Airport Taxi Situation Awareness with a Macroscopic Distribution Network Analysis

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.

[1]  Lance Sherry,et al.  Airport Taxi-Out Prediction Using Approximate Dynamic Programming , 2008 .

[2]  M. J. Soomer,et al.  Optimisation of Airport Taxi Planning , 2003 .

[3]  Hamsa Balakrishnan,et al.  A Framework for Coordinated Surface Operations Planning at Dallas-Fort Worth International Airport , 2007 .

[4]  Nicolas Durand,et al.  Aircraft ground traffic optimization , 2001 .

[5]  Nicolas Durand,et al.  Aircraft ground traffic optimisation using a Genetic Algorithm , 2001 .

[6]  Gillian Clare,et al.  Optimization of Taxiway Routing and Runway Scheduling , 2008, IEEE Transactions on Intelligent Transportation Systems.

[7]  Li Weigang,et al.  Using Ant Algorithm to Arrange Taxiway Sequencing in Airport , 2014 .

[8]  Robert A. Shumsky.  Dynamic statistical models for the prediction of aircraft take-off times , 1995 .

[9]  Annette Temme,et al.  Taxi routing for aircraft: Creation and ControllingGround movements with time constraints , 2012 .

[10]  Anna Norin,et al.  Airport Logistics Modeling and Optimizing the Turn-Around Process , 2008 .

[11]  Shamsuddin S. Khan,et al.  Reinforcement learning for taxi-out time prediction: An improved Q-learning approach , 2015, 2015 International Conference on Computing and Network Communications (CoCoNet).

[12]  Edmund K. Burke,et al.  The trade-off between taxi time and fuel consumption in airport ground movement , 2013, Public Transp..

[13]  Ángel Marín,et al.  Airport management: taxi planning , 2006, Ann. Oper. Res..

[14]  Regina R. Clewlow,et al.  Impact of Arrivals on Departure Taxi Operations at Airports , 2010 .

[15]  Ryota Mori,et al.  Aircraft Ground-Taxiing Model for Congested Airport Using Cellular Automata , 2013, IEEE Transactions on Intelligent Transportation Systems.

[16]  Jean-Philippe Gayon,et al.  The aircraft ground routing problem: Analysis of industry punctuality indicators in a sustainable perspective , 2016, Eur. J. Oper. Res..

[17]  P. R. Waal,et al.  An Optimisation Model for Airport Taxi Scheduling , 2004 .

[18]  Jimmy Krozel,et al.  Improved Taxi Prediction Algorithms for the Surface Management System , 2002 .

[19]  Waqar Malik,et al.  A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem , 2010 .

[20]  L. Sherry,et al.  Taxi-out Prediction Using Approximate Dynamic Programming , 2007 .

[21]  Sanjiv Sharma,et al.  Receding Horizon, Iterative Optimization of Taxiway Routing and Runway Scheduling , 2009 .

[22]  B.S. Levy,et al.  Accurate OOOI Data: Implications for Efficient Resource Utilization , 2006, 2006 ieee/aiaa 25TH Digital Avionics Systems Conference.

[23]  S. Rathinam,et al.  AN OPTIMIZATION MODEL FOR REDUCING AIRCRAFT TAXI TIMES AT THE DALLAS FORT WORTH INTERNATIONAL AIRPORT , 2008 .

[24]  Henk Hesselink,et al.  Mantea Departure Sequencer: Increasing Airport Capacity By Planning Optimal Sequences , 2002 .

[25]  Dietmar Böhme,et al.  Coordinated Arrival Departure Management , 2007 .

[26]  Ángel Marín,et al.  Network design: taxi planning , 2008, Ann. Oper. Res..

[27]  Lance Sherry,et al.  Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: A case-study of Tampa Bay departures , 2010 .

[28]  Edmund K. Burke,et al.  A combined statistical approach and ground movement model for improving taxi time estimations at airports , 2013, J. Oper. Res. Soc..

[29]  Qing Liu,et al.  Airport Taxi Scheduling Strategy based on Cooperate Particle Swarm Optimization Algorithm , 2011 .

[30]  Edmund K. Burke,et al.  The Airport Ground Movement Problem: Past and Current Research and Future Directions , 2010 .

[31]  John-Paul Clarke,et al.  Queuing Model for Taxi-Out Time Estimation , 2002 .

[32]  Nicolas Durand,et al.  Airport surface management and runways scheduling , 2009 .