Abstract
In this paper, we focus on the forecasting of monthly departure passenger movements for one of the busiest airport in Asia. Firstly, we forecast the monthly airport departure passenger flows for the next 12 months for macro level planning. Next, we used SAS Forecast Studio for detailed-level planning based on airline and per airline-city combinations using hierarchical forecasting. We have also used the actual data to validate the accuracy of the forecast error. We have shown that in most cases, the mean absolute percentage error is less than 3%, which indicates the usefulness of our model for better decision making.
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Acknowledgement
This paper is a combination of work done by the faculty members and one of the analytics projects done the student in SIM University.
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Ma, N.L. (2017). Forecasting Passenger Flows Using Data Analytics. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_24
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DOI: https://doi.org/10.1007/978-3-319-60042-0_24
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