As part of ongoing research, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework to assist policymakers in identifying impacts on the U.S. air transportation system (ATS) of potential policies and technology related to the implementation of the Next Generation Air Transportation System (NextGen). This framework, called the Air Transportation System Evolutionary Simulation (ATS-EVOS), integrates multiple models into a single process flow to best simulate responses by U.S. commercial airlines and other ATS stakeholders to NextGen-related policies, and in turn, how those responses impact the ATS. Development of this framework required NASA and LMI to create an agent-based model of airline and passenger behavior. This Airline Evolutionary Simulation (AIRLINE-EVOS) models airline decisions about tactical airfare and schedule adjustments, and strategic decisions related to fleet assignments, market prices, and equipage. AIRLINE-EVOS models its own heterogeneous population of passenger agents that interact with airlines; this interaction allows the model to simulate the cycle of action-reaction as airlines compete with each other and engage passengers. We validated a baseline configuration of AIRLINE-EVOS against Airline Origin and Destination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments. These experiments demonstrated AIRLINE-EVOS's capabilities in responding to an input price shock in fuel prices, and to equipage challenges in a series of analyses based on potential incentive policies for best equipped best served, optimal-wind routing, and traffic management initiative exemption concepts..
[1]
Shahab Hasan,et al.
Cost Benefit Analysis of a Near Term Implementation of Dynamic Weather Re-Routing Concepts and Technologies
,
2012
.
[2]
J. Gareth Polhill,et al.
The ODD protocol: A review and first update
,
2010,
Ecological Modelling.
[3]
Birgit Müller,et al.
A standard protocol for describing individual-based and agent-based models
,
2006
.
[4]
S. Nielsen.
Agent-Based and Individual-Based Modeling: A Practical Introduction, S.F. Railsback, V. Grimm. Princeton University Press, Princeton (2011), 352 pp., $99.50 (cloth), $55.00 (paper), ISBN:9780691136738 (cloth), ISBN:9780691136745 (paper)
,
2012
.
[5]
Vikram Manikonda,et al.
FAST -TIME SIMULATION SYSTEM FOR ANALYSIS OF ADVANCED AIR TRANSPORTATION CONCEPTS
,
2002
.
[6]
A. J. Field,et al.
Phytoplankton co-existence: Results from an individual-based simulation model
,
2006
.