived applicant–job and actual employee–job fit in the EU aviation sector. Competency was the component used to operationalize the measurement of the person–job fit. A competency gap assessment framework estimated the gaps between the competencies for adequately performing the job tasks and those acquired by higher education students. A total of 88 competencies, divided into four domains of activity (airlines, airports, air navigation service providers, and manufacturers), were taken into consideration. The methodological approach included two large-scale surveys. The results evidenced a high actual person–job fit but a low perceived person–job fit in the aeronautical or aerospace engineering programs and a low actual person–job fit and perceived person–job fit in the remaining ones. Results suggested that students’ attraction to pursuing a career in aviation might be negatively affected but, if starting out in a manufacturing industry, students were likely to perform above their initial expectations.

Engine failure caused by bird strikes can be particularly perilous for today's typical twin-engine aircraft. Although large-bird populations have increased substantially since the 1970s, modern-day turbofan engines are not tested for large birds. Instead, it is acceptable for contemporary turbofan engines to lose all power because of large-bird ingestion. With the increasing use of turbofan engines and air traffic, not only are more bird strikes expected in the near future, but also more bird strikes are anticipated to result in engine failure. This study identified the factors that were statistically associated with the probability of engine failure in the event of a bird strike. A large sample of more than 42,000 U.S. bird strikes was used. The missing data in the sample were multiply imputed by using an approximate Bayesian bootstrap method. With the multiply imputed data, 15 factors were statistically analyzed. Six of those factors were found to be significantly associated with the probability of engine failure in the event of a bird strike: altitude above ground level, bird size, number of birds struck, flight phase, daylight conditions, and sky conditions. A logistic regression model was developed, and a detailed probabilistic interpretation of the model is given for practitioners. By using the findings, aviation authorities can improve bird strike hazard mitigation strategies, flight crews can reduce the potential of bird strikes resulting in engine failure, and researchers can better understand the nature of bird strikes and develop a scientific approach to minimize the likelihood of engine failure in the event of a bird strike.

Many airports are adding nonaviation activities in an effort to diversify and become less dependent on direct air traffic revenues and thus create so-called airport cities. As these added activities generate an increase in traffic, the question arises as to how this increased traffic influences the ground transport access system and if this influence can be used in strategies to achieve higher public transport mode shares. In the best case, the additional traffic would follow a distribution that would balance out the peaks created by the aviation-induced travel and therefore would help create more even transport demand. Such a more balanced situation is especially critical for providing cost-efficient public transport services where service capacity cannot be adjusted very flexibly without sacrificing ease of use. In the worst case, however, additional nonaviation travel demand would peak at the same time as aviation-induced travel and thus increase peak period travel demand. This effect would increase costs for peak period service without improving utilization during low-demand periods. This paper presents a generic framework for analyzing aviation and nonaviation transport demand, followed by a specific study of Zurich Airport in Switzerland. In this case, the nonaviation demand is distributed as complementary to the aviation-induced demand, but the volume of nonaviation demand is so high that it dominates the total demand curve. This result means that the total travel demand is high enough to support a higher level of public transport service than could be operated economically if fed only by the aviation operations and is one reason why Zurich Airport's public transport mode share for air travelers is approximately 50%.

A novel methodology used taxi global position system data and high-resolution transit schedule information to compare travel times and travel fares of the two main nondriving travel modes for airport ground access: taxi and transit. Five origin–destination pairs between Pennsylvania Station in New York City and three airports in the New York region were used as an example to demonstrate these methods. An analysis of total trip cost considered both travel time and expenditures on fare. A binary logit model was used to model the mode choice of travelers. The results indicate that transit is the more likely choice during most of the day except the midnight period when transit service has longer headways. A sensitivity analysis shows the relationship between the value of time and total trip cost per passenger for different numbers of passengers traveling together and at different times of day. The higher the value of time and the number of passengers in a group, the more likely it is that a taxi is chosen for airport trips. The attractiveness of one mode relative to the other varies spatially and temporally according to the travel time and price. This paper focuses on understanding temporal variation of total cost of each mode and the effect that this variation is likely to have on mode share.

