Prediction of air traffic delays: An agent-based model introducing refined parameter estimation methods.
PLoS One
; 16(4): e0249754, 2021.
Article
em En
| MEDLINE
| ID: mdl-33826641
ABSTRACT
We propose an agent-based model for predicting individual flight delays in an entire air traffic network. In contrast to previous work, more detailed parameter estimation methods were incorporated into the agent-based model, acting on the state transitions of agents. Specifically, a conditional probability model was proposed for modifying the expected departure time, which was used to indicate whether a flight had experienced the necessary waiting due to Ground Delay Programs (GDPs) or carrier-related reasons. Additionally, two random forest regression models were presented for estimating the turnaround time and the elapsed time of flight agents in the agent-based delay prediction model. The parameter models were trained and fitted using the flight data for 2017 in the United States. The performance of the delay prediction model was tested for thirty days with three types of delay levels (low, medium, and high), which were randomly selected from 2018. The experimental results showed that the average absolute error in the test days was 6.8 min, and the classification accuracy with a 15 min threshold for a two-hour forecast horizon was 89.5%. The performance of our model outperformed that of existing research. Additionally, the positive effect of introducing parameter models and the negative impact of increasing the prediction horizon on the prediction performance were further studied.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Agendamento de Consultas
/
Viagem
/
Aeronaves
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
PLoS One
Assunto da revista:
CIENCIA
/
MEDICINA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
China