Analyzing the Impacts of Inbound Flight Delay Trends on Departure Delays Due to Connection Passengers Using a Hybrid RNN Model

Tsegai O. Yhdego (), An-Tsun Wei, Gordon Erlebacher, Hui Wang and Miguel G. Tejada
Additional contact information
Tsegai O. Yhdego: Department of Industrial and Manufacturing Engineering, Florida A&M University-Florida State University College of Engineering, 2525 Pottsdamer St., Tallahassee, FL 32310, USA
An-Tsun Wei: Department of Industrial and Manufacturing Engineering, Florida A&M University-Florida State University College of Engineering, 2525 Pottsdamer St., Tallahassee, FL 32310, USA
Gordon Erlebacher: Department of Scientific Computing, Florida State University, 600 W College Ave., Tallahassee, FL 32306, USA
Hui Wang: Department of Industrial and Manufacturing Engineering, Florida A&M University-Florida State University College of Engineering, 2525 Pottsdamer St., Tallahassee, FL 32310, USA
Miguel G. Tejada: Operational Efficiency, Copa Airlines, Panama 0816-06819, Panama

Mathematics, 2023, vol. 11, issue 11, 1-24

Abstract: Some delay patterns are correlated to historical performance and can reflect the trend of delays in future flights. A typical example is the delay from an earlier inbound flight causing delayed departure of a connecting and downstream outbound flight. Specifically, if an arriving aircraft arrives late, the connecting airline may decide to wait for connecting passengers. Due to the consistent flow of passengers to various destinations during a travel season, similar delay patterns could occur in future days/weeks. Airlines may analyze such trends days or weeks before flights to anticipate future delays and redistribute resources with different priorities to serve those outbound flights that are likely to be affected by feeder delays. In this study, we use a hybrid recurrent neural network (RNN) model to estimate delays and project their impacts on downstream flights. The proposed model integrates a gated recurrent unit (GRU) model to capture the historical trend and a dense layer to capture the short-term dependency between arrival and departure delays, and, then, integrates information from both branches using a second GRU model. We trained and tuned the model with data from nine airports in North, Central, and South America. The proposed model outperformed alternate approaches with traditional structures in the testing phase. Most of the predicted delay of the proposed model were within the predefined 95% confidence interval. Finally, to provide operational benefits to airline managers, our analysis measured the future impact of a potentially delayed inbound feeder, (PDIF) in a case study, by means of identifying the outbound flights which might be affected based on their available connection times (ACTs). From an economic perspective, the proposed algorithm offers potential cost savings for airlines to prevent or minimize the impact of delays.

Keywords: airline delay; delay propagation; recurrent neural network; delay trend analysis; hybrid model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/11/2427/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/11/2427/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:11:p:2427-:d:1154618

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().