NDLI: A Scenario-Based Optimization Approach to Robust Estimation of Airport Capacity

Content Provider	IEEE Xplore Digital Library
Author	Fei Ju Kaiquan Cai Yang Yang Yuan Gao
Copyright Year	2015
Description	Author affiliation: IT Dept., Beijing Capital Int. Airport, Beijing, China (Yuan Gao) \|\| Beijing Key Lab. of Network Enabled Collaborative ATM, Beihang Univ., Beijing, China (Fei Ju; Kaiquan Cai; Yang Yang)
Abstract	Estimation of airport capacity plays a fundamental role in planning air traffic flow around the airport. Due to the impact of various dynamic factors on practical airport operation, e.g., the varying meteorological condition and changing fleet mix, airport capacity is characterized by uncertainties. The robustness of the existing iconic estimation approaches is challenged. This paper proposes a scenario-based optimization approach to robust estimation of airport capacity in the presence of the operational uncertainties. The capacity envelope identified through empirical analysis is associated with some probabilistic level and the estimation problem is then formulated as a chance-constrained optimization program approximately solved via scenario approach. Case study using real data set collected from Beijing Capital International Airport shows that the capacity envelope obtained by the proposed approach is more robust than two iconic approaches, i.e., proportion-based filtration approach and the quantile regression approach.
Starting Page	2066
Ending Page	2071
File Size	228102
Page Count	6
File Format	PDF
ISSN	21530017
e-ISBN	9781467365963
DOI	10.1109/ITSC.2015.334
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-15
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Airports Estimation Optimization Uncertainty Capacity planning Robustness Probabilistic logic scenario approach airport capacity robust estimation airport operational uncertainty chance-constrained optimization
Content Type	Text
Resource Type	Article

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