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Abstract:
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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 . |