Statistical engineering approach to improve the realism of computer-simulated experiments with aircraft trajectory clustering

ABSTRACT

This article presents a statistical engineering approach for clustering aircraft trajectories. The clustering methodology was developed to address the need to incorporate more realistic trajectories in fast-time computer simulations used to evaluate an aircraft spacing algorithm. The methodology is a combination of Dynamic Time Warping and k-Means clustering, and can be viewed as one of many possible solutions to the immediate problem. The implementation of this statistical engineering approach is also repeatable, scalable, and extendable to the investigation of other air traffic management technologies. Development of the clustering methodology is presented in addition to an application and description of results.

KEYWORDS:

• dynamic time warping
• gap statistic
• k-means
• statistical engineering
• trajectory clustering

About the authors

Sara Wilson is a statistician at NASA Langley Research Center. She currently supports the Airspace Operations and Safety Program's Airspace Technology Demonstration Project. Sara earned a B.S. in mathematics and computer science from the College of William and Mary, an M.S. in Operations Research from Georgia Tech, and an M.S. and Ph.D. in Statistics from Virginia Tech.

Kurt Swieringa is a research engineer at the NASA Langley Research Center. He received bachelor's degrees in engineering physics and aerospace engineering from the University of Michigan in 2010 and a master's degree in aerospace engineering from the Georgia Institute of Technology in 2013. Since then, Kurt has participated in and led several air traffic management simulations investigating new flight deck automation and air traffic controller tools that have the potential to improve the efficiency of arrival operations into busy airports.

Robert Leonard is a Research Assistant and Ph.D. candidate in the Systems Modeling and Analysis Program at Virginia Commonwealth University. He holds a B.S.Ed. in Integrated Mathematics from Ohio University. His research interests include experimental design and analysis, model selection methods, and the advancement of math and science education. He is also a member of the American Statistical Association.

Evan Freitag received a dual bachelor's degree with majors in mathematics and philosophy and a master's in mathematics from the State University of New York at Potsdam in 2013. In 2015, while a graduate research assistant at the NASA Langley Research Center, he completed a master's in applied mathematics with a concentration in statistics at Virginia Commonwealth University.

David Edwards is an Associate Professor of Statistics in the Department of Statistical Sciences and Operations Research at Virginia Commonwealth University. He earned a B.S. in mathematics from Virginia Tech and an M.S. and Ph.D. in statistics from the University of Tennessee. His research focuses on design and analysis of experiments and reliability data analysis. He is on the editorial review boards of Journal of Quality Technology and Quality Engineering.

Funding

The authors appreciate the support of the NASA Airspace Operations and Safety Program Airspace Technology Demonstration Project for funding this research effort.