Pronóstico pospandemia de tráfico aéreo. Caso de Colombia

La planificación aeroportuaria, y por lo tanto el desarrollo de las infraestructuras aéreas, depende en gran medida de los niveles de demanda que se prevén para el futuro. Para planificar las inversiones en infraestructura de un sistema aeroportuario y poder satisfacer las necesidades futuras, es es...

Full description

Autores:
Nagera Acosta, Ana Leonilde
Lemus Franco, Exmelin Hamid
Tipo de recurso:
Masters Thesis
Fecha de publicación:
2022
Institución:
Universidad Santo Tomás
Repositorio:
Universidad Santo Tomás
Idioma:
spa
OAI Identifier:
oai:repository.usta.edu.co:11634/48350
Acceso en línea:
http://hdl.handle.net/11634/48350
Palabra clave:
airport
air transport
forecast
demand
Bayesian Structural Time Series
Ingeniería Civil
Aeroportuaria
Infraestructura-Áereas
Pasajeros
aeropuerto
transporte aéreo
pronóstico
demanda
series de tiempo estructural Bayesiano.
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 2.5 Colombia
id SantoToma2_52bbe818b55184ce4898a029563a6ab0
oai_identifier_str oai:repository.usta.edu.co:11634/48350
network_acronym_str SantoToma2
network_name_str Universidad Santo Tomás
repository_id_str
dc.title.spa.fl_str_mv Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
title Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
spellingShingle Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
airport
air transport
forecast
demand
Bayesian Structural Time Series
Ingeniería Civil
Aeroportuaria
Infraestructura-Áereas
Pasajeros
aeropuerto
transporte aéreo
pronóstico
demanda
series de tiempo estructural Bayesiano.
title_short Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
title_full Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
title_fullStr Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
title_full_unstemmed Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
title_sort Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
dc.creator.fl_str_mv Nagera Acosta, Ana Leonilde
Lemus Franco, Exmelin Hamid
dc.contributor.advisor.none.fl_str_mv Diaz Olariaga, Oscar Eduardo
Rodriguez Pinzon, Heivar Yesid
dc.contributor.author.none.fl_str_mv Nagera Acosta, Ana Leonilde
Lemus Franco, Exmelin Hamid
dc.contributor.orcid.spa.fl_str_mv https://orcid.org/0000-0002-4858-3677
https://orcid.org/0000-0002-9553-0455
dc.contributor.googlescholar.spa.fl_str_mv https://scholar.google.com/citations?hl=es&user=v4XBXJAAAAAJ
https://scholar.google.com/citations?hl=es&user=9gC738EAAAAJ
dc.contributor.cvlac.spa.fl_str_mv https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001561684
https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001256491
dc.contributor.corporatename.spa.fl_str_mv Universidad Santo Tomas
dc.subject.keyword.spa.fl_str_mv airport
air transport
forecast
demand
Bayesian Structural Time Series
topic airport
air transport
forecast
demand
Bayesian Structural Time Series
Ingeniería Civil
Aeroportuaria
Infraestructura-Áereas
Pasajeros
aeropuerto
transporte aéreo
pronóstico
demanda
series de tiempo estructural Bayesiano.
dc.subject.lemb.spa.fl_str_mv Ingeniería Civil
Aeroportuaria
Infraestructura-Áereas
Pasajeros
dc.subject.proposal.spa.fl_str_mv aeropuerto
transporte aéreo
pronóstico
demanda
series de tiempo estructural Bayesiano.
description La planificación aeroportuaria, y por lo tanto el desarrollo de las infraestructuras aéreas, depende en gran medida de los niveles de demanda que se prevén para el futuro. Para planificar las inversiones en infraestructura de un sistema aeroportuario y poder satisfacer las necesidades futuras, es esencial predecir el nivel y la distribución de la demanda, tanto de pasajeros como de carga aérea. En el presente trabajo de tesis se realizó un pronóstico, a medio-largo plazo (10 años), de la demanda de pasajeros y de carga aérea, aplicado a un caso de estudio concreto, Colombia, y en donde se tuvo en cuenta el impacto en el tráfico aéreo del periodo más severo de la pandemia del COVID-19, año 2020, y el periodo de transición a la pospandemia (2021). Para conseguir tal objetivo, y como planteamiento metodológico, se desarrolla un modelo del tipo Bayesian Structural Time Series (BSTS), diseñado para trabajar con datos de series temporales, y muy utilizado para la selección de características, la previsión de series temporales, la predicción inmediata, y la inferencia del impacto causal. De los resultados obtenidos se puede destacar dos aspectos relevantes, en primer lugar, que tanto la demanda como la tendencia de crecimiento de la misma se recuperará muy pronto (en solo un par de años), con respecto al año prepandemia-2019, en el caso de estudio analizado. Y, en segundo lugar, el modelo presenta valores MAPE muy aceptables (de entre 1% y 7%, según la variable a pronosticar) lo que convierte al método BSTS en una metodología alternativa viable para el cálculo de pronóstico de tráfico aéreo.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-12-14T13:38:34Z
dc.date.available.none.fl_str_mv 2022-12-14T13:38:34Z
dc.date.issued.none.fl_str_mv 2022-12-13
dc.type.local.spa.fl_str_mv Tesis de maestría
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_bdcc
dc.type.drive.none.fl_str_mv info:eu-repo/semantics/masterThesis
format http://purl.org/coar/resource_type/c_bdcc
status_str acceptedVersion
dc.identifier.citation.spa.fl_str_mv Nagera Acosta, A. L. y Lemus Franco, E. H. (2022). Pronóstico post pandemia del tráfico aéreo. Caso de Colombia. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11634/48350
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad Santo Tomás
dc.identifier.instname.spa.fl_str_mv instname:Universidad Santo Tomás
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.usta.edu.co
identifier_str_mv Nagera Acosta, A. L. y Lemus Franco, E. H. (2022). Pronóstico post pandemia del tráfico aéreo. Caso de Colombia. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.
reponame:Repositorio Institucional Universidad Santo Tomás
instname:Universidad Santo Tomás
repourl:https://repository.usta.edu.co
url http://hdl.handle.net/11634/48350
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Abed, S. Y.; Ba-Fail, A. O. & Jasimuddin, S. M. (2001). An econometric analysis of international air travel demand in Saudi Arabia. Journal of Air Transport Management, 7, 143–148. https://doi.org/10.1016/S0969-6997(00)00043-0
ACI (2022). The impact of COVID-19 on airports - and the path to recovery. Montreal: ACI World.
