A model for estimating airline passenger trip reliability metrics from system-wide flight simulations

Um modelo para estimar métricas de confiabilidade de viagens de passageiros das companhias aéreas a partir de simulações de voo sistêmicas

Abstracts

Analysis of the benefits of government modernization initiatives for airports or air traffic control are conducted using complex software models that simulate up to 60,000 flights per day. These flight-centric simulations do not model passenger flows and therefore do not account for passenger trip delays due to cancelled flights and missed connections, which account for up to 60% of the total passenger trip delays. This paper describes a closed-form model for estimating passenger trip reliability metrics from flight delay data from system-wide simulations. The outputs of the model, (i) percent passengers disrupted, (ii) average passenger trip delay, and (iii) total passenger trip delays, are derived from the probability of delayed flights and network structure parameters. The model highlights the role of network structure, in addition to flight on-time performance, on passenger trip reliability. These results have implications for government and industry initiatives to improve flight on-time performance through modernization, consumer protection, and the conduct of benefits analysis.

passenger trip delays; airline operations; nas-wide simulations; reliability metrics

Neste trabalho, foram conduzidas análises dos benefícios de iniciativas governamentais de modernização de aeroportos ou controle de tráfego aéreo utilizando programas complexos de modelos que simulam até 60.000 voos por dia. Estas simulações de vôo não modelam fluxos de passageiros e, portanto, não levam em consideração os atrasos de viagens devido a cancelamento de voos e conexões perdidas, que respondem por até 60% do total dos atrasos de viagem de passageiros. Este trabalho descreve um modelo de forma fechada para estimar métricas confiabilidade de viagem de passageiros a partir de dados de atraso de vôo obtidas de simulações do sistema aéreo. As variáveis de resultado do modelo, (i) percentual de passageiros afetados, (ii) o atraso médio de viagem do passageiro e (iii) o total de atrasos computados, são derivados a partir da probabilidade de voos atrasados e de parâmetros de estrutura de rede. O modelo enfatiza o papel da estrutura de rede, além do desempenho dos tempos, na confiabilidade da viagem do passageiro. Estes resultados têm implicações para as iniciativas do governo e da indústria em melhorar o desempenho de tempos de vôo por meio da modernização, da defesa do consumidor, e da realização de análise de benefícios.

atrasos de viagem; operações de companhias aéreas; simulação do sistema aéreo; métricas de confiabilidade

• Baik, H., Li, T., Chintapudi, N. (2010) Estimation of Flight Delay Costs for U.S. Domestic Air Passengers. Transportation Research Record. Journal of the Transportation Research Board, n. 2177, p. 49.
• Ball, M., Lovell, D., Mukherjee, A. and Subramanian, A. (2006) Analisys of Passenger Delays: Developing a Passenger Delay Metric. NEXTOR NAS Performance Metrics Conference, ASilomar, CA. March
• Barnhart, C., Fearing, D. and Vaze, V. (2010) Modeling Passenger Travel and Delays in the National Air Transportation System. Working paper
• Bilimoria, K. D., Sridhar, B., Chatterji, G. B., Sheth, K. S. and Grabbe, S. R. (2001) FACET: Future ATM Concepts Evalutaion Tool. Air Traffic Control Quarterly, vol. 9, n. 1, pp. 1-20.
• Bratu, S. and Barnhart, C. (2005) An Analysis of Passenger Delays Using Flight Operations and Passenger Booking Data. Journal of Transportation and Statistics, n. 1, vol. 13, pp. 1-27.
• Bureau of Transportation and Statistics (2011) Airline On-Time Performance Data, Form 41 Traffic T100 Domestic Segment Data, DB1_B Coupon Data Available at www.transtats.bts.gov 2010
• Department of Transportation (2003) Revised Departmental Guidance: Valuation of Travel Time in Economic Analysis. Office of the Assistant Secretary for Transportation Policy. Memorandum Available at/ostpxweb.dot.gov/policy
• Federal Aviation Administration (2010) Airport Improvement Program Website for FAA Airports business unit. www.faa.gov/airports/aip/
• JPDO (2010) FAA's NextGen 2010 Implementation Plan Available at www.faa.gov/nextgen
• Long, D., Wingrove, E., Lee, D., Gribko, J., Hemm, R., and Kostiuk, P. (1999) A method for evaluating air carrier operational strategies and forecasting air traffic with flight delay. National Aeronautics and Space Administration (NASA) NASA Contract No. NAS2-14361. NS902S1. Logistics Management Institute (LMI). McLean, Virginia.
• Millner, D. C. (1993) Design of the NASPAC simulation modeling system. Technical Report MTR 92W0000135, The MITRE Corporation, McLean, VA. FAA. Contract No. DTFEA01-93-C00001.
• NEXTOR (2010) Total Delay Impact Study: A Comprehensive Assessment of the Costs and Impacts of Flight Delay in the United States. Report prepared for FAA Air Traffic organization -Strategy and Performance Business Unit Washington D.C Available at www.isr.umd.edu
  » link
• Post, J. (2011) NextGen Modeling Requirements: The AXTO Perspective. 5th Integrated Communications Navigation and Surveillance Conference, Dulles, Virginia. May 10-12, 2011
• Ramamoorthy, K., Boisvert, B., and Hunter, G. (2006) A Real-Time Probabilistic Traffic Flow Management Evaluation Tool. 25th Digital Avionics Systems. IEEE/AIAA Conference, pp.1-13.
• Sherry, L. and Wang, D. (2007) Air Travel Consumer Protection: Metric for Passenger On-Time Performance. Transportation Research Record, pages 22-27.
• Sherry, L., Samant, A., Calderon-Meza, G. (2010) Trends in Airline Passenger Trip Delays (2007): A Multi-Segment Itinerary Analysis. American Institute of Aeronautics and Astronautics: 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Sept.
• Sherry, L. (2011) Modeling Passenger Trip Reliability: Why NextGen May Not Improve Passenger Delays. The Journal of Air Traffic Control, vol. 53, n. 3.
• Subramanian, B. (2007) Aggregate Statistical Models for National Airspace System Performance. MS thesis, University of Maryland, College Park
• Sweet, D. N., Manikonda, V., Aronson, J. S., Roth, K., and Blake, M. (2002) Fast-time simulation system for analysis of advanced air transportation concept. AIAA Modeling and Simulation Conference
• Tien, S. A., Subramanian, B. and Ball, M. (2008) Constructing a Passenger Trip Delay Metric: An Aggregate-level Approach. Proceedings International Conference on Research in Air Transportation (ICRAT-2008), Fairfax, VA, 2008.
• Wang, D. (2007) Methods For Analysis Of Passenger Trip Performance In A Complex Networked Transportation System. Dissertation, Systems Engineering and Operations Research Department, George Mason University
• Zhu, Y. (2007) Evaluating Airline Delays: The Role of Airline Networks, Schedules and Passenger Demands. Master's Thesis, MIT, Cambridge, Massachusetts

Publication Dates

• Publication in this collection
  11 Oct 2013
• Date of issue
  Apr 2013

History

• Received
  19 July 2012
• Accepted
  09 Aug 2012
• Reviewed
  06 Aug 2012