Investigating Prediction Models for Vehicle Demand in a Service Industry

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.