Board 2: Exploring Average Taxi Times at U.S. Hub Airports with ASDE-X

Jiansen Wang; Shantanu Gupta; Mary E. Johnson

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: June 26, 2024
Conference Session: Aerospace Division (AERO) Poster Session
Tagged Division: Aerospace Division (AERO)
Page Count: 9
DOI: 10.18260/1-2--46765
Permanent URL: https://peer.asee.org/46765
Download Count: 89

Paper Authors

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Jiansen Wang Purdue University orcid.org/0009-0001-2663-834X

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Jiansen is a PhD candidate in the School of Aviation and Transportation Technology at Purdue University. He began his PhD study in Technology at Purdue University in 2021, under the supervision of Dr. Mary E Johnson. His research focuses on improving airport capacity and sustainability. Jiansen completed his M.S degree in Aviation and Aerospace Management at Purdue University in 2020. During his masters study, Jiansen earned second place award in Airport Cooperative Research Program Competition in 2020. Prior to graduate school, Jiansen completed his B.S. degree in Engineering from Civil Aviation University of China.

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Shantanu Gupta Purdue University orcid.org/0000-0002-5619-7012

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Shantanu Gupta is a PhD candidate in the School of Aviation and Transportation Technology at Purdue University with Dr. Mary E. Johnson. He earned his B.E in Mechanical Engineering from Visvesvaraya Technological University, India, M.S in Aviation and Aerospace Management and a Graduate Certificate in Applied Statistics from Purdue University, West Lafayette. Mr. Gupta worked with Dr. Johnson as research assistant on the PEGASAS Project 33 – Augmented Weather Information Project (AWIP) and Project 35 - Enhanced Hands-Minimized Weather Interfaces (EHMI) for pilots.

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Mary E. Johnson Ph.D. Purdue University orcid.org/0000-0002-6572-0979

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Mary E. Johnson is a Professor and Associate Head for Graduate Studies and Research in the School of Aviation and Transportation Technology (SATT) at Purdue University in West Lafayette, Indiana. She earned her BS, MS and PhD in Industrial Engineering from UTArlington

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Abstract

Airport taxi times affect operation efficiency and congestion, fuel consumption, and aircraft emissions. Aircraft taxi time refer to the time it takes for an aircraft to move from the gate to takeoff, or to move from landing to the gate. Efforts to better understand and reduce airport taxi times may potentially improve airport capacity and reduce fuel usage, costs, and emissions. Airport Surface Detection Equipment, Model X (ASDE-X) is a surveillance system that provides location and movement information of aircraft and vehicles on the airport to air traffic controllers. ASDE-X was developed to reduce the Category A and B runway incursions at airports by providing continuous information of aircraft and vehicle location on airport movement areas. This equipment was implemented at 35 major U.S. airports. The FAA defines U.S. airports as Large, Medium, Small, and Non hub airports. In National Plan of Integrated Airport System (NPIAS), the Appendix A: List of NPIAS Airports provides a list of U.S. airports and their hub classifications (Small, Medium, and Large hub). The Aviation System Performance Metrics (ASPM) dataset from the FAA publishes airport and airline operation data of 77 airports in the U.S. In the dataset, the quarter-hour taxi-in time and quarter-hour taxi-out time of the 77 airports are given. In this study, the researchers explore the possible effect of ASDE-X implementation on airport taxi-times at 71 U.S. airports in the ASPM dataset. ASDE-X is installed at 35 airports and not installed in the other 36 airports in the ASPM dataset. In this paper, taxi times for the ASPM airports with ASDE-X are compared to the airports without ASDE-X. Identifying potential factors affecting airport taxi times may help researchers build better taxi time prediction model and may help airport managers to make better decisions to improve airport efficiency and capacity. Educators may use this research to teach large-sample data collection, data cleaning and consolidation, design of experiment, and statistical and graphical methods to answer research questions in undergraduate engineering courses. Undergraduate aerospace or aviation students may improve their comprehension of taxi times, ASDE-X, and airport operations from this research.

Citation
Format

Wang, J., & Gupta, S., & Johnson, M. E. (2024, June), Board 2: Exploring Average Taxi Times at U.S. Hub Airports with ASDE-X Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--46765

TY - CPAPER
AB - Airport taxi times affect operation efficiency and congestion, fuel consumption, and aircraft emissions. Aircraft taxi time refer to the time it takes for an aircraft to move from the gate to takeoff, or to move from landing to the gate. Efforts to better understand and reduce airport taxi times may potentially improve airport capacity and reduce fuel usage, costs, and emissions. Airport Surface Detection Equipment, Model X (ASDE-X) is a surveillance system that provides location and movement information of aircraft and vehicles on the airport to air traffic controllers. ASDE-X was developed to reduce the Category A and B runway incursions at airports by providing continuous information of aircraft and vehicle location on airport movement areas. This equipment was implemented at 35 major U.S. airports.
The FAA defines U.S. airports as Large, Medium, Small, and Non hub airports. In National Plan of Integrated Airport System (NPIAS), the Appendix A: List of NPIAS Airports provides a list of U.S. airports and their hub classifications (Small, Medium, and Large hub). The Aviation System Performance Metrics (ASPM) dataset from the FAA publishes airport and airline operation data of 77 airports in the U.S. In the dataset, the quarter-hour taxi-in time and quarter-hour taxi-out time of the 77 airports are given. In this study, the researchers explore the possible effect of ASDE-X implementation on airport taxi-times at 71 U.S. airports in the ASPM dataset. ASDE-X is installed at 35 airports and not installed in the other 36 airports in the ASPM dataset. In this paper, taxi times for the ASPM airports with ASDE-X are compared to the airports without ASDE-X.
Identifying potential factors affecting airport taxi times may help researchers build better taxi time prediction model and may help airport managers to make better decisions to improve airport efficiency and capacity. Educators may use this research to teach large-sample data collection, data cleaning and consolidation, design of experiment, and statistical and graphical methods to answer research questions in undergraduate engineering courses. Undergraduate aerospace or aviation students may improve their comprehension of taxi times, ASDE-X, and airport operations from this research.
AU - Jiansen Wang
AU - Shantanu Gupta
AU - Mary E. Johnson Ph.D.
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Board 2: Exploring Average Taxi Times at U.S. Hub Airports with ASDE-X
UR - https://peer.asee.org/46765
DO - 10.18260/1-2--46765
ER -