Carsharing Programs Research Articles

Fake it till you make it: Synthetic data for emerging carsharing programs

Carsharing is an integral part of the transformation toward flexible and sustainable mobility. New carsharing programs are entering the market to challenge large operators by offering innovative services. This study investigates the use of generative machine learning models for creating synthetic data to support carsharing decision–making when data access is limited. To this end, it explores the evaluation, selection, and implementation of leading-edge methods, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), to generate synthetic tabular transaction data of carsharing trips. The study analyzes usage data of an emerging carsharing program that is expanding its services to include free-floating electric vehicles (EVs). The results show that augmenting real training data with synthetic samples improves predictive modeling of upcoming trips by up to 4.63%. These results support carsharing researchers and practitioners in generating and leveraging synthetic mobility data to develop solutions to real-world decision support problems in carsharing.

What drives the joint demand for ride-hailing and carsharing services? Understanding consumers’ behaviors, attitudes, & concerns

Driven by the emerging collaborative consumption trends, new shared ownership-based business models provide more flexible and accessible on-demand mobility options. This study simultaneously analyzes factors correlated with the consumers’ use of two interrelated disruptive on-demand mobility services, including ride-hailing (RH) and carsharing (CS). A comprehensive behavioral framework is presented to explicitly address important methodological concerns regarding the complex stochastic dependence between the use of CS and RH programs and the underlying behavioral heterogeneity in latent factors influencing the use of the two shared mobility options. Using unique data from the California Vehicle Survey, a rigorous elliptical and Archimedean copula-based finite mixture bivariate ordered probit (BOP) modeling methodology is used to understand behavioral, attitudes, and concern-related correlates of households’ participation levels in CS and RH programs. Characterized by a clayton-copula based finite mixture BOP model, participation levels in CS and RH programs exhibited complex (both synergistic and competing) non-linear stochastic dependence patterns. With a stronger left tail dependence, majority of the households having lower levels of participation in RH also had lower participation levels in CS programs. Contrarily, of the households with higher levels of participation in RH programs, the majority had lower levels of participation in CS programs (revealing weaker right tail dependence). Taste heterogeneity was observed in the unobserved determinants of CS use. Results show that current users of CS and/or RH programs tend to be those with greater awareness, owners of plug-in electric vehicles, those making greater transit trips, workers with greater commute distances, and those lacking free parking at their residences. Compared to ride-hailing, the negative effect of more frequent drivers in a household on CS use was less pronounced. Analysis of marginal and joint marginal effects provided deeper insights into the (interactive) effects of other behavioral and socio-demographic correlates. Treatment effects were simulated and discussed to further demonstrate the policy implications of our results. The new empirical insights provide a more granular understanding of the use patterns of on-demand shared mobility services and can inform more context-sensitive travel forecasts for planning and programming purposes.

Can Subsidized Carshare Programs Enhance Access for Low-Income Travelers?

Problem, research strategy, and findings Carsharing programs—subscription-based car rentals—allow users to purchase only the automobility that they need. These programs may benefit low-income travelers by increasing access at lower prices than private auto ownership. Most carshare programs, however, disproportionately serve higher-income drivers. To assess carsharing’s potential to address the accessibility needs of disadvantaged households, we interviewed members of BlueLA, an electric carsharing program in central Los Angeles (CA) that offers both subsidized and regular memberships. We found few differences in how travelers with different membership types used BlueLA. They both used the service to complement travel by other modes like public transit and ridehail. In addition, members cited the benefits of gaining car access without the financial burden of car ownership or the unpredictability of ridehail fares. Neighborhood context, including residential density and the availability of non-automobile transportation options, also increased BlueLA’s appeal. However, due to limited and unreliable vehicle availability, most users did not rely on BlueLA for time-sensitive trips. BlueLA both eased and increased access to destinations outside of the commute and complemented public transit for subsidized and regular members. Takeaway for practice Services like BlueLA cannot meet all transportation needs. However, subsidized electric carsharing—particularly targeted to central-city neighborhoods—may address some accessibility needs of low-income households without imposing the burdens of automobile ownership.

