Statistical Engineering in Air Traffic Management Research

NASA is working to develop an integrated set of advanced technologies to enable efficient arrival operations in high-density terminal airspace for the Next Generation Air Transportation System. This integrated arrival solution is being validated and verified in laboratories and transitioned to a field prototype for an operational demonstration at a major U.S. airport. Within NASA, this is a collaborative effort between Ames and Langley Research Centers involving a multi-year iterative experimentation process. Designing and analyzing a series of sequential batch computer simulations and human-in-the-loop experiments across multiple facilities and simulation environments involves a number of statistical challenges. Experiments conducted in separate laboratories typically have different limitations and constraints, and can take different approaches with respect to the fundamental principles of statistical design of experiments. This often makes it difficult to compare results from multiple experiments and incorporate findings into the next experiment in the series. A statistical engineering approach is being employed within this project to support risk-informed decision making and maximize the knowledge gained within the available resources. This presentation describes a statistical engineering case study from NASA, highlights statistical challenges, and discusses areas where existing statistical methodology is adapted and extended

Modeling Relationships Between Flight Crew Demographics and Perceptions of Interval Management

The Interval Management Alternative Clearances (IMAC) human-in-the-loop simulation experiment was conducted to assess interval management system performance and participants' acceptability and workload while performing three interval management clearance types. Twenty-four subject pilots and eight subject controllers flew ten high-density arrival scenarios into Denver International Airport during two weeks of data collection. This analysis examined the possible relationships between subject pilot demographics on reported perceptions of interval management in IMAC. Multiple linear regression models were created with a new software tool to predict subject pilot questionnaire item responses from demographic information. General patterns were noted across models that may indicate flight crew demographics influence perceptions of interval management

Space-Filling Designs for Multi-Layer Nested Factors

This articles considers computer experiments where levels for continuous factors are selected in sequential order with the level selected for one factor directly a ecting the range of possible levels for the nested factor, and so on for a nite number of factors. In addition, we assume the nested relationships between the factors have no closed form solution. In this paper, we propose an approach for constructing a multi-layer nested factor design, or multi-NFD for short. This space- lling design approach takes advan- tage of the maximin criterion and can be analyzed using a standard Gaussian process model. While the multi-NFD approach can be adapted for future computer experi- ments involving factor relationships of this type, we present results from a particular aerospace computer simulation study

Integration of Weather Avoidance and Traffic Separation

This paper describes a dynamic convective weather avoidance concept that compensates for weather motion uncertainties; the integration of this weather avoidance concept into a prototype 4-D trajectory-based Airborne Separation Assurance System (ASAS) application; and test results from a batch (non-piloted) simulation of the integrated application with high traffic densities and a dynamic convective weather model. The weather model can simulate a number of pseudo-random hazardous weather patterns, such as slow- or fast-moving cells and opening or closing weather gaps, and also allows for modeling of onboard weather radar limitations in range and azimuth. The weather avoidance concept employs nested "core" and "avoid" polygons around convective weather cells, and the simulations assess the effectiveness of various avoid polygon sizes in the presence of different weather patterns, using traffic scenarios representing approximately two times the current traffic density in en-route airspace. Results from the simulation experiment show that the weather avoidance concept is effective over a wide range of weather patterns and cell speeds. Avoid polygons that are only 2-3 miles larger than their core polygons are sufficient to account for weather uncertainties in almost all cases, and traffic separation performance does not appear to degrade with the addition of weather polygon avoidance. Additional "lessons learned" from the batch simulation study are discussed in the paper, along with insights for improving the weather avoidance concept. Introductio

An Analysis of the Speed Commands from an Interval Management Algorithm during the ATD-1 Flight Test

NASA's first Air Traffic Management Technology Demonstration (ATD-1) successfully completed a nineteen-day flight test under a NASA contract with Boeing, with Honeywell and United Airlines as sub-contractors. An Interval Management (IM) avionics prototype was built based on international IM standards, integrated into two test aircraft, and then flown in real-world conditions to determine if the goals of improving aircraft efficiency and airport throughput during high-density arrival operations could be met. This paper describes the speed behavior of the IM avionics prototype, focusing on the speed command rate and the number of speed increases

Committed to Oprah, Homer, or House: Using the Investment Model to Understand Parasocial Relationships

People can develop close relationships with media figures viewed on TV. Across two studies we examined the extent to which satisfaction with, alternatives to, and investments in such parasocial relationships (PSR) account for feelings of commitment toward favored TV characters. In Study 1, satisfaction and investments positively predicted commitment to fictional TV characters, whereas the alternative of not following any TV character negatively predicted commitment to the PSR. In Study 2, we tested the bases of the investment model as predictors of commitment to fictional (e.g., Homer Simpson) versus nonfictional (e.g., Oprah Winfrey) TV characters. As in Study 1, for both fictional and nonfictional characters, commitment level was significantly predicted by levels of satisfaction and investments. However, the alternative of not following any character was significantly associated with commitment only for fictional characters. Results support the use of the investment model to understand processes underlying PSRs

An Investigation of Interval Management Displays

NASA's first Air Traffic Management (ATM) Technology Demonstration (ATD-1) was created to transition the most mature ATM technologies from the laboratory to the National Airspace System. One selected technology is Interval Management (IM), which uses onboard aircraft automation to compute speeds that help the flight crew achieve and maintain precise spacing behind a preceding aircraft. Since ATD-1 focuses on a near-term environment, the ATD-1 flight demonstration prototype requires radio voice communication to issue an IM clearance. Retrofit IM displays will enable pilots to both enter information into the IM avionics and monitor IM operation. These displays could consist of an interface to enter data from an IM clearance and also an auxiliary display that presents critical information in the primary field-of-view. A human-in-the-loop experiment was conducted to examine usability and acceptability of retrofit IM displays, which flight crews found acceptable. Results also indicate the need for salient alerting when new speeds are generated and the desire to have a primary field of view display available that can display text and graphic trend indicators

Statistical Engineering in Air Traffic Management Research

No abstract availabl

SURF IA Conflict Detection and Resolution Algorithm Evaluation

The Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF IA) algorithm was evaluated in a fast-time batch simulation study at the National Aeronautics and Space Administration (NASA) Langley Research Center. SURF IA is designed to increase flight crew situation awareness of the runway environment and facilitate an appropriate and timely response to potential conflict situations. The purpose of the study was to evaluate the performance of the SURF IA algorithm under various runway scenarios, multiple levels of conflict detection and resolution (CD&R) system equipage, and various levels of horizontal position accuracy. This paper gives an overview of the SURF IA concept, simulation study, and results. Runway incursions are a serious aviation safety hazard. As such, the FAA is committed to reducing the severity, number, and rate of runway incursions by implementing a combination of guidance, education, outreach, training, technology, infrastructure, and risk identification and mitigation initiatives [1]. Progress has been made in reducing the number of serious incursions - from a high of 67 in Fiscal Year (FY) 2000 to 6 in FY2010. However, the rate of all incursions has risen steadily over recent years - from a rate of 12.3 incursions per million operations in FY2005 to a rate of 18.9 incursions per million operations in FY2010 [1, 2]. The National Transportation Safety Board (NTSB) also considers runway incursions to be a serious aviation safety hazard, listing runway incursion prevention as one of their most wanted transportation safety improvements [3]. The NTSB recommends that immediate warning of probable collisions/incursions be given directly to flight crews in the cockpit [4]