The uneven geography of US air traffic delays: quantifying the impact of connecting passengers on delay propagation
| dc.contributor.author | Sismanidou, Athina | |
| dc.contributor.author | Tarradellas, Joan | |
| dc.contributor.author | Suau-Sanchez, Pere | |
| dc.date.accessioned | 2022-01-18T12:32:10Z | |
| dc.date.available | 2022-01-18T12:32:10Z | |
| dc.date.issued | 2021-12-20 | |
| dc.description.abstract | 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. | en_UK |
| dc.identifier.citation | Sismanidou A, Tarradellas J, Suau-Sanchez P. (2022) The uneven geography of US air traffic delays: quantifying the impact of connecting passengers on delay propagation, Journal of Transport Geography, Volume 98, January 2022, Article number 103260 | en_UK |
| dc.identifier.eissn | 1873-1236 | |
| dc.identifier.issn | 0966-6923 | |
| dc.identifier.uri | https://doi.org/10.1016/j.jtrangeo.2021.103260 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/17423 | |
| dc.language.iso | en | en_UK |
| dc.publisher | Elsevier | en_UK |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | Airport congestion | en_UK |
| dc.subject | Network congestion | en_UK |
| dc.subject | Flight delay propagation | en_UK |
| dc.subject | Carrier delay | en_UK |
| dc.subject | Delay prediction | en_UK |
| dc.subject | Intra-airport delay | en_UK |
| dc.subject | Machine learning algorithms | en_UK |
| dc.title | The uneven geography of US air traffic delays: quantifying the impact of connecting passengers on delay propagation | en_UK |
| dc.type | Article | en_UK |