On the Statistics and Predictability of Go-Arounds

This paper takes an empirical approach to identify operational factors at busy airports that may predate go-around maneuvers. Using four years of data from San Francisco International Airport, we begin our investigation with a statistical approach to investigate which features of airborne, ground operations (e.g., number of inbound aircraft, number of aircraft taxiing from gate, etc.) or weather are most likely to fluctuate, relative to nominal operations, in the minutes immediately preceding a missed approach. We analyze these findings both in terms of their implication on current airport operations and discuss how the antecedent factors may affect NextGen. Finally, as a means to assist air traffic controllers, we draw upon techniques from the machine learning community to develop a preliminary alert system for go-around prediction.United States. National Aeronautics and Space Administration (Grant NNX08AY52A)

On the Statistics and Predictability of Go-Arounds

This paper takes an empirical approach to identify operational factors at busy airports that may predate go-around maneuvers. Using four years of data from San Francisco International Airport, we begin our investigation with a statistical approach to investigate which features of airborne, ground operations (e.g., number of inbound aircraft, number of aircraft taxiing from gate, etc.) or weather are most likely to fluctuate, relative to nominal operations, in the minutes immediately preceding a missed approach. We analyze these findings both in terms of their implication on current airport operations and discuss how the antecedent factors may affect NextGen. Finally, as a means to assist air traffic controllers, we draw upon techniques from the machine learning community to develop a preliminary alert system for go-around prediction.Comment: 10 pages, 14 figures, Submitted to USA/Europe ATM Seminar 201

Aircraft Go-Arounds Associated to Vessel Traffic: Hamburg Finkenwerder Case Study

An aircraft go-around is a costly yet safety critical procedure. While there are many reasons to decide that a go-around is necessary, at Hamburg Finkenwerder airport (EDHI) there is a rather peculiar one: vessel traffic crossing the approach path. As both vessels and aircraft transmit their position at regular intervals through the Automatic Identification System (AIS) and Automatic Dependent Surveillance Broadcast (ADS-B) protocols it is possible to identify vessels that can cause problems to the aircraft’s approach. In this work we identified a 10 time higher than average go-around incidence at Finkenwerder airport and were able to find evidence of its relation to large passing vessels. As vessel traffic has a mostly stable course and speed, we found it is possible to predict the passing vessels well ahead of time in order to determine the best approach and reduce the number of go-arounds, allowing to save both fuel and emissions

JUDGING EQUIVALENTS

JUDGING EQUIVALENT

Understanding Behavioral Sources of Process Variation Following Enterprise System Deployment

This paper extends the current understanding of the time-sensitivity of intent and usage following large-scale IT implementation. Our study focuses on perceived system misfit with organizational processes in tandem with the availability of system circumvention opportunities. Case study comparisons and controlled experiments are used to support the theoretical unpacking of organizational and technical contingencies and their relationship to shifts in user intentions and variation in work-processing tactics over time. Findings suggest that managers and users may retain strong intentions to circumvent systems in the presence of perceived task-technology misfit. The perceived ease with which this circumvention is attainable factors significantly into the timeframe within which it is attempted, and subsequently impacts the onset of deviation from prescribed practice and anticipated dynamics

Predicting airplane go-arounds using machine learning and open-source data

Go-arounds (GAs) are standard air traffic control procedures during which aircraft approach a runway but do not land. The incidence of a GA can subsequently affect the workload of flight crews and air traffic controllers, and might impact an airport runway’s throughput capacity. In this study, two different modeling methods for predicting the occurrence of GAs based on open-source Automatic Dependent Surveillance–Broadcast (ADS-B) and meteorological data are presented. A macroscopic model quantifies the probability of a GA within the next hour for an airport by applying a generalized additive model. A microscopic model employs a number of machine learning classifiers on trajectories of aircraft on approach in order to predict if a GA will be performed. Even though the results of the macroscopic model are promising, the information currently available to predict the probability of a GA is not detailed enough to achieve satisfactory predictions. Similarly, the microscopic model is capable of predicting 50% of all GAs, with false positive rate below 7%. Despite the limitations of the quality of the results, the authors are convinced that both modeling methods can be inspiring to other researchers and provide useful insights into the airport system under scrutiny

A Narrative Study of Nurses' Interactions When Using Health Information Technology

abstract: Nurses are using health information technology during patient care activities in acute care at an unprecedented rate. Previous literature has presented nurses' response to technology obstacles as a work-around, a negative behavior. Using a narrative inquiry in one hospital unit, this dissertation examines nurses' interactions when they encounter technology obstacles from a complexity science perspective. In this alternative view, outcomes are understood to emerge from tensions in the environment through nonlinear and self-organizing interactions. Innovation is a process of changing interaction patterns to bring about transformation in practices or products that have the potential to contribute to social wellbeing, such as better care. Innovation was found when nurses responded to health information technology obstacles with self-organizing interactions, sensitivity to initial conditions, multidirectionality, and their actions were influenced by a plethora of sets of rules. Nurses self-organized with co-workers to find a better way to deliver care to patients when using technology. Nurses rarely told others outside their work-group of the obstacles that occurred in their everyday interactions, including hospital-wide process improvement committees. Managers were infrequently consulted when nurses encountered technology obstacles, and often nurses did not find solutions to their obstacles when they contacted the Help Desk. Opportunities exist to facilitate interactions among nurses and other members of the organization to realize better use of health information technology that improves quality and safety while decreasing cost in the patient experience.Dissertation/ThesisPh.D. Nursing and Healthcare Innovation 201

Collective and self-efficacy and their relationship to team and individual performance in male intercollegiate basketball players

This study examined the predictive relationship0 between self-efficacy (SE) and individual basketball performance, and collective efficacy (CE) and team basketball performance in a competitive setting. [This is an excerpt from the abstract. For the complete abstract, please see the document.

The Efficacy of Operational Bird Strike Prevention

Involving air traffic controllers and pilots into the bird strike prevention process is considered an essential step to increase aviation and avian safety. Prior to implementing operational measures such as real-time warning systems, it is vital to evaluate their feasibility. This paper studies the efficacy of a bird strike advisory system for air traffic control. In addition to the potential safety benefit, the possible impact on airport operations is analyzed. To this end, a previously developed collision avoidance algorithm underlying the system was tested in fast-time Monte Carlo simulations involving various air traffic and bird densities to obtain representative conclusions for different operational conditions. The results demonstrate the strong safety potential of operational bird strike prevention in case of precise bird movement prediction. Unless airports operate close to their capacity limits while bird abundance is high, the induced delays remain tolerable. Prioritization of hazardous strikes involving large individuals as well as flocks of birds are expected to support operational feasibility in all conditions