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Networks and Spatial Economics

, Volume 19, Issue 3, pp 669–695 | Cite as

Airport Taxi Situation Awareness with a Macroscopic Distribution Network Analysis

  • Jianan YinEmail author
  • Minghua Hu
  • Yuanyuan Ma
  • Ke Han
  • Dan Chen
Article
  • 210 Downloads

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 

Nomenclature

ATM

Air traffic management

MDN

Macroscopic distribution network

TSI

Taxi situation index

ATTI

Aircraft taxi time index

SIFI

Surface instantaneous flow index

SCFI

Surface cumulative flow index

AQLI

Aircraft queue length index

SRDI

Slot resource demand index

Notes

Acknowledgments

This work is supported by the China Postdoctoral Science Foundation (Grant No. 2017M611809), Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No. 1701099C) and National Natural Science Foundation of China (Grant Nos. 61573181, 61671237 and U1633126).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Department of Civil and Environmental EngineeringImperial College LondonLondonUK
  3. 3.College of Civil AviationNanjing University of Aeronautics and AstronauticsNanjingChina
  4. 4.State Key Laboratory of Air Traffic Management System and TechnologyThe 28th Research Institute of China Electronics Technology Group CorporationNanjingChina

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