ACI (2021). Annual World Airport Traffic Report. Montreal: ACI (Airports Council International).
ACI (2016). Traffic Forecast. Montreal: ACI (Airports Council International).
Adenigbo, A.; Mageto, J.; Luke, R. (2022). Macroeconomic Determinants of Air Cargo Flows in Ghana. Latin American Journal of Trade Policy, 5(12), 7-36. DOI: 10.5354/0719-9368.2022.67061
Aerocivil (2022). Estadísticas de transporte aéreo. https://www.aerocivil.gov.co/atencion/estadisticas-de-las-actividades-aeronauticas/boletines-operacionales (visitada el 14 de mayo 2022)
Alexander, D.; Merkert, R. (2021). Applications of gravity models to evaluate and forecast US international air freight markets post-GFC. Transport Policy, 104, 52–62. DOI: 10.1016/j.tranpol.2020.04.004
Al-Sultan, A.; Al-Rubkhi, A.; Alsaber, A.; Pan, J. (2021). Forecasting air passenger traffic volume: evaluating time series models in long-term forecasting of Kuwait air passenger data. Advances and Applications in Statistics, 70 (1), 69-89. DOI: 10.17654/AS070010069
ANI - Agencia Nacional de Infraestructura (Colombia) (2022). https://www.ani.gov.co/categorias/aeropuertos (visitada el 18 de mayo 2022)
Ashford, N.; Mumayiz, S. & Wright, P. (2011). Airport Engineering. New Jersey: John Wiley & Sons.
Aston, J. A. D. & Koopman, S. J. (2006). A non-Gaussian generalization of the airline model for robust seasonal adjustment. Journal of Forecasting, 25, 325–349. https://doi.org/10.1002/for.991
Bach, S.; Huang, B.; London, B.; Getoor, L. (2013). Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. arXiv:1309.6813 [cs.LG]. https://doi.org/10.48550/arXiv.1309.6813
Baier, F.; Berster, P.; Gelhausen, M. (2021). Global cargo gravitation model: airports matter for forecasts. International Economics and Economic Policy. DOI: 10.1007/s10368-021-00525-2
Bao, Y.; Xiong, T. & Hu, Z. (2012). Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method. Discrete Dynamics in Nature and Society, ID 431512, 1-12. doi:10.1155/2012/431512
Box, G.; Tiao, G. (1975). Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association, 70, 70–79. http://dx.doi.org/10.1080/01621459.1975.10480264
Brodersen, K.; Gallusser, F.; Koehler, J.; Remy, N.; Scott, S. (2015). Inferring causal impact using Bayesian structural time-series models. The Annals of Applied Statistics, 9, 247–274. https://doi.org/10.1214/14-AOAS788
Budd, L. & Ison, S. (2017). Air Transport Management. London: Routledge.
Cerri, J.; Carnevali, L.; Monaco, A.; Genovesi, P.; Bertolino, S. (2022). Blacklists do not necessarily make people curious about invasive alien species. A case study with Bayesian structural time series and Wikipedia searches about invasive mammals in Italy. NeoBiota, 71, 113–128. https://doi.org/10.3897/neobiota.71.69422
Chen, S.; Kuo, S.; Chang, K. & Wang, Y. (2012). Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks. Transportation Planning and Technology, 35(3), 373-392. DOI: 10.1080/03081060.2012.673272
Chin, A. T. H. & Tay, J. H. (2001). Developments in air transport: implications on investment decisions, profitability and survival of Asian airlines. Journal of Air Transport Management, 7, 319–330. https://doi.org/10.1016/S0969-6997(01)00026-6
Chou, T.; Liang, G.; Han, T. (2011). Application of fuzzy regression on air cargo volume forecast. Quality and Quantity, 45, 1539–1550. DOI: 10.1007/s11135-010-9342-8
DANE - Departamento Administrativo Nacional de Estadística (Colombia) (2022). https://www.dane.gov.co/ (visitada el 17 abril 2022)
Dantas, T.; Oliveira, F.; Repolho, H. (2017). Air transportation demand forecast through Bagging Holt Winters methods. Journal of Air Transport Management, 59, 116–123. https://doi.org/10.1016/j.jairtraman.2016.12.006
de Neufville, R. & Odoni, A. (2013). Airport Systems, Planning, Design, and Management. New York: McGraw-Hill.
Díaz Olariaga, O. (2021). Contribución del transporte aéreo a la conectividad territorial. El caso de Colombia. EURE, 47(140), 117-141. doi: https://doi.org/10.7764/EURE.47.140.06
Díaz Olariaga, O.; Alonso, C. (2021). Impact of airport policies on regional development. Evidence from the Colombian case. Regional Science Policy & Practice, 1–26. DOI: 10.1111/rsp3.1248326
Díaz Olariaga, O.; Girón, E. (2020). Análisis de la influencia de la privatización de aeropuertos en el pronóstico de la demanda de pasajeros. El caso de Colombia. Revista Transporte y Territorio, 22, 94-113. doi: 10.34096/rtt.i22.8408
Díaz Olariaga, O.; Pulido, L. (2019). Measurement of airport efficiency. The case of Colombia. Transport and Telecommunication, 20(1), 40-51
Díaz Olariaga, O.; Zea, J.F. (2018). Influence of the liberalization of the air transport industry on configuration of the traffic in the airport network. Transportation Research Procedia, 33, 43-50.
