Evaluating Air Traffic Flow Management in a Collaborative Decision-Making Environment

Abstract

The collaborative decision-making (CDM) framework introduced into ground delay programs in the late 1990s is an integral component of FAA's traffic flow management (TFM) procedures. CDM allows FAA to act as a mediator when managing TFM programs, transferring as much decision making as possible to the individual airlines. Although this approach has been highly successful in practice, it creates a new question for the research community: How should proposed enhancements to TFM be evaluated in a CDM environment? A sequential evaluation procedure, developed in this paper, addresses this question. The procedure includes airline disruption responses and a quasi-compression operation, attempting to mimic the three-stage CDM process. To model airline disruption responses, an integer optimization model was developed to balance operational and passenger considerations in determining which flights to cancel, swap, or delay. The value of this procedure is demonstrated by analyzing an optimization-based TFM approach in the CDM environment.

Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management

“…In complement, AI for Blockchain provides a medium for a structured peer-to-peer network interaction of high-level stakeholders (created via Blockchain for AI platforms) that represent primary actors in the air transportation network, such as multiple airlines and airports. As such, emergent behavior in terms of different collaborative decision-making (Ball et al, 2001;Fearing & Barnhart, 2011) routines for airline disruption management can be enabled through autonomic AI computing.…”