Analysis of the value of demand forecasting within vehicle sharing systems

Abstract

Vehicle sharing systems (VSSs) are becoming increasingly popular, primarily due to their financial and environmental advantages. However, VSSs face many operational challenges, including inventory management of vehicles and parking spots, vehicle load balancing and redistribution, pricing strategies, and demand forecasting. If these challenges are not addressed properly, the VSS risks experiencing a significant loss of customers and revenue. Recently, new VSSs have been introduced which use light electric vehicles (LEVs). These systems face a number of unique challenges. For instance, demand forecasting for LEV sharing systems is more complex, as locations are not fixed and journeys can start and end at any allowable location. LEV sharing systems also serve a higher portion of the population since these type of vehicles do not require a driving license. As such, the existing techniques for analysing VSSs are not sufficient for these new systems. To address this need, one needs to forecast the future demand of this novel transportation mode. Since it is exhausting to collect the data to develop a demand model, this project will aim to identify the value of a demand model by using mathematical models tailored for rebalancing operations from the literature and simulations. As LEV systems are still in their infancy, data describing them is not yet available. Therefore, the student will use alternative data, including PubliBike bike-sharing system data, accounting for any potential differences in the analysis. Based on the findings from the literature, the student will analyse different mathematical models.