Dingari, M.; Reddy, M. & Sumalatha, V. (2019). Air Traffic Forecasting Using Artificial Neural Networks. International Journal of Scientific & Technology Research, 8(10), 556-559.
Durbin, J.; Koopman, S. (2002). A simple and efficient simulation smoother for state space time series analysis. Biometrika, 89, 603–615. https://doi.org/10.1093/biomet/89.3.603
Fernandes, E. & Pacheco, R. R. (2010). The causal relationship between GDP and domestic air passenger traffic in Brazil. Transportation Planning and Technology, 33, 569–581. https://doi.org/10.1080/03081060.2010.512217
García Cruzado, M. (2013). Aeropuertos. Planificación, Diseño y Medio Ambiente. Madrid: Ibergarceta Publicaciones.
Garrow, L. A. & Koppelman, F. S. (2004). Predicting air travelers’ no-show and standby behavior using passenger and directional itinerary information. Journal of Air Transport Management, 10, 401–411. https://doi.org/10.1016/j.jairtraman.2004.06.007
George, E.; McCulloch, R. (1997). Approaches for Bayesian Variable Selection. Statistica Sinica. 7. 339-373. http://www.jstor.org/stable/24306083
Giri, S.; Purkayastha, S.; Hazra, S.; Chanda, A.; Das, I.; Das, S. (2020). Prediction of Monthly Hilsa (Tenualosa ilisha) Catch in the Northern Bay of Bengal using Bayesian Structural Time Series Model. Regional Studies in Marine Science. 39, 101456. DOI: 10.1016/j.rsma.2020.101456
Griffiths, W. (2001). Bayesian Inference in the Seemingly Unrelated Regressions Model. Computer-aided econometrics. Boca Ratón: CRC Press.
Grosche, T.; Rothlauf, F. & Heinzl, A. (2007). Gravity models for airline passenger volume estimation. Journal of Air Transport Management, 13, 175–183. DOI: 10.1016/j.jairtraman.2007.02.001
Gudmundsson, S.V.; Cattaneo, M. & Redondi, R. (2021). Forecasting temporal world recovery in air transport markets in the presence of large economic shocks: The case of COVID-19. Journal of Air Transport Management, 91, 102007.
Gupta, V.; Sharma, K.; Sangwan, M. (2019). Airlines passenger forecasting using LSTM based recurrent neural networks. International Journal Information Theories and Applications, 26(2), 178-187.
Hamilton, D. (1994). Time Series Analysis. Princeton: Princeton University Press.
Harvey, A.; Trimbur, T.; Van Dijk, H. (2007). Trends and Cycles in Economic Time Series: A Bayesian Approach. Journal of Econometrics, 140, 618-649. DOI: 10.1016/j.jeconom.2006.07.006
Hoeting, J.; Madigan, D.; Raftery, A.; Volinsky, C. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382-417. https://www.jstor.org/stable/2676803
Honjo, K.; Shiraki, H. & Ashina, S. (2018). Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect. PloS ONE, 13(4), e0196331. https://doi.org/10.1371/journal.pone.0196331
Horonjeff, R.; McKelvey, F.; Sproule, W. & Young, S. (2010). Planning and Design of Airports. New York: McGraw-Hill.
IATA (2022). Global Outlook for Air Transport. Geneva: IATA.
IATA (2021). World Air Transport Statistics 2021. Geneva: IATA.
ICAO (2022). Effects of Novel Coronavirus (COVID 19) on Civil Aviation: Economic Impact Analysis. Montreal: ICAO.
ICAO (2006). Manual on Air Traffic Forecasting. Doc 8991. Montreal: ICAO.
ICAO (1987). Master Planning. Part 1. Doc 9184. Montreal: ICAO.
Jalali, P.; Rabotyagov, S. (2020). Quantifying cumulative effectiveness of green stormwater infrastructure in improving water quality. Science of the Total Environment, 731, 138953. DOI: 10.1016/j.scitotenv.2020.138953
Janic (2021). System Analysis and Modelling in Air Transport. Boca Raton: CRC Press.
Janic, M. (2009). Airport analysis, planning and design: demand, capacity and congestion. New York: Nova Science Publishers.
Jones, R.H. (2019). Longitudinal Data with Serial Correlation: A state-space approach. Boca Raton: Chapman & Hall / CRC Press
Kalman, R.E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering, 82, 35-45.
Kazda, A. & Caves, R. (2015). Airport design and operations. Bingley: Emerald.
Kiracı, K.; Battal, Ü. (2018). Macroeconomic Determinants of Air Transportation: A VAR Analysis on Turkey. Gaziantep University Journal of Social Sciences, 17(4), 1536-1557.
Koller, D.; Friedman, N. (2009). Probabilistic Graphical Models. Cambridge, MA: MIT Press.
Li, H.; Bai, J.; Cui, X.; Li, Y.; Sun, S. (2020). A new secondary decomposition-ensemble approach with cuckoo search optimization for air cargo forecasting. Applied Soft Computing Journal, 90, 106161. DOI: 10.1016/j.asoc.2020.106161
Liu, J.; Ding, L.; Guan, X.; Gui, J., Xu, J. (2020). Comparative analysis of forecasting for air cargo volume: Statistical techniques vs. machine learning. Journal of Data, Information and Management, 2, 243–255. DOI: 10.1007/s42488-020-00031-1
Madhavan, M.; Sharafuddin, M.; Piboonrungroj, P., Yang, C. (2020). Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo. Global Business Review. DOI: 10.1177/0972150920923316
Madigan, D.; Raftery, A. E. (1994). Model selection and accounting for model uncertainty in graphical models using Occam’s window. Journal of the American Statistical Association, 89(428), 1535–1546. https://doi.org/10.2307/2291017
Meijer, G. (2020). Fundamentals of Aviation Operations. London: Routledge.
