Browsing by Subject "Carsharing"
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Item Car sharing as an alternative to car ownership: opportunities for carsharing organizations and low-income communities(2016-08) Sanchez, Alvan-Bidal Timothy; Mueller, Elizabeth J.; Greenberg, SherriCar sharing organizations (CSOs) have established themselves as a formal mode of transportation across the United States. These systems purport to offer their members the benefits of a private vehicle, without any of the accompanying pitfalls. Despite these benefits, low-income individuals are less likely to be a member of a CSO than higher-income individuals. This paper synthesizes the major transportation issues facing low-income individuals, explores possible opportunities between CSOs and low-income communities, and examines 7 CSOs for best practice in encouraging participation by low-income individuals. The findings show that when viewed as one piece of the transportation puzzle, CSOs can fill gaps in the transportation system and provide numerous benefits. With community partnerships, innovative solutions, and active outreach, CSOs can broaden awareness of carshare systems and facilitate increased usage among low-income individuals.Item Demographic and demand characteristics of carsharing : a case study of Austin, Texas(2008-05) Thomen, Martin K.; Zhang, Ming, 1963 April 22-Demographic and Demand Characteristics of Carsharing: A Case Study of Austin, Texas explores the use of geospatial analysis in order to understand the demand characteristics and market for carsharing services. A literature review was performed and the demographic characteristics of typical users of carsharing were established. A series of maps was created to geospatially identify concentrations of typical users and their location and access in reference to carsharing vehicle locations. The greater urbanized area of Austin, Texas located within Travis County was used as a case study for this analysis. The report demonstrates that geospatial analysis is a valuable tool to understand the spatial relationship between typical carshare users, nontypical carshare users and the placement of carshare vehicles.Item Free-floating carsharing systems : innovations in membership prediction, mode share, and vehicle allocation optimization methodologies(2012-05) Kortum, Katherine, 1983-; Machemehl, Randy B.; Bhat, Chandra; Gilbert, Robert; Kockelman, Kara; McCray, TaliaFree-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.Item Management of a shared, autonomous, electric vehicle fleet : vehicle choice, charging infrastructure & pricing strategies(2015-08) Chen, Tong Donna; Kockelman, Kara; Machemehl, Randy; Boyles, Stephen; Stone, Peter; Baldick, RossThere are natural synergies between shared autonomous vehicle (AV) fleets and electric vehicle (EV) technology, since fleets of AVs resolve the practical limitations of today's non-autonomous EVs, including traveler range anxiety, access to charging infrastructure, and charging time management. Fleet-managed AVs relieve such concerns, managing range and charging activities based on real-time trip demand and established charging-station locations, as demonstrated in this paper. This work explores the management of a fleet of shared autonomous (battery-only) electric vehicles (SAEVs) in a regional (100-mile by 100-mile) discrete-time, agent-based model. The dissertation examines the operation of SAEVs under various vehicle range and charging infrastructure scenarios in a gridded city modeled roughly after the densities of Austin, Texas. Results indicate that fleet size is sensitive to battery recharge time and vehicle range, with each 80-mile range SAEV replacing 3.7 privately owned vehicles and each 200-mile range SAEV replacing 5.5 privately owned vehicles, under Level II (240-volt AC) charging. With Level III 480-volt DC fast-charging infrastructure in place, these ratios rise to 5.4 vehicles for the 80-mile range SAEV and 6.8 vehicles for the 200-mile range SAEV. However, due to the need to travel while "empty" for charging and passenger pickup, SAEV fleets are predicted to generate an additional 7.1 to 14.0% of travel miles. Financial analysis suggests that the combined cost of charging infrastructure, vehicle capital and maintenance, electricity, insurance, and registration for a fleet of SAEVs ranges from $0.42 to $0.49 per occupied mile traveled, which implies SAEV service can be offered at the equivalent per-mile cost of private vehicle ownership for low-mileage households, and thus be competitive with current manually-driven carsharing services and significantly less expensive than on-demand driver-operated transportation services. The mode share of SAEVs in the simulated mid-sized city is predicted to be between 14 and 39%, when competing against privately-owned, manually-driven vehicles and city bus service. This assumes SAEVs are priced between $0.75 and $1.00 per mile, which delivers significant net revenues to the fleet owner-operator, under all modeled scenarios, assuming 80-mile-range EVs and remote/cordless Level II charging infrastructure and $10,000-per-vehicle automation costs.