Dr. Max Li
Visiting Assistant Professor
Aerospace Engineering
University of Michigan

Disruptions in the air transportation system often lead to flight delays and cancellations. In order to better predict the impact of disruptions, as well as provide more targeted and proactive system recovery actions, it is critical to unambiguously identify key characteristics such as: (1) When did a disruption begin, and how long did it last for, (2) Where did a disruption occur, and with what intensity, and (3) how will an ongoing disruption evolve. Identifying performance measures pertaining to, e.g., the duration or intensity of disruptions is straightforward for individual airports; however, this goal is significantly more challenging for a large, geographically disparate, and interconnected network of airports. Furthermore, a resilient air traffic management model ideally allows for a rapid recovery after such disruptions, but these models are often complicated by two factors: The lack of a high-fidelity model for predicting and controlling airport delay dynamics, and poor computational tractability of tailored large-scale flight rescheduling optimization problems.

I will discuss two recent interconnected works, the first of which addresses the identification and prediction of disruptions and recoveries in airline networks. We accomplish this by first formalizing the notion of disruption-recovery trajectories (DRTs). We show that these DRTs capture information regarding both the magnitude and spatial impact of disruptions in airline networks. Using this DRT framework, we identify past disruptions and recovery characteristics for four major US airlines, analyze airline-specific relationships between flight delays and cancellations, as well as predict short-term evolution of DRTs. In the second part, we combine the DRT framework (macroscopic) with a flight delay assignment optimization model (microscopic), resulting in a two-stage hierarchical control strategy for rescheduling aircraft (i.e., assigning delays) after network disruptions. If time permits, I will briefly touch on ongoing work related to an implementation of our hierarchical control strategy with multiple agents (i.e., airlines).

The work on identifying and predicting DRTs is joint work with Karthik Gopalakrishnan (Stanford University), Xiyitao Zhu, Aritro Nandi, Lavanya Marla (University of Illinois at Urbana-Champaign), and Hamsa Balakrishnan (MIT). The work on flight delay assignments with hierarchical control objectives is joint work with Christopher Chin (MIT), Hamsa Balakrishan, and Karthik Gopalakrishnan.

About the speaker...
Max is a Visiting Assistant Professor of Aerospace Engineering at the University of Michigan – Ann Arbor and will be starting as an Assistant Professor of Aerospace Engineering in Fall 2022, with a courtesy appointment in Industrial and Operations Engineering. He is also currently a Senior Data Scientist at MITRE’s Center for Advanced Aviation System Development (CAASD). Max received his PhD in Aerospace Engineering from the Massachusetts Institute of Technology in 2021. He earned his MSE in Systems Engineering and BSE in Electrical Engineering and Mathematics, both from the University of Pennsylvania, in 2018. Broadly, he is interested in the analysis, control, and optimization of networks and networked processes, signal processing over irregular domains and manifolds, and geometric/topological data analysis, with an eye towards applications in air transportation systems and other societal-scale networks. He is the recipient of the Federal Aviation Administration RAISE Award (2018), a National Science Foundation Graduate Research Fellowship (2018), and the Wellington and Irene Loh Fellowship from MIT (2019), as well as several best paper awards from ICRAT and the ATM R&D Seminar, two joint FAA-Eurocontrol conferences.