Free-floating carsharing systems : innovations in membership prediction, mode share, and vehicle allocation optimization methodologies
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Free-floating carsharing systems are among the newest types of carsharing programs. They allow one-way rentals and have no set “homes” or docks for the carsharing vehicles; instead, users are permitted to drive the vehicles anywhere within the operating zone and leave the vehicle in a legal parking space. Compared to traditional carsharing operations, which require the user to bring the vehicle back to its assigned parking space before being able to end the rental, free-floating carsharing allows much greater spontaneity and flexibility for the user. However, it leads to additional operational challenges for the program. This dissertation provides methodologies for some of these challenges facing both free-floating and traditional carsharing programs. First, it analyzes cities with carsharing to determine what characteristics increase the likelihood of the city supporting a successful carsharing program; high overall population, small household sizes, high transit use, and high levels of government employment all make the city a likely carsharing contender. Second, in terms of membership prediction, several modeling alternatives exist. All of the options find that the operating area is of key importance, with other factors (including household size, household densities, and proportion of the population between ages 20 and 39) of varying importance depending on the modeling technique. Third, carsharing trip frequencies and mode share are of value to both carsharing and metropolitan planning organizations, and this dissertation provides innovative techniques to determine the number of trips taken and the share of total travel completed with carsharing (both free-floating and traditional). Fourth and finally, an original methodology for optimizing the vehicle allocation issue for free-floating carsharing organizations is provided. The methodology takes a user input for the total number of vehicles and returns the allocations across multiple demand periods that will maximize revenue, taking into account the cost of reallocating vehicles between demand periods.