1,606 research outputs found

    Prediction of Airport Arrival Rates Using Data Mining Methods

    Get PDF
    This research sought to establish and utilize relationships between environmental variable inputs and airport efficiency estimates by data mining archived weather and airport performance data at ten geographically and climatologically different airports. Several meaningful relationships were discovered using various statistical modeling methods within an overarching data mining protocol and the developed models were tested using historical data. Additionally, a selected model was deployed using real-time predictive weather information to estimate airport efficiency as a demonstration of potential operational usefulness. This work employed SAS® Enterprise Miner TM data mining and modeling software to train and validate decision tree, neural network, and linear regression models to estimate the importance of weather input variables in predicting Airport Arrival Rates (AAR) using the FAA’s Aviation System Performance Metric (ASPM) database. The ASPM database contains airport performance statistics and limited weather variables archived at 15-minute and hourly intervals, and these data formed the foundation of this study. In order to add more weather parameters into the data mining environment, National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) meteorological hourly station data were merged with the ASPM data to increase the number of environmental variables (e.g., precipitation type and amount) into the analyses. Using the SAS® Enterprise Miner TM, three different types of models were created, compared, and scored at the following ten airports: a) Hartsfield-Jackson Atlanta International Airport (ATL), b) Los Angeles International Airport (LAX), c) O’Hare International Airport (ORD), d) Dallas/Fort Worth International Airport (DFW), e) John F. Kennedy International Airport (JFK), f) Denver International Airport (DEN), g) San Francisco International Airport (SFO), h) Charlotte-Douglas International Airport (CLT), i) LaGuardia Airport (LGA), and j) Newark Liberty International Airport (EWR). At each location, weather inputs were used to estimate AARs as a metric of efficiency easily interpreted by FAA airspace managers. To estimate Airport Arrival Rates, three data sets were used: a) 15-minute and b) hourly ASPM data, along with c) a merged ASPM and meteorological hourly station data set. For all three data sets, the models were trained and validated using data from 2014 and 2015, and then tested using 2016 data. Additionally, a selected airport model was deployed using National Weather Service (NWS) Localized Aviation MOS (Model Output Statistics) Program (LAMP) weather guidance as the input variables over a 24-hour period as a test. The resulting AAR output predictions were then compared with the real-world AARs observed. Based on model scoring using 2016 data, LAX, ATL, and EWR demonstrated useful predictive performance that potentially could be applied to estimate real-world AARs. Marginal, but perhaps useful AAR prediction might be gleaned operationally at LGA, SFO, and DFW, as the number of successfully scored cases fall loosely within one standard deviation of acceptable model performance arbitrarily set at ten percent of the airport’s maximum AAR. The remaining models studied, DEN, CLT, ORD, and JFK appeared to have little useful operational application based on the 2016 model scoring results

    Aviation System Performance During the Summer Convective Weather Season

    Get PDF
    This paper analyzes recent trends related to delays, airborne holding and diversions in the National Airspace System (NAS) during the summer convective weather season. A weather variable is introduced to help analyze these performance metrics in a way that factors out differences in the location and intensity of thunderstorms. Regression analysis indicates a nearly 50% increase in flights delayed more than an hour from summer 2003 to 2005. The increase in delay is associated with a growing concentration of flights at busy hub airports over the past five years

    Aviation System Performance During the Summer Convective Weather Season

    Get PDF
    This paper analyzes recent trends related to delays, airborne holding and diversions in the National Airspace System (NAS) during the summer convective weather season. A weather variable is introduced to help analyze these performance metrics in a way that factors out differences in the location and intensity of thunderstorms. Regression analysis indicates a nearly 50% increase in flights delayed more than an hour from summer 2003 to 2005. The increase in delay is associated with a growing concentration of flights at busy hub airports over the past five years

    Systems Analysis of NASA Aviation Safety Program: Final Report

    Get PDF
    A three-month study (February to April 2010) of the NASA Aviation Safety (AvSafe) program was conducted. This study comprised three components: (1) a statistical analysis of currently available civilian subsonic aircraft data from the National Transportation Safety Board (NTSB), the Federal Aviation Administration (FAA), and the Aviation Safety Information Analysis and Sharing (ASIAS) system to identify any significant or overlooked aviation safety issues; (2) a high-level qualitative identification of future safety risks, with an assessment of the potential impact of the NASA AvSafe research on the National Airspace System (NAS) based on these risks; and (3) a detailed, top-down analysis of the NASA AvSafe program using an established and peer-reviewed systems analysis methodology. The statistical analysis identified the top aviation "tall poles" based on NTSB accident and FAA incident data from 1997 to 2006. A separate examination of medical helicopter accidents in the United States was also conducted. Multiple external sources were used to develop a compilation of ten "tall poles" in future safety issues/risks. The top-down analysis of the AvSafe was conducted by using a modification of the Gibson methodology. Of the 17 challenging safety issues that were identified, 11 were directly addressed by the AvSafe program research portfolio

