Day-to-day dynamics of traveler learning behavior and the incentivization scheme of the operator for one-way carsharing services

Abstract

In station-based one-way carsharing system, the asymmetric demand-supply issue represents a toughing challenge. This problem affects the carsharing system’s level of service as well as the financial viability, and requires the engagement of a large amount of resources in redistributing the sharing cars to meet travelers’ need. Firstly, this paper proposes an approach involves the day-to-day dynamics of traveler’s ’learning behavior’ together with an adaptive incentivization scheme of the carsharing operator. Secondly, on each day, carsharing travelers make the route choice decisions according to their perceived travel costs, which can be affected by the past experience and the incentivization scheme of the carsharing operator. More specifically, the adaptive scheme does not require specific information about travelers’ behavior traits, is adopted by the operator so as to motivate travelers to rent their car from an over supplied station and/or return it to an under supplied station, thereby reducing the expected cost of relocating the cars using dedicated staff. What is more, travelers tend to discount the value of the incentive, making it less effective in relocations. Then, the equilibrium state and stability of the evolution model is examined. Finally, numerical experiments are conducted to illustrate the application of the approach.