From Booking Data to Demand Knowledge Unconstraining Carsharing Demand

Abstract

Since the introduction of free-floating carsharing (FFCS), system optimization has always been a crucial point in operations. Especially knowledge about the usage of such systems allows for a better understanding, leading to maximized utilization and therefore revenue. In order to understand demand for FFCS services, most often rental data is utilized. However, utilizing such data yields systematic underreporting of demand, since lack of vehicles obstructs counting real demand. In this paper we present an unconstraining algorithm for FFCS system analysis, called Pois_d, that minimizes demand underreporting in rental data due to unavailability. Evaluation of this algorithm shows that it approximates actual demand, reduces underreporting by up to 70% compared to utilizing solely rental data, and reduces error measures by up to 26% as well. Applying Pois_d to real world data, the size of undetected potential in FFCS systems is illustrated. Therefore, the analysis of four areas from the business area of an FFCS provider is presented. Results reveal potential markups on pure rental data of up to 90%. Adjusting demand data for these systems with this algorithm can help to optimize operative measures like vehicle reallocation, adjustment of pricing systems, and planning of business areas.