Identification, Prediction, and Control of Disruptions in Airline Networks
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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.
***Event will take place in hybrid format. The location for in-person attendance will be room 1311 EECS. Attendance will also be possible via Zoom. Zoom link and password will be distributed to the Controls Group e-mail list-serv.
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