Mostafaeipour, A.; Goli, A. & Qolipour, M. (2018). Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study. The Journal of Supercomputing, 74(10), 5461-5484. https://doi.org/10.1007/s11227-018-2452-0
Muros, J.G.; Díaz Olariaga, O. (2021). Utilización de algoritmos de redes neuronales artificiales en el pronóstico de la demanda de pasajeros aéreos. In Serna, E. (Ed.), Desarrollo e Innovación en Ingeniería Vol. I (pp. 277-294). Medellín: Editorial Instituto Antioqueño de Investigación. DOI: 10.5281/5513899
Navarro, S.; Acuña, J. (2021). Determination of the average daily annual traffic growth rates in Nicaragua based on macroeconomic data. Revista Ciencia y Tecnología El Higo, 11(2), 70–83. https://doi.org/10.5377/elhigo.v11i2.13033
Onder, E.; Kunzu, S. (2014). Forecasting air traffic volumes using smoothing techniques. Journal of Aeronautics and Space Technologies, 7(1), 65-85. DOI: 10.7603/s40690-014-0006-0
Peters, J.; Janzing, D.; Schölkopf, B. (2017). Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA: MIT Press.
Petris, G.; Petrone, S.; Campagnoli, P. (2009). Dynamic linear models with R. Springer. https://doi.org/10.1007/b135794_2
Ren, L.; Glasure, Y. (2009), Applicability of the revised mean absolute percentage errors (mape) approach to some popular normal and non-normal independent time series. International Advances in Economic Research, 15, 409–420. DOI: 10.1080/01621459.1975.10480264
Rodríguez, M.; Mejía, M.; Zapata, S. (2015). La causalidad entre el crecimiento económico y la expansión del transporte aéreo: un análisis empírico para Chile. Revista de Economía Del Rosario, 18(1), 127–144. https://doi.org/10.12804/rev.econ.rosario.18.01.2015.04
Rodríguez, Y.; Pineda, W. & Díaz Olariaga, O. (2020). Air traffic forecast in post-liberalization context: a Dynamic Linear Models approach. Aviation, 24(1), 10-19. DOI: 10.3846/aviation.2020.12273
Samagaio, A. & Wolters, M. (2010). Comparative analysis of government forecasts for the Lisbon Airport. Journal of Air Transport Management, 16, 213–217. DOI: 10.1016/j.jairtraman.2009.09.002
Santana, L.J. (2019). Nowcasting with Google Trends: Dynamics of the Monthly Economic Activity, Private Consumption and Investment based on Google Trends Data and a Bayesian
Structural Time Series Model. XIII Foro de Investigadores de Bancos Centrales del Consejo Monetario Centroamericano. Ciudad de Guatemala, Guatemala, 5-6 Sept. 2019. https://www.secmca.org/recard/index.php/foro/article/view/154
Schmitt, D; Gollnick, V. (2016). Air Transport System. Heidelberg: Springer.
Scott, S.; Varian, H. (2014). Predicting the present with Bayesian structural time series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1-2), 4-23.
Scott, S.; Varian, H. (2015). Bayesian Variable Selection for Nowcasting Economic Time Series. NBER Chapters, in: Economic Analysis of the Digital Economy, 119-135. National Bureau of Economic Research, Inc
Srisaeng, P.; Baxter, G. & Wild, G. (2015). Using an artificial neural network approach to forecast Australia’s domestic passenger air travel demand. World Review of Intermodal Transportation Research, 5(3), 281-313.
Stock, J. & Watson, M. (2012). Introducción a la Econometría. Madrid: Pearson.
Suryani, E.; Chou, S.; Chen, C. (2012). Dynamic simulation model of air cargo demand forecast and terminal capacity planning. Simulation Modelling Practice and Theory, 28, 27–41. DOI: 10.1016/j.simpat.2012.05.012
Suryani, E.; Chou, S.; Chen, C. (2010). Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework. Expert Systems with Applications, 37, 2324–2339.
Tascón, D.; Díaz Olariaga, O. (2021). Air traffic forecast and its impact on runway capacity. A System Dynamics approach. Journal of Air Transport Management, vol. 90, 1-10. DOI: 10.1016/j.jairtraman.2020.101946
Tsui, W. H. K.; Balli, H.; Gilbey, A.; Gow, H. (2014). Forecasting of Hong Kong airport’s passenger throughput. Tourism Management, 42, 62–76. https://doi.org/10.1016/j.tourman.2013.10.008
Vieira, J.; Braga, C., Pereira, R. (2022). The impact of COVID-19 on air passenger demand and CO2 emissions in Brazil. Energy Policy, 164, 112906. DOI: 10.1016/j.enpol.2022.112906
Walpole, R.; Myers, R.; Myers, S; Ye, K. (2012). Probabilidad y estadística para ingenierías y ciencias. México: Pearson.
Wells, A.; Young, S. (2004). Airport Planning & Management. New York: McGraw-Hill.
West, M. & Harrison, J. (2006). Bayesian forecasting and dynamic models. Springer Science & Business Media.
Wooldridge, J. (2013). Introductory Econometrics. Mason (OH): South-Western.
