A Comparative Analysis of Pickup Forecasting Methods for Customer Arrivals in Airport Carparks

Andreas Papayiannis, Paul Johnson, Peter Duck

Abstract

Accurate forecasts of customer demand lie at the core of any successful revenue management system. Most research has focused upon studying such methods for the airline and hotel industry. In this paper, we present a comparative analysis of various forecasting methods which we apply to the rapidly evolving airport carparking (ACP) industry. We use real ACP booking data from four distinct carparks of a major airport in UK to forecast customer arrivals for one to eight weeks out in the future. Conclusions are reached with regards to which forecasting methods perform best in this operating environment, and whether there is any benefit in employing complex methods over simpler ones.

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Paper Citation


in Harvard Style

Papayiannis A., Johnson P. and Duck P. (2016). A Comparative Analysis of Pickup Forecasting Methods for Customer Arrivals in Airport Carparks . In Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-171-7, pages 15-24. DOI: 10.5220/0005631900150024


in Bibtex Style

@conference{icores16,
author={Andreas Papayiannis and Paul Johnson and Peter Duck},
title={A Comparative Analysis of Pickup Forecasting Methods for Customer Arrivals in Airport Carparks},
booktitle={Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2016},
pages={15-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005631900150024},
isbn={978-989-758-171-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - A Comparative Analysis of Pickup Forecasting Methods for Customer Arrivals in Airport Carparks
SN - 978-989-758-171-7
AU - Papayiannis A.
AU - Johnson P.
AU - Duck P.
PY - 2016
SP - 15
EP - 24
DO - 10.5220/0005631900150024