Studying Boarding Strategies

Studying Boarding Strategies

The number of passengers carried by commercial aircraft has increased dramatically over the past 50 years, closely in-step with advances in aircraft design. This makes unloading and loading an aircraft, called turntime, critical to the success of the airport, the aircraft and the airlines. A number of mathematical algorithms have been developed over the years that purport to determine the most efficient boarding strategy for passengers by decreasing turn time. This research evaluated the boarding strategies most often used by the airlines and algorithms used to predict boarding efficiency.

Role: Lead UX Researcher
Tools: Ethnographic Observations and Video Collections, ARENA simulation, Video Data Analysis
Note:  This was my Master Thesis at Embry Riddle Aeronautical University

The models used were obtained from the literature and from personal communication with the authors. The Kruskal-Wallis one way analysis of variance test was used to determine that the Random boarding strategy had the greatest boarding rate of 16 passengers per minute (PPM) and the Rotating Zone strategy had the slowest at 8 PPM. The Ferarri and Nagel sensitivity analysis algorithm was consistently predictive of the empirical observations of boarding strategies. The usefulness of the modeling approach to predicting boarding efficiency is discussed as well as alternative means for enplaning and deplaning large super jumbo aircraft.

In order to compare data created with algorithms proposed by different authors utilizing modeling techniques, we collected empirical data during a project funded by Boeing.

Ethnographic Research

I was one of the researchers traveling to several airports all over the country and abroad, taking measurements from each airline and strategy in several variables including anthropometrics (height, weight, age and gender of passengers), time to load/unload the aircraft, different interference times produced by either seat or bin interferences, and loading technique by airline. Data was collected through observation and video collection and was then analyzed through statistical and graphical evaluations, as well as video evaluations.

Data Analysis and Modeling

I had the honor to work personally with several of the modeling authors including:  Van den Briel, Ferrari and Nagel, and Jason Stephen.  With their help, I was able to run their models with different configurations and compare them to the empirical data.

Additionally, during part of the project I worked at Boeing Commercial Aviation where I had the opportunity to ask questions,  get insights and have great conversations with aerospace engineers, pilots and human factors experts that have a direct impact in the design, manufacturing and safety of aircrafts.

Results

As expected, no model was able to recreate empirical data.  Even with our advances in technology, mathematics cannot be used to explain human behavior and the limitations of the modeling tools need to be addressed very carefully before making radical changes in design or operations.  Models need to be constantly tested and “questioned” in order to provide any relevant or useful information.

I learned a lot during the process of my thesis.  From data collection, to experiment design and set up, sharing information with other authors, and working in the aviation industry, every step of the process provided me with tools that I plan to utilize during the extent of my career.

* Photo Credits:  Unknown photographer, 1969.

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