Zhang, Y.; Fricker, J. (2021). Quantifying the Impact of COVID-19 on Non-Motorized Transportation: A Bayesian Structural Time Series Model. Transport Policy, 103, 11-20. DOI: 10.1016/j.tranpol.2021.01.013
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spelling Diaz Olariaga, Oscar EduardoRodriguez Pinzon, Heivar YesidNagera Acosta, Ana LeonildeLemus Franco, Exmelin Hamidhttps://orcid.org/0000-0002-4858-3677https://orcid.org/0000-0002-9553-0455https://scholar.google.com/citations?hl=es&user=v4XBXJAAAAAJhttps://scholar.google.com/citations?hl=es&user=9gC738EAAAAJhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001561684https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001256491Universidad Santo Tomas2022-12-14T13:38:34Z2022-12-14T13:38:34Z2022-12-13Nagera Acosta, A. L. y Lemus Franco, E. H. (2022). Pronóstico post pandemia del tráfico aéreo. Caso de Colombia. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.http://hdl.handle.net/11634/48350reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coLa planificación aeroportuaria, y por lo tanto el desarrollo de las infraestructuras aéreas, depende en gran medida de los niveles de demanda que se prevén para el futuro. Para planificar las inversiones en infraestructura de un sistema aeroportuario y poder satisfacer las necesidades futuras, es esencial predecir el nivel y la distribución de la demanda, tanto de pasajeros como de carga aérea. En el presente trabajo de tesis se realizó un pronóstico, a medio-largo plazo (10 años), de la demanda de pasajeros y de carga aérea, aplicado a un caso de estudio concreto, Colombia, y en donde se tuvo en cuenta el impacto en el tráfico aéreo del periodo más severo de la pandemia del COVID-19, año 2020, y el periodo de transición a la pospandemia (2021). Para conseguir tal objetivo, y como planteamiento metodológico, se desarrolla un modelo del tipo Bayesian Structural Time Series (BSTS), diseñado para trabajar con datos de series temporales, y muy utilizado para la selección de características, la previsión de series temporales, la predicción inmediata, y la inferencia del impacto causal. De los resultados obtenidos se puede destacar dos aspectos relevantes, en primer lugar, que tanto la demanda como la tendencia de crecimiento de la misma se recuperará muy pronto (en solo un par de años), con respecto al año prepandemia-2019, en el caso de estudio analizado. Y, en segundo lugar, el modelo presenta valores MAPE muy aceptables (de entre 1% y 7%, según la variable a pronosticar) lo que convierte al método BSTS en una metodología alternativa viable para el cálculo de pronóstico de tráfico aéreo.Airport planning, and therefore the development of air infrastructure, depends to a large extent on the levels of demand that are forecast for the future. To plan investments in infrastructure of an airport system and to be able to meet future needs, it is essential to predict the level and distribution of demand, both for passengers and air cargo. In this thesis work, a medium-long term forecast (10 years) of the demand for passengers and air cargo was made, applied to a specific case study, Colombia, and where the impact on the air traffic during the most severe period of the COVID-19 pandemic, 2020, and the post-pandemic transition period (2021). To achieve this objective, and as a methodological approach, a model of the Bayesian Structural Time Series (BSTS) type is developed, designed to work with time series data, and widely used for feature selection, time series forecasting, immediate, and the inference of the causal impact. From the results obtained, two relevant aspects can be highlighted, firstly, that both demand and its growth trend will recover very soon (in just a couple of years), compared to the pre-pandemic year-2019, in which analyzed case study. And, secondly, the model presents very acceptable MAPE values (between 1% and 7%, depending on the variable to be forecast), which makes the BSTS method a viable alternative methodology for calculating air traffic forecasts.Magíster en Infraestructura VialMaestríaapplication/pdfspaUniversidad Santo TomásMaestría Infraestructura VialFacultad de Ingeniería CivilAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Pronóstico pospandemia de tráfico aéreo. Caso de Colombiaairportair transportforecastdemandBayesian Structural Time SeriesIngeniería CivilAeroportuariaInfraestructura-ÁereasPasajerosaeropuertotransporte aéreopronósticodemandaseries de tiempo estructural Bayesiano.Tesis de maestríainfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_bdccinfo:eu-repo/semantics/masterThesisCRAI-USTA BogotáAbed, S. Y.; Ba-Fail, A. O. & Jasimuddin, S. M. (2001). An econometric analysis of international air travel demand in Saudi Arabia. Journal of Air Transport Management, 7, 143–148. https://doi.org/10.1016/S0969-6997(00)00043-0ACI (2022). The impact of COVID-19 on airports - and the path to recovery. Montreal: ACI World.ACI (2021). Annual World Airport Traffic Report. Montreal: ACI (Airports Council International).ACI (2016). Traffic Forecast. Montreal: ACI (Airports Council International).Adenigbo, A.; Mageto, J.; Luke, R. (2022). Macroeconomic Determinants of Air Cargo Flows in Ghana. Latin American Journal of Trade Policy, 5(12), 7-36. DOI: 10.5354/0719-9368.2022.67061Aerocivil (2022). Estadísticas de transporte aéreo. https://www.aerocivil.gov.co/atencion/estadisticas-de-las-actividades-aeronauticas/boletines-operacionales (visitada el 14 de mayo 2022)Alexander, D.; Merkert, R. (2021). Applications of gravity models to evaluate and forecast US international air freight markets post-GFC. Transport Policy, 104, 52–62. DOI: 10.1016/j.tranpol.2020.04.004Al-Sultan, A.; Al-Rubkhi, A.; Alsaber, A.; Pan, J. (2021). Forecasting air passenger traffic volume: evaluating time series models in long-term forecasting of Kuwait air passenger data. Advances and Applications in Statistics, 70 (1), 69-89. DOI: 10.17654/AS070010069ANI - Agencia Nacional de Infraestructura (Colombia) (2022). https://www.ani.gov.co/categorias/aeropuertos (visitada el 18 de mayo 2022)Ashford, N.; Mumayiz, S. & Wright, P. (2011). Airport Engineering. New Jersey: John Wiley & Sons.Aston, J. A. D. & Koopman, S. J. (2006). A non-Gaussian generalization of the airline model for robust seasonal adjustment. Journal of Forecasting, 25, 325–349. https://doi.org/10.1002/for.991Bach, S.; Huang, B.; London, B.; Getoor, L. (2013). Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. arXiv:1309.6813 [cs.LG]. https://doi.org/10.48550/arXiv.1309.6813Baier, F.; Berster, P.; Gelhausen, M. (2021). Global cargo gravitation model: airports matter for forecasts. International Economics and Economic Policy. DOI: 10.1007/s10368-021-00525-2Bao, Y.; Xiong, T. & Hu, Z. (2012). Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method. Discrete Dynamics in Nature and Society, ID 431512, 1-12. doi:10.1155/2012/431512Box, G.; Tiao, G. (1975). Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association, 70, 70–79. http://dx.doi.org/10.1080/01621459.1975.10480264Brodersen, K.; Gallusser, F.; Koehler, J.; Remy, N.; Scott, S. (2015). Inferring causal impact using Bayesian structural time-series models. The Annals of Applied Statistics, 9, 247–274. https://doi.org/10.1214/14-AOAS788Budd, L. & Ison, S. (2017). Air Transport Management. London: Routledge.Cerri, J.; Carnevali, L.; Monaco, A.; Genovesi, P.; Bertolino, S. (2022). Blacklists do not necessarily make people curious about invasive alien species. A case study with Bayesian structural time series and Wikipedia searches about invasive mammals in Italy. NeoBiota, 71, 113–128. https://doi.org/10.3897/neobiota.71.69422Chen, S.; Kuo, S.; Chang, K. & Wang, Y. (2012). Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks. Transportation Planning and Technology, 35(3), 373-392. DOI: 10.1080/03081060.2012.673272Chin, A. T. H. & Tay, J. H. (2001). Developments in air transport: implications on investment decisions, profitability and survival of Asian airlines. Journal of Air Transport Management, 7, 319–330. https://doi.org/10.1016/S0969-6997(01)00026-6Chou, T.; Liang, G.; Han, T. (2011). Application of fuzzy regression on air cargo volume forecast. Quality and Quantity, 45, 1539–1550. DOI: 10.1007/s11135-010-9342-8DANE - Departamento Administrativo Nacional de Estadística (Colombia) (2022). https://www.dane.gov.co/ (visitada el 17 abril 2022)Dantas, T.; Oliveira, F.; Repolho, H. (2017). Air transportation demand forecast through Bagging Holt Winters methods. Journal of Air Transport Management, 59, 116–123. https://doi.org/10.1016/j.jairtraman.2016.12.006de Neufville, R. & Odoni, A. (2013). Airport Systems, Planning, Design, and Management. New York: McGraw-Hill.Díaz Olariaga, O. (2021). Contribución del transporte aéreo a la conectividad territorial. El caso de Colombia. EURE, 47(140), 117-141. doi: https://doi.org/10.7764/EURE.47.140.06Díaz Olariaga, O.; Alonso, C. (2021). Impact of airport policies on regional development. Evidence from the Colombian case. Regional Science Policy & Practice, 1–26. DOI: 10.1111/rsp3.1248326Díaz Olariaga, O.; Girón, E. (2020). Análisis de la influencia de la privatización de aeropuertos en el pronóstico de la demanda de pasajeros. El caso de Colombia. Revista Transporte y Territorio, 22, 94-113. doi: 10.34096/rtt.i22.8408Díaz Olariaga, O.; Pulido, L. (2019). Measurement of airport efficiency. The case of Colombia. Transport and Telecommunication, 20(1), 40-51Díaz Olariaga, O.; Zea, J.F. (2018). Influence of the liberalization of the air transport industry on configuration of the traffic in the airport network. Transportation Research Procedia, 33, 43-50.Dingari, M.; Reddy, M. & Sumalatha, V. (2019). Air Traffic Forecasting Using Artificial Neural Networks. International Journal of Scientific & Technology Research, 8(10), 556-559.Durbin, J.; Koopman, S. (2002). A simple and efficient simulation smoother for state space time series analysis. Biometrika, 89, 603–615. https://doi.org/10.1093/biomet/89.3.603Fernandes, E. & Pacheco, R. R. (2010). The causal relationship between GDP and domestic air passenger traffic in Brazil. Transportation Planning and Technology, 33, 569–581. https://doi.org/10.1080/03081060.2010.512217García Cruzado, M. (2013). Aeropuertos. Planificación, Diseño y Medio Ambiente. Madrid: Ibergarceta Publicaciones.Garrow, L. A. & Koppelman, F. S. (2004). Predicting air travelers’ no-show and standby behavior using passenger and directional itinerary information. Journal of Air Transport Management, 10, 401–411. https://doi.org/10.1016/j.jairtraman.2004.06.007George, E.; McCulloch, R. (1997). Approaches for Bayesian Variable Selection. Statistica Sinica. 7. 