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Predicting Scheduled Block Time (SBT) of Airlines: A Case Study

Pramod K Mishra*, Amit Bardhan** and Amit Das***

DOI: https://doi.org/10.62206/sajm.30.5.2024.231-259

PUBLISHED : 11 JUNE 2024

Abstract

The Scheduled Block Time (SBT) is one of the critical parameters of airline operations. The higher block time may consider higher taxi times ultimately increasing the operational costs of the airlines. The time series data between 2013 and 2022 have been collected and analyzed using various Machine Learning (ML) based techniques to predict the SBT of a reputed airline operating between Dubai and New Delhi. In the process of analysis, both stationarity and the non-stationarity checks were performed using various techniques, respectively, under seasonal auto regressive moving average and neural network-based regression methods to reveal some interesting insights about the SBT of Dubai-New Delhi bound airline. In the estimation, we predicted the respective block times as 189.95 and 227.58 min, respectively, for Dubai to New Delhi and New Delhi to Dubai flights with accuracy levels of more than 98%. It is presumed that the results will help the respective airline operator and other competing operators to optimize their SBT by bringing down the travel time of the customers while minimizing the block time padding

Key Words

Airline operations, SBT, Machine Learning, Prediction, Model accuracy

Author Biography

Pramod K Mishra
Assistant Professor, School of Management Studies, University of Hyderabad, Hyderabad-500 046, Telangana, India. E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Amit Bardhan
Professor, Faculty of Management Studies, University of Delhi, Delhi-110 007, NCT, India. E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Amit Das
Doctoral Research Scholar, Faculty of Management Studies, University of Delhi, Delhi-110 007, NCT, India. E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

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