    Real-Time Monitoring and Prediction of Airspace Safety

    Get PDF
    The U.S. National Airspace System (NAS) has reached an extremely high level of safety in recent years. However, it will only become more difficult to maintain the current level of safety with the forecasted increase in operations, and so the FAA has been making revolutionary changes to the NAS to both expand capacity and ensure safety. Our work complements these efforts by developing a novel model-based framework for real-time monitoring and prediction of the safety of the NAS. Our framework is divided into two parts: (offline) safety analysis and modeling part, and a real-time (online) monitoring and prediction of safety. The goal of the safety analysis task is to identify hazards to flight (distilled from several national databases) and to codify these hazards within our framework such that we can monitor and predict them. From these we define safety metrics that can be monitored and predicted using dynamic models of airspace operations, aircraft, and weather, along with a rigorous, mathematical treatment of uncertainty. We demonstrate our overall approach and highlight the advantages of this approach over the current state-of-the-art through simulated scenarios

    Clustering Days with Similar Airport Weather Conditions

    Get PDF
    On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport

    System elements required to guarantee the reliability, availability and integrity of decision-making information in a complex airborne autonomous system

    Get PDF
    Current air traffic management systems are centred on piloted aircraft, in which all the main decisions are made by humans. In the world of autonomous vehicles, there will be a driving need for decisions to be made by the system rather than by humans due to the benefits of more automation such as reducing the likelihood of human error, handling more air traffic in national airspace safely, providing prior warnings of potential conflicts etc. The system will have to decide on courses of action that will have highly safety critical consequences. One way to ensure these decisions are robust is to guarantee that the information being used for the decision is valid and of very high integrity. [Continues.

    An Analysis of Air Traffic Controllers’ Job Satisfaction

    Get PDF
    The air traffic controllers\u27 job is one of the most hectic in today’s world, predominantly due to its safety-critical operations and altering expectations. The primary purpose of this paper is to provide a holistic directory of determinants and synthesized reinforcements for air traffic controllers\u27 job satisfaction. Researchers in the past have put the spotlight on individual air traffic controller’s technical job satisfaction factors, such as impacts from remote tower operation, airplane trajectory changes, and dynamic air traffic situations. However, none described the connection among those factors and how adjusting those factors can enhance the cognitive components related to their job satisfaction. This paper\u27s in-depth analysis identified factors contributing to air traffic controllers\u27 job satisfaction based on past literature. It is intended to increase understanding and improve knowledge for future researchers and practitioners. The five predominant factors identified for air traffic controllers’ job satisfaction are ambiguity of job functions, overwhelming workload, complex task performance and uncertain work demand, job fatigue, and work-family conflict. Some effective methods to increase air traffic controllers’ job satisfaction are regular break between shifts, technological advancement to facilitate jobs, and sound insulations

    Small UAS Detect and Avoid Requirements Necessary for Limited Beyond Visual Line of Sight (BVLOS) Operations

    Get PDF
    Potential small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) operational scenarios/use cases and Detect And Avoid (DAA) approaches were collected through a number of industry wide data calls. Every 333 Exemption holder was solicited for this same information. Summary information from more than 5,000 exemption holders is documented, and the information received had varied level of detail but has given relevant experiential information to generalize use cases. A plan was developed and testing completed to assess Radio Line Of Sight (RLOS), a potential key limiting factors for safe BVLOS ops. Details of the equipment used, flight test area, test payload, and fixtures for testing at different altitudes is presented and the resulting comparison of a simplified mathematical model, an online modeling tool, and flight data are provided. An Operational Framework that defines the environment, conditions, constraints, and limitations under which the recommended requirements will enable sUAS operations BVLOS is presented. The framework includes strategies that can build upon Federal Aviation Administration (FAA) and industry actions that should result in an increase in BVLOS flights in the near term. Evaluating approaches to sUAS DAA was accomplished through five subtasks: literature review of pilot and ground observer see and avoid performance, survey of DAA criteria and recommended baseline performance, survey of existing/developing DAA technologies and performance, assessment of risks of selected DAA approaches, and flight testing. Pilot and ground observer see and avoid performance were evaluated through a literature review. Development of DAA criteria—the emphasis here being well clear— was accomplished through working with the Science And Research Panel (SARP) and through simulations of manned and unmanned aircraft interactions. Information regarding sUAS DAA approaches was collected through a literature review, requests for information, and direct interactions. These were analyzed through delineation of system type and definition of metrics and metric values. Risks associated with sUAS DAA systems were assessed by focusing on the Safety Risk Management (SRM) pillar of the SMS (Safety Management System) process. This effort (1) identified hazards related to the operation of sUAS in BVLOS, (2) offered a preliminary risk assessment considering existing controls, and (3) recommended additional controls and mitigations to further reduce risk to the lowest practical level. Finally, flight tests were conducted to collect preliminary data regarding well clear and DAA system hazards
    • …
    corecore