Flight and passenger delay assignment optimization strategies

This paper compares different optimization strategies for the minimization of flight and passenger delays at two levels: pre-tactical, with on-ground delay at origin, and tactical, with airborne delay close to the destination airport. The optimization model is based on the ground holding problem and uses various cost functions. The scenario considered takes place in a busy European airport and includes realistic values of traffic. A passenger assignment with connections at the hub is modeled. Statistical models are used for passenger and connecting passenger allocation, minimum time required for turnaround and tactical noise; whereas uncertainty is also introduced in the model for tactical noise. Performance of the various optimization processes is presented and compared to ration by schedule results.

[1]  Lorenzo Castelli,et al.  Improved flexibility and equity for airspace users during demand-capacity imbalance - an introduction to the user-driven prioritisation process , 2016 .

[2]  Eugene P. Gilbo,et al.  Airport capacity: representation, estimation, optimization , 1993, IEEE Trans. Control. Syst. Technol..

[3]  Wen-Hua Chen,et al.  Genetic algorithm based on receding horizon control for arrival sequencing and scheduling , 2005, Eng. Appl. Artif. Intell..

[4]  Sanjiv Shresta,et al.  Analysis of Continuous Descent Benefits and Impacts During Daytime Operations , 2009 .

[5]  Mark Hansen,et al.  Generating day-of-operation probabilistic capacity scenarios from weather forecasts , 2013 .

[6]  Xavier Prats,et al.  Cruise speed reduction for ground delay programs: A case study for San Francisco International Airport arrivals , 2013 .

[7]  David J. Lovell,et al.  Algorithms for Dynamic Resequencing of En Route Flights to Relieve Terminal Congestion , 2012 .

[8]  George L. Nemhauser,et al.  Air Transportation: Irregular Operations and Control , 2007 .

[9]  Sandrine Carlier,et al.  ENVIRONMENTAL IMPACT OF AIR TRAFFIC FLOW MANAGEMENT DELAYS , 2007 .

[10]  Adeline de Villardi de Montlaur,et al.  Delay Assignment Optimization Strategies at Pre- Tactical and Tactical Levels , 2015 .

[11]  Daniel DeLaurentis,et al.  Evaluation of Continuous Descent Approach as a Standard Terminal Airspace Operation , 2011 .

[12]  Luis Delgado,et al.  Hub operations delay recovery based on cost optimisation - Dynamic cost indexing and waiting for passengers strategies , 2016 .

[13]  M. Tielrooij.  Predicting Arrival Time Uncertainty from Actual Flight Information , 2015 .

[14]  Kenneth Kuhn,et al.  Ground delay program planning: Delay, equity, and computational complexity , 2013 .

[15]  Fedja Netjasov,et al.  Utilizing schedule buffers to reduce propagated delay A new approach for tactical Air Traffic Flow Management slot allocation , 2016 .

[16]  Fedja Netjasov,et al.  Air Traffic Flow Management slot allocation to minimize propagated delay and improve airport slot adherence , 2017 .

[17]  Harshad Khadilkar,et al.  Optimal control of airport operations with gate capacity constraints , 2013, 2013 European Control Conference (ECC).

[18]  Xavier Prats,et al.  Operating cost based cruise speed reduction for ground delay programs: Effect of scope length , 2014 .

[19]  Paolo Dell'Olmo,et al.  A dynamic programming approach for the airport capacity allocation problem , 2003 .

[20]  Lance Sherry,et al.  Analysis of performance and equity in ground delay programs , 2008 .

[21]  Andrew J. Cook.  Passenger-Oriented Enhanced Metrics , 2011 .

[22]  Roberto Sabatini,et al.  A case study of arrival and departure managers cooperation for reducing airborne holding times at destination airports , 2012, ICAS 2012.

[23]  David A. Hinton,et al.  Design of an Aircraft Vortex Spacing System for Airport Capacity Improvement , 2000 .

[24]  Amedeo R. Odoni,et al.  Dynamic Ground-Holding Policies for a Network of Airports , 1994, Transp. Sci..

[25]  G. Inalhan,et al.  Towards a secure trading of aviation CO2 allowance , 2016 .

[26]  Hartmut Fricke,et al.  Turnaround prediction concept: proofing and control options by microscopic process modelling GMAN proof of concept & possibilities to use microscopic process scenarios as control options , 2014 .

[27]  Subramanian Prakash,et al.  A simulation study to investigate runway capacity using TAAM , 2002, Proceedings of the Winter Simulation Conference.

[28]  Jaewoo Jung,et al.  Arrival Scheduling with Shortcut Path Options and Mixed Aircraft Performance , 2015 .

[29]  Luis Delgado.  European route choice determinants , 2015 .

[30]  Michael O. Ball,et al.  Stochastic optimization models for ground delay program planning with equity–efficiency tradeoffs , 2013 .

[31]  Jonathan F. Bard,et al.  Reallocating arrival slots during a ground delay program , 2008 .

[32]  Alan R. Groskreutz.  Required Surveillance Performance for reduced minimal-pair arrival separations , 2015 .

[33]  Shannon Zelinski,et al.  Optimizing Integrated Arrival , Departure and Surface Operations Under Uncertainty , 2015 .

[34]  S. Cristobal,et al.  Measuring the cost of resilience , 2016 .

[35]  Lance Sherry,et al.  Analysis of Gate-waiting Delays at Major US Airports , 2009 .

[36]  George L. Nemhauser,et al.  Chapter 1 Air Transportation: Irregular Operations and Control , 2007, Transportation.

[37]  Graham Tanner,et al.  European airline delay cost reference values , 2011 .

[38]  Andrew J. Cook,et al.  Quantifying resilience in ATM - contrasting the impacts of four mechanisms during disturbance , 2016 .