We examine pre-COVID-19 long-haul commercial air passenger traffic between London Heathrow Airport (LHR) in the United Kingdom and several destinations in Australia (‘the Kangaroo Route’) in order to identify itinerary attributes that affect the passenger’s choice of connecting hub airports. Such insights can be useful to airlines and hub airports seeking to increase their share of the traffic between specific origin-destination pairs. The statistical challenge is that booking and fare data for such flight itineraries are available only on an aggregate monthly basis whereas actual itinerary options vary in frequency and connecting times on a daily basis. This contrasts with similar studies in the U.S. and Europe where finer-grained booking data are available. We extract significant features of the detailed connection data to represent the single monthly aggerate itinerary and report on the success of fitting multinomial choice models to the resulting data.

Mingmei Li is currently a Research Fellow at the Aviation Studies Institute (ASI). Under the guidance of Professor Peter Jackson, she works on research projects related to airport connectivity. She graduated from the Singapore University of Technology and Design (SUTD) in 2019 with a Ph.D. degree in Information Systems Technology and Design (ISTD). Mingmei received her B.E. and M.E. degrees in Communication Engineering from Shandong University and Southeast University, China, in 2011 and 2014, respectively. She is interested in network economics, game theory, and data science.

Air traffic network congestion results in higher levels of flight rerouting, ground/en-route delays, and in extreme cases, cancellation. Due to the high interconnectivity of airline flight schedules, the impacts of these adverse events can propagate throughout the network, even to non-capacitated airports or regions. Focusing on the propagation of flight delays, we axiomatically examine the “ripple effects” of air traffic congestion delays on downstream flights, whose root causes can be identified upstream. Using trajectory-based ATFM optimisation model, which considers the capacities of airport runways and en-route sectors, we aim to minimise flight arrival delays and maximise the success rate of passenger hub connections. The distinguishing aspect of the research is our root cause delay analysis methodology which satisfies fundamental axioms – the study of direct delay transmissions between distinct aircraft and continued flights through a granular MIP solution results in a high-resolution performance measure for congestion delays in an air traffic network.

Benjamin Tan is currently a Research Fellow at the Aviation Studies Institute (ASI). Under the guidance of Professor Peter Jackson, he works on projects related to the optimisation and analysis of air traffic in the Network Capacity Network Collaboration projects. Benjamin received his Ph.D. in Engineering Systems and Design (ESD) from the Singapore University of Technology and Design (SUTD) in 2021. Prior to his Ph.D. studies, Benjamin graduated summa cum laude from SUTD in 2016, receiving a Bachelor of Engineering (ESD) with a specialisation in Business Analytics.

For more information about the ESD Seminar, please email esd_invite@sutd.edu.sg