Design and simulation of airport congestion control algorithms

This paper proposes a stochastic model of runway departures and a dynamic programming algorithm for their control at congested airports. Using a multi-variable state description that includes the capacity forecast, the runway system is modeled as a semi-Markov process. The paper then introduces a qu...

Full description

Bibliographic Details
Main Authors: Simaiakis, Ioannis (Author), Balakrishnan, Hamsa (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2015-05-01T18:28:14Z.
Subjects:
Online Access:Get fulltext
LEADER 01519 am a22001933u 4500
001 96876
042 |a dc 
100 1 0 |a Simaiakis, Ioannis  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Aeronautics and Astronautics  |e contributor 
100 1 0 |a Balakrishnan, Hamsa  |e contributor 
700 1 0 |a Balakrishnan, Hamsa  |e author 
245 0 0 |a Design and simulation of airport congestion control algorithms 
260 |b Institute of Electrical and Electronics Engineers (IEEE),   |c 2015-05-01T18:28:14Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/96876 
520 |a This paper proposes a stochastic model of runway departures and a dynamic programming algorithm for their control at congested airports. Using a multi-variable state description that includes the capacity forecast, the runway system is modeled as a semi-Markov process. The paper then introduces a queuing system for modeling the controlled departure process that enables the efficient calculation of optimal pushback policies using decomposition techniques. The developed algorithm is simulated at Philadelphia International Airport, and compared to other potential control strategies including a threshold-policy. The algorithm is also shown to effectively adapt to changes in airport departure capacity, maintain runway utilization and efficiently manage congestion. 
520 |a National Science Foundation (U.S.) (Award 0931843) 
546 |a en_US 
655 7 |a Article 
773 |t Proceedings of the 2014 American Control Conference