In the past 10 to 15 years, carsharing has attracted significant attention as a green transportation alternative that can reduce car dependence and promote smarter, healthier mode choice in urban areas. Despite rapid growth and aggressive funding, carsharing remains a niche product with quantifiable but relatively small benefits. A new type of carsharing seeks to expand its reach, and perhaps its benefits, by eliminating some of the inconveniences of traditional services, such as having to book in advance and having to return the car to the same location. Flexible carsharing, pioneered by Daimler's car2go program in late 2008, also introduces a logistical problem not present in traditional carsharing: the spatial distribution of vehicles tends to be irregular because of the randomness of demand. Vehicle redistribution, which can be done periodically (e.g., at the end of the day) or continuously, can be used to ameliorate this problem. By using an agent-based model of a flexible carsharing system, this research explores the trade-offs between fleet size and hired vehicle redistributors, with the objective of maximizing the demand level that can be satisfactorily served. Production functions for representative city sizes and demand densities are presented. Finally, the results of the simulation are compared with actual data from car2go's Austin, Texas, operation. The comparison shows that the simulation results are reasonably close to the measured performance of one-way carsharing in Austin.