This work addresses the real-time problem of managing takeoff and landing operations during traffic disturbances at a busy terminal control area (TCA). An important objective of traffic controllers is the minimization of delay propagation, which may reduce the aircraft travel time and the energy consumption. To improve the effectiveness of air traffic monitoring and control in a busy TCA, this paper presents an advanced optimization-based decision support system and compares centralized and rolling horizon approaches. The possible aircraft conflict detection and resolution actions were viewed as aircraft timing and routing decisions. The problem was modeled by an alternative graph formulation (i.e., a detailed model of air traffic flows in the TCA) and solved by scheduling and rerouting algorithms. The paper also proposes a new mixed-integer linear programming (MILP) formulation to compute (near) optimal scheduling and routing solutions. The paper compares the first-in, first-out (FIFO) rule, used as a surrogate for the dispatchers’ behavior, a truncated branch and bound algorithm for aircraft scheduling with fixed routes, a tabu search algorithm for combined aircraft scheduling and rerouting, and the MILP formulation solved via a commercial solver. Computational experiments are presented for practical-sized instances from Milano Malpensa Airport in Milan, Italy. Disturbed traffic situations were generated by simulating various sets of delayed landing and departing aircraft. A detailed analysis of the experimental results demonstrates that the solutions produced by the optimization algorithms are of a remarkably better quality compared with the FIFO rule, in relation to delay and travel time minimization.

If appropriately equipped, aircraft traveling throughout the global air space have the unique capability to measure and report high-resolution meteorological data, under all weather conditions. These data can have high economic value for both aviation and nonaviation uses by communicating a dense picture of weather conditions from thousands of ad hoc sensors operating daily in the global air space over areas of an aviation operational interest. However, ensuring that the technology provides adequate levels of service and safety requires a clear understanding of system performance characteristics in conjunction with sensor and data link capabilities, broadcast frequencies, equipage rates, and operational service environment requirements. This paper explores the density of coverage as a measure of performance by presenting the problem as a stochastic area coverage model of a homogeneous dynamic wireless ad hoc sensor network. A model was developed to relate the density needs of various advanced air traffic management applications to the spatial and temporal coverage of aircraft-sensed meteorological data in the airport terminal area. A comprehensive experimental design is presented with varying aircraft equipage rates, message broadcast frequencies, coverage granularities, and traffic demand levels. Infrastructure and sensor limitations are included to capture some realism within the model. Results are determined by solving this variation on a traditional set covering model using Monte Carlo simulation techniques.

Aviation carbon taxes have occasionally been debated as a measure to curb aviation carbon dioxide (CO₂) emissions. This paper presents a simulation study on the effects of high aviation carbon taxes on tourism and its related CO₂ emissions. The paper investigates the scenario in which, as a result of high aviation carbon taxes, air fares increase by 50%. To assess such impacts, choice probabilities of vacation alternatives, which were conceptualized as portfolios consisting of a destination, mode of transport, accommodation type, and length of stay, were simulated by using a portfolio vacation choice model. Lower and upper bounds of the impacts were derived. Simulation results suggested that a substantial reduction of the contribution of tourism travel to anthropogenic climate change could be achieved. Tourism travel-related CO₂-equivalent emissions were estimated to drop between 9% and 32%. Furthermore, it was found that the choice probability of nearby destinations (<200 km) increased considerably: between 4% and 18%. It was also found that the choice probability of short vacations (<1 week) also increased considerably: between 3% and 11%.

Airlines reduced available domestic capacity at airports across the United States from 2007 to 2012 in response to a global economic downturn and high and volatile fuel prices. More recently, despite an economic recovery and more stable fuel prices, airlines have continued to keep capacity low relative to historical levels in a strategy that has been referred to as “capacity discipline.” The effects of these shifts in capacity on airport connectivity to the global air transportation network remain unclear. This paper introduces an intuitive index to compute airport connectivity as a function of both the quantity and quality of scheduled nonstop and connecting service. Connectivity scores were computed for 462 U.S. airports; medium-hub and small-hub airports were found to have lost more connectivity on average than large-hub airports from 2007 to 2012. In multiairport regions, losses in connectivity at secondary and tertiary airports outpaced losses at primary airports. With lower levels of connectivity, smaller airports in these regions will need to employ creative strategies to prevent passengers from leaking to primary hubs. Whether these airports can recover from connectivity consolidation will also depend on how long the capacity discipline equilibrium remains in effect among U.S. airlines.

Econometric models of air travel demand are an essential tool in the preparation of air travel forecasts that form the basis for a wide range of planning activities in air transportation and airport operation and development. However, most air travel demand models attempt to predict aggregate demand for air travel and do not consider differences in the use of air travel across different segments of the population or how changes in economic factors or the way in which air service is provided affect different types of air trip differently. The paper addresses the need for a more disaggregate approach to understanding how travel propensity varies across different segments of the population or how the characteristics of air travelers vary across different markets and examines the potential role of air passenger surveys in meeting this need. The paper reviews the factors that may influence air travel propensity, describes the information on air traveler characteristics that is typically collected in air passenger surveys, and presents an analysis of the variation in air travel propensity with household income and composition, on the basis of data from a recent air passenger survey performed in the San Francisco Bay Area in California. The paper concludes with some recommendations for improving access to air passenger survey data to support further research into these issues.

