Matching vs. Individual Choice: How to Counter Regional Imbalance of Carsharing Demand

Among the most crucial organizational challenges of free-floating carsharing is the question how to cope with regional demand imbalance. Because users are allowed to leave a rented car anywhere in the service district, it regularly occurs that too many cars are left behind in low-demand regions whereas other regions face a demand surplus. In this paper, we consider a countermeasure that has been overlooked by previous research: an optimization-based matching of carsharing supply and demand that not only addresses the profit promised by the current matches but also targets future demand imbalance. To account for such imbalances, we define regional demand levels that specify the projected number of requested cars per region and aim to reduce the deviations of the regions' actual car supply from these target levels. We present exact polynomial-time algorithms for this extended matching task that are suitable for real-time application on large carsharing platforms. In an extensive computational study, we compare optimization-based matching approaches with and without the consideration of demand imbalance and benchmark them with the status quo, the individual choice of carsharing users among available cars. Based on generated data with considerable demand variation among regions, our results indicate a clear advantage of our novel matching approach. In a further study based on a large carsharing data set, however, the proof of concept fails because the real-world regions are cut according to geographical characteristics instead of demand variation. To successfully relieve the strains of demand imbalance, our novel matching task thus requires a properly partitioned service district and reliable forecasts of the carsharing demands.