339-373. http://www.jstor.org/stable/24306083Giri, S.; Purkayastha, S.; Hazra, S.; Chanda, A.; Das, I.; Das, S. (2020). Prediction of Monthly Hilsa (Tenualosa ilisha) Catch in the Northern Bay of Bengal using Bayesian Structural Time Series Model. Regional Studies in Marine Science. 39, 101456. DOI: 10.1016/j.rsma.2020.101456Griffiths, W. (2001). Bayesian Inference in the Seemingly Unrelated Regressions Model. Computer-aided econometrics. Boca Ratón: CRC Press.Grosche, T.; Rothlauf, F. & Heinzl, A. (2007). Gravity models for airline passenger volume estimation. Journal of Air Transport Management, 13, 175–183. DOI: 10.1016/j.jairtraman.2007.02.001Gudmundsson, S.V.; Cattaneo, M. & Redondi, R. (2021). Forecasting temporal world recovery in air transport markets in the presence of large economic shocks: The case of COVID-19. Journal of Air Transport Management, 91, 102007.Gupta, V.; Sharma, K.; Sangwan, M. (2019). Airlines passenger forecasting using LSTM based recurrent neural networks. International Journal Information Theories and Applications, 26(2), 178-187.Hamilton, D. (1994). Time Series Analysis. Princeton: Princeton University Press.Harvey, A.; Trimbur, T.; Van Dijk, H. (2007). Trends and Cycles in Economic Time Series: A Bayesian Approach. Journal of Econometrics, 140, 618-649. DOI: 10.1016/j.jeconom.2006.07.006Hoeting, J.; Madigan, D.; Raftery, A.; Volinsky, C. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382-417. https://www.jstor.org/stable/2676803Honjo, K.; Shiraki, H. & Ashina, S. (2018). Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect. PloS ONE, 13(4), e0196331. https://doi.org/10.1371/journal.pone.0196331Horonjeff, R.; McKelvey, F.; Sproule, W. & Young, S. (2010). Planning and Design of Airports. New York: McGraw-Hill.IATA (2022). Global Outlook for Air Transport. Geneva: IATA.IATA (2021). World Air Transport Statistics 2021. Geneva: IATA.ICAO (2022). Effects of Novel Coronavirus (COVID 19) on Civil Aviation: Economic Impact Analysis. Montreal: ICAO.ICAO (2006). Manual on Air Traffic Forecasting. Doc 8991. Montreal: ICAO.ICAO (1987). Master Planning. Part 1. Doc 9184. Montreal: ICAO.Jalali, P.; Rabotyagov, S. (2020). Quantifying cumulative effectiveness of green stormwater infrastructure in improving water quality. Science of the Total Environment, 731, 138953. DOI: 10.1016/j.scitotenv.2020.138953Janic (2021). System Analysis and Modelling in Air Transport. Boca Raton: CRC Press.Janic, M. (2009). Airport analysis, planning and design: demand, capacity and congestion. New York: Nova Science Publishers.Jones, R.H. (2019). Longitudinal Data with Serial Correlation: A state-space approach. Boca Raton: Chapman & Hall / CRC PressKalman, R.E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering, 82, 35-45.Kazda, A. & Caves, R. (2015). Airport design and operations. Bingley: Emerald.Kiracı, K.; Battal, Ü. (2018). Macroeconomic Determinants of Air Transportation: A VAR Analysis on Turkey. Gaziantep University Journal of Social Sciences, 17(4), 1536-1557.Koller, D.; Friedman, N. (2009). Probabilistic Graphical Models. Cambridge, MA: MIT Press.Li, H.; Bai, J.; Cui, X.; Li, Y.; Sun, S. (2020). A new secondary decomposition-ensemble approach with cuckoo search optimization for air cargo forecasting. Applied Soft Computing Journal, 90, 106161. DOI: 10.1016/j.asoc.2020.106161Liu, J.; Ding, L.; Guan, X.; Gui, J., Xu, J. (2020). Comparative analysis of forecasting for air cargo volume: Statistical techniques vs. machine learning. Journal of Data, Information and Management, 2, 243–255. DOI: 10.1007/s42488-020-00031-1Madhavan, M.; Sharafuddin, M.; Piboonrungroj, P., Yang, C. (2020). Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo. Global Business Review. DOI: 10.1177/0972150920923316Madigan, D.; Raftery, A. E. (1994). Model selection and accounting for model uncertainty in graphical models using Occam’s window. Journal of the American Statistical Association, 89(428), 1535–1546. https://doi.org/10.2307/2291017Meijer, G. (2020). Fundamentals of Aviation Operations. London: Routledge.Mostafaeipour, A.; Goli, A. & Qolipour, M. (2018). Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study. The Journal of Supercomputing, 74(10), 5461-5484. https://doi.org/10.1007/s11227-018-2452-0Muros, J.G.; Díaz Olariaga, O. (2021). Utilización de algoritmos de redes neuronales artificiales en el pronóstico de la demanda de pasajeros aéreos. In Serna, E. (Ed.), Desarrollo e Innovación en Ingeniería Vol. I (pp. 277-294). Medellín: Editorial Instituto Antioqueño de Investigación. DOI: 10.5281/5513899Navarro, S.; Acuña, J. (2021). Determination of the average daily annual traffic growth rates in Nicaragua based on macroeconomic data. Revista Ciencia y Tecnología El Higo, 11(2), 70–83. https://doi.org/10.5377/elhigo.v11i2.13033Onder, E.; Kunzu, S. (2014). Forecasting air traffic volumes using smoothing techniques. Journal of Aeronautics and Space Technologies, 7(1), 65-85. DOI: 10.7603/s40690-014-0006-0Peters, J.; Janzing, D.; Schölkopf, B. (2017). Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA: MIT Press.Petris, G.; Petrone, S.; Campagnoli, P. (2009). Dynamic linear models with R. Springer. https://doi.org/10.1007/b135794_2Ren, L.; Glasure, Y. (2009), Applicability of the revised mean absolute percentage errors (mape) approach to some popular normal and non-normal independent time series. International Advances in Economic Research, 15, 409–420. DOI: 10.1080/01621459.1975.10480264Rodríguez, M.; Mejía, M.; Zapata, S. (2015). La causalidad entre el crecimiento económico y la expansión del transporte aéreo: un análisis empírico para Chile. Revista de Economía Del Rosario, 18(1), 127–144. https://doi.org/10.12804/rev.econ.rosario.18.01.2015.04Rodríguez, Y.; Pineda, W. & Díaz Olariaga, O. (2020). Air traffic forecast in post-liberalization context: a Dynamic Linear Models approach. Aviation, 24(1), 10-19. DOI: 10.3846/aviation.2020.12273Samagaio, A. & Wolters, M. (2010). Comparative analysis of government forecasts for the Lisbon Airport. Journal of Air Transport Management, 16, 213–217. DOI: 10.1016/j.jairtraman.2009.09.002Santana, L.J. (2019). Nowcasting with Google Trends: Dynamics of the Monthly Economic Activity, Private Consumption and Investment based on Google Trends Data and a BayesianStructural Time Series Model. XIII Foro de Investigadores de Bancos Centrales del Consejo Monetario Centroamericano. Ciudad de Guatemala, Guatemala, 5-6 Sept. 2019. https://www.secmca.org/recard/index.php/foro/article/view/154Schmitt, D; Gollnick, V. (2016). Air Transport System. Heidelberg: Springer.Scott, S.; Varian, H. (2014). Predicting the present with Bayesian structural time series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1-2), 4-23.Scott, S.; Varian, H. (2015). Bayesian Variable Selection for Nowcasting Economic Time Series. NBER Chapters, in: Economic Analysis of the Digital Economy, 119-135. National Bureau of Economic Research, IncSrisaeng, P.; Baxter, G. & Wild, G. (2015). Using an artificial neural network approach to forecast Australia’s domestic passenger air travel demand. World Review of Intermodal Transportation Research, 5(3), 281-313.Stock, J. & Watson, M. (2012). Introducción a la Econometría. Madrid: Pearson.Suryani, E.; Chou, S.; Chen, C. (2012). Dynamic simulation model of air cargo demand forecast and terminal capacity planning. Simulation Modelling Practice and Theory, 28, 27–41. DOI: 10.1016/j.simpat.2012.05.012Suryani, E.; Chou, S.; Chen, C. (2010). Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework. Expert Systems with Applications, 37, 2324–2339.Tascón, D.; Díaz Olariaga, O. (2021). Air traffic forecast and its impact on runway capacity. A System Dynamics approach. Journal of Air Transport Management, vol. 90, 1-10. DOI: 10.1016/j.jairtraman.2020.101946Tsui, W. H. K.; Balli, H.; Gilbey, A.; Gow, H. (2014). Forecasting of Hong Kong airport’s passenger throughput. Tourism Management, 42, 62–76. https://doi.org/10.1016/j.tourman.2013.10.008Vieira, J.; Braga, C., Pereira, R. (2022). The impact of COVID-19 on air passenger demand and CO2 emissions in Brazil. Energy Policy, 164, 112906. DOI: 10.1016/j.enpol.2022.112906Walpole, R.; Myers, R.; Myers, S; Ye, K. (2012). Probabilidad y estadística para ingenierías y ciencias. México: Pearson.Wells, A.; Young, S. (2004). Airport Planning & Management. New York: McGraw-Hill.West, M. & Harrison, J. (2006). Bayesian forecasting and dynamic models. Springer Science & Business Media.Wooldridge, J. (2013). Introductory Econometrics. Mason (OH): South-Western.Zhang, Y.; Fricker, J. (2021). Quantifying the Impact of COVID-19 on Non-Motorized Transportation: A Bayesian Structural Time Series Model. Transport Policy, 103, 11-20. DOI: 10.1016/j.tranpol.2021.01.013ORIGINAL2022ananageraexmelinlemus.pdf2022ananageraexmelinlemus.pdfTrabajo de gradoapplication/pdf677805https://repository.usta.edu.co/bitstream/11634/48350/1/2022ananageraexmelinlemus.pdfe892caca4cab64023181ce76022ad404MD51open accessCarta de aprobación Negera-Lemus (1).pdfCarta de aprobación Negera-Lemus (1).pdfCarta de aprobación facultadapplication/pdf586108https://repository.usta.edu.co/bitstream/11634/48350/2/Carta%20de%20aprobaci%c3%b3n%20Negera-Lemus%20%281%29.pdfe90b63cb3b8aca8a20786af4c0833a8cMD52metadata only accessCarta_autorizacion_archivo_autores_2022 (1).pdfCarta_autorizacion_archivo_autores_2022 (1).pdfCarta derechos de autores.application/pdf422685https://repository.usta.edu.co/bitstream/11634/48350/3/Carta_autorizacion_archivo_autores_2022%20%281%29.pdfe72b0debf1566347712b63efe89591d7MD53metadata only accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repository.usta.edu.co/bitstream/11634/48350/4/license_rdf217700a34da79ed616c2feb68d4c5e06MD54open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8807https://repository.usta.edu.co/bitstream/11634/48350/5/license.txtaedeaf396fcd827b537c73d23464fc27MD55open accessTHUMBNAIL2022ananageraexmelinlemus.pdf.jpg2022ananageraexmelinlemus.pdf.jpgIM Thumbnailimage/jpeg5168https://repository.usta.edu.co/bitstream/11634/48350/6/2022ananageraexmelinlemus.pdf.jpge96ce5fbca79a9af11a0b423e5382510MD56open accessCarta de aprobación Negera-Lemus (1).pdf.jpgCarta de aprobación Negera-Lemus (1).pdf.jpgIM Thumbnailimage/jpeg7564https://repository.usta.edu.co/bitstream/11634/48350/7/Carta%20de%20aprobaci%c3%b3n%20Negera-Lemus%20%281%29.pdf.jpge7894e24b1e22f791b27de168aa41341MD57open accessCarta_autorizacion_archivo_autores_2022 (1).pdf.jpgCarta_autorizacion_archivo_autores_2022 (1).pdf.jpgIM Thumbnailimage/jpeg8171https://repository.usta.edu.co/bitstream/11634/48350/8/Carta_autorizacion_archivo_autores_2022%20%281%29.pdf.jpg01f36e8a58a5b34db2dc78f476ac8824MD58open access11634/48350oai:repository.usta.edu.co:11634/483502023-05-09 08:57:59.235open accessRepositorio Universidad Santo Tomásrepositorio@usantotomas.edu.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