Investigating Prediction Models for Vehicle Demand in a Service Industry

Ahmed Alzaidi, Siddhartha Shakya, Himadri Khargharia

2022

Abstract

Demand prediction is an important part of resource management. Higher forecasting accuracy leads to better decision taking capabilities, especially in a competitive service-based business such as telecommunication services. In this paper, a telecommunication service provider’s data on the use of vehicles by their employees is analyzed and used to forecast the vehicle booking demand for the future at different geographical locations. We implement multiple forecasting models and investigate the effect on forecasting accuracy of two prediction strategies, namely the Direct multi-step forecasting strategy (DMS) and the Rolling mechanism strategy (RMS). Moreover, the effect of different external inputs such as temperatures and holidays were tested. The results show that both DMS and RMS can be used to forecast vehicle demand, with the highest improvement in forecasting achieved through the addition of the holiday input, particularly by using the RMS strategy in the majority of the cases.

Paper Citation

in Harvard Style

Alzaidi A., Shakya S. and Khargharia H. (2022). Investigating Prediction Models for Vehicle Demand in a Service Industry. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA; ISBN 978-989-758-611-8, SciTePress, pages 359-366. DOI: 10.5220/0011527400003332

in Bibtex Style

@conference{ncta22,
author={Ahmed Alzaidi and Siddhartha Shakya and Himadri Khargharia},
title={Investigating Prediction Models for Vehicle Demand in a Service Industry},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA},
year={2022},
pages={359-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011527400003332},
isbn={978-989-758-611-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA
TI - Investigating Prediction Models for Vehicle Demand in a Service Industry
SN - 978-989-758-611-8
AU - Alzaidi A.
AU - Shakya S.
AU - Khargharia H.
PY - 2022
SP - 359
EP - 366
DO - 10.5220/0011527400003332
PB - SciTePress