The introduction of larger aircraft on flexible airfield pavements has led to a need for asphalt mixtures capable of sustaining such heavy loads. This laboratory and analytical study investigated the mechanical responses of a number of modified asphalt mixtures to identify their potential for use in airfield aprons and taxiways that were subjected to heavy, static, or slow-moving aircraft loads. The airfield flexible pavement section constructed at the FAA's National Airport Pavement Test Facility Construction Cycle 1 was modeled by using the three-dimensional finite element analysis software ABAQUS. Laboratory-compacted specimens of each modified asphalt mixture were tested by using AASHTO standards to determine volumetric properties and mechanical responses. The effects of static and dynamic aircraft loading were evaluated in ABAQUS with the material properties of the mixtures determined in the laboratory. On the basis of the findings of this study, it appears that several mixtures more commonly used in highway pavements, including modified mixtures, warm-mix asphalt, and reclaimed asphalt pavement, perform similarly to or even outperformed the FAA standard asphalt mixture. The results of this initial study support the idea that an opportunity exists for airports to implement emerging asphalt paving materials without compromising the pavement design life.

This paper presents a research study to handle the problem of missing data in airport pavement management systems. This study was a continuation of an earlier study addressing the same concern. In the earlier study, a stochastic multiple imputation (SMI) approach was adopted to overcome major limitations associated with conventional data imputation methods. The SMI approach considered the variation of multiple plausible imputations and obtained an unbiased estimate in replacing a missing data value. This approach was found to outperform the three most commonly used imputation methods for missing data handling in pavement management: the linear interpolation method, the substitution by mean method, and the regression method. However, the SMI approach estimated missing data values by using purely statistical techniques, without making use of any unique characteristics of pavement performance data. The present study explored the possibility of further improving the data imputation process by exploiting parallel pavement performance-related data available from pavement condition and performance surveys of airfield pavements. An augmented stochastic multiple imputation (ASMI) approach was proposed to incorporate auxiliary parameters to aid in reducing uncertainty and improving prediction performance of runway pavement condition data. Pavement friction data were used as illustration; the related parameter data included aircraft landing volume, rainfall, and temperature. This study showed that the proposed ASMI approach provided an analytically meaningful method from a pavement engineering point of view that can further improve the quality and reliability of imputing missing data for airport pavement management systems.

Visual inspection plays an important role in aviation maintenance. Human reliability analysis (HRA) in this field is necessary and can bring benefits to better human error management. Because of the lack of safety data, the present paper aims to introduce the Bayesian network (BN) approach to perform HRA in visual inspection, which permits the utilization of multi-disciplinary sources of objective and subjective information. In this paper, significant influence factors of visual inspection are identified according to the Human Factors Analysis and Classification System–Maintenance Extension. Then a network representing the visual inspection performance model is constructed. Expert opinions, data fusion from accident reports, and related literature are utilized in the step of obtaining parameters. Two canonical models used in probabilistic network model building, the Noisy-OR gates and the Recursive Noisy-OR rule, are applied to generate conditional probabilities from parameters obtained by the absolute probability judgment technique. Through the BN inference, the inspection reliability can be assessed, and some conclusions and recommendations are drawn that could provide theoretical base and data support to make interventions for safety management of visual inspection.

Disability glare, or visual impairment due to extreme brightness or contrast, can be caused by intense reflections from new constructions onto existing transportation or building infrastructure located nearby. A case study analyzed a disability glare hazard at an airport created by the installation of a large array of photovoltaic panels between an air traffic control tower and the aircraft taxiway. The panels reflected blinding quantities of daylight into the control tower, produced temporary afterimages, and dangerously obscured taxiing aircraft. The existing FAA guidelines for installation of solar technologies are discussed relative to their shortcomings in identifying the glare hazard. High dynamic range photography was used to analyze the glaring situation at the airport. The authors proposed a maximum brightness threshold of 30,000 cd/m² based on the physiology of human vision and the brightness of tasks necessary for air traffic controllers at the case study airport. Detailed reflectivity and three-dimensional models of the photovoltaic panels and the airport were created and validated against measured data. These models were used to perform an annual analysis of the glare hazard. This analysis is displayed temporally by graphs and spatially by images that indicate where the glaring reflections originate. Such information is useful in identifying potential for disability glare before new constructions are built. Finally, the authors used the new method to analyze designs for remediation of the glare hazard.