Airport Taxi Situation Awareness with a Macroscopic Distribution Network Analysis

Jianan Yin (), Minghua Hu, Yuanyuan Ma, Ke Han and Dan Chen
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Jianan Yin: Nanjing University of Aeronautics and Astronautics
Minghua Hu: Nanjing University of Aeronautics and Astronautics
Yuanyuan Ma: The 28th Research Institute of China Electronics Technology Group Corporation
Ke Han: Imperial College London
Dan Chen: Nanjing University of Aeronautics and Astronautics

Networks and Spatial Economics, 2019, vol. 19, issue 3, No 1, 669-695

Abstract: Abstract This paper proposes a framework for airport taxi situation awareness to enhance the assessment of aircraft ground movements in complex airport surfaces. Through a macroscopic distribution network (MDN) of arrival and departure taxi processes in a spatial-temporal domain, we establish two sets of taxi situation indices (TSIs) from the perspectives of single aircraft and the whole network. These TSIs are characterized into five categories: aircraft taxi time indices (ATTIs), surface instantaneous flow indices (SIFIs), surface cumulative flow indices (SCFIs), aircraft queue length indices (AQLIs), and slot resource demand indices (SRDIs). The coverage of the TSIs system is discussed in detail based on the departure and arrival reference aircraft. A real-world case study of Shanghai Pudong airport demonstrates significant correlations among some of the proposed TSIs such as the ATTIs, SCFIs and AQLIs. We identify the most crucial influencing factors of the taxi process and propose two new metrics to assess the taxi situation at the aircraft and network levels, by establishing taxi situation assessment models instead of using two systems of multiple TSIs. The findings can provide significant references to decision makers regarding airport ground movements for the purposes of air traffic scheduling and congestion control in complex airports.

Keywords: Macroscopic distribution network; Situation awareness; Complexity; Airport performance; Air traffic management (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11067-018-9402-5

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