Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10609/144208
Título : The uneven geography of US air traffic delays: Quantifying the impact of connecting passengers on delay propagation
Autoría: Sismanidou, Athina
Tarradellas Espuny, Joan
Suau Sánchez, Pere
Otros: Universitat Oberta de Catalunya. Estudis d'Economia i Empresa
Cranfield University
EADA Business School
Citación : Sismanidou, A., Tarradellas, J. & Suau-Sánchez, P. (2022). The uneven geography of US air traffic delays: Quantifying the impact of connecting passengers on delay propagation. Journal of Transport Geography, 98, 1-12. doi: 10.1016/j.jtrangeo.2021.103260
Resumen : Sustained airport congestion periods translate into delays, especially in hub-and-spoke networks in which delay propagation is more evident. We examine the impact of connecting passenger arrival delays on network delay propagation by using passenger level data combined with flight delay data that allow us to analyse the correlation between delayed incoming flights and departure delays at the 21 U.S. airports with most delays, in July 2018. Results show that correlation between daily arrival delays and daily carrier induced departure delays are statistically significant only for flights carrying high proportions of connecting passengers. Correlation values are also higher for short-to-moderate arrival delays. In addition, a Neural Network model was trained for six major airports to build a delay prediction model and map the potential delay propagation. The results of the propagation scenarios suggest that the presence of a unique dominant carrier at an airport translates into a stronger correlation between arrival and carrier delays than that at airports where different carriers compete for connecting passengers. Furthermore, airline hubs located near the areas of the network with more traffic density, independently of the hub's volume of traffic, are more likely to propagate the delay than hubs located in the periphery. The results of this study can be relevant for airline, airport, and traffic control policies aimed at mitigating airport and network congestion.
Palabras clave : congestión del aeropuerto
congestión en la red
propagación de retraso de vuelo
retraso del transportista
predicción de retraso
retraso dentro del aeropuerto
algoritmos de aprendizaje automático
DOI: http://doi.org/10.1016/j.jtrangeo.2021.103260
Tipo de documento: info:eu-repo/semantics/article
Versión del documento: info:eu-repo/semantics/publishedVersion
Fecha de publicación : 20-dic-2021
Licencia de publicación: https://creativecommons.org/licenses/by-nc-nd/4.0/  
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