Understanding the factors associated with one-way and round-trip carsharing usage based on a hybrid operation carsharing system: A case study in Beijing

As carsharing is promoted in many cities, studying the factors related to station-level demand under multi-mode operating carsharing systems will help better develop carsharing programs. In this paper, the number of transactions is calculated to represent the carsharing demand based on the data from a one-way and round-trip hybrid operation carsharing system in Beijing. The generalized additive model is then conducted with carsharing demand as the dependent variable and five groups of independent variables: station attributes, public transit, built environment, weather, and consumption features. The main results are: (1) the one-way station has a substitution effect on the round-trip station; (2) the one-way and round-trip stations not only have considerable demand in places well served by transit but also can play different and complementary roles in areas underserved by transit; (3) the percentage of residential POIs, the POI-mixed entropy, and the number of cars per household are the most statistically significant factors; (4) weather conditions such as precipitation, high temperatures, and poor air quality play a positive role in carsharing usage; (5) personalized incentives help attract more usage, while the positive effect is minor; (6) both population density and average consumption are highly nonlinear to carsharing usage. Population densities of around 10,000 people/km2 have the largest positive effect on one-way station usage and around 6,500 people/km2 have the largest negative effect on round-trip station usage. When the average consumption exceeds 100 RMB, the usage of one-way stations decreases more rapidly compared to round-trip stations. This study can help the operators understand and refine their business strategies, and can provide insights into determining the location and mode of carsharing stations.

A joint behavioral choice model for adoption of automated vehicle ride sourcing and carsharing technologies: Role of built environment & sustainable travel behaviors

Rapid advancements in vehicle automation and a shift towards collaborative consumption trends will disrupt future urban mobility systems. We present a joint model of consumers’ affinity towards shared automated vehicles (SAVs) with two distinct yet related configurations: automated vehicle (AV) carsharing and AV ride sourcing. Compact and walkable built environment is known to support active travel and shared mobility but the potential impacts of built environment on adoption of automated shared mobility are largely unknown. Besides sociodemographic, sustainable travel, and technology-related factors, we examine the role of neighborhood-level walkability (composite measure of land-use, pedestrian-oriented urban design, and transit accessibility) in shaping public’s opinion about AV carsharing and AV ride sourcing technologies. Detailed travel behavior and stated preference/taste data for over 2700 individuals from the most recent 2019 Puget Sound Travel Survey are complemented with neighborhood-level high-resolution data on built environment fabric. Two dependent variables of interest include individual’s level of interest in AV carsharing and AV ride sourcing programs. A novel and comprehensive geo-additive heterogeneous copula-based joint behavioral model is developed - revealing complex stochastic dependence patterns between users’ interest in AV ride sourcing and carsharing programs and different layers of systematic and random taste heterogeneity. Results show that improving neighborhood walkability can be an effective way to increase public’s interest in using automated ride sourcing and carsharing services. Findings also highlight the opportunity to enable SAV adoption by harnessing the synergies between green (transit) travel, technology use for work/non-work purposes, use of existing mobility-on-demand (MOD) services, and affinity towards automated MOD services. Results are translated to potential policies for designing proactive behavioral and built environment interventions for accelerating SAV adoption with an enhanced sensitivity towards the needs of distinct and more vulnerable population groups (e.g., older, unemployed, and/or less educated individuals). The study methodologically contributes by presenting a comprehensive behavioral model for SAV adoption by differentiating ride sourcing and carsharing.

A novel neutrosophic set based hierarchical challenge analysis approach for servicizing business models: A case study of car share service network

Servicization as a part of circular economy has a prominent role in operations management research by emphasizing the functionality of the products. Servicizing business model is a phenomenon that the products are converted into services for sale, or process of transforming the consumers into users. This newly introduced way of business is defined as a new opportunity for operations management by focusing on; the reliability and durability of the products against the extreme repeatedly use, coordination problems in conducting the business, risk of encountering unknown negative customers, and moral hazard in product usage, operational difficulties, and the degradation of the products. All of the aforementioned issues in implementing the servicizing business models constitute a research question with the high impact of COVID-19 pandemics. Since people avoid using public transportation, car share programs are highly in demand. In addition, there is an obvious research gap in challenge analysis of the car-sharing programs’ literature, an overall service network challenge analysis is missing in the field of study. Hence, the purpose of this study is proposing a novel neutrosophic set based hierarchical challenge analysis approach for the servicizing business models with a step by step guidance, and presenting a real world case study addressing a car sharing company’s entire service network. A practitioner can understand which challenge should be dealt with first, what is the inter-relationship structure of these challenges in a hierarchy, and what is the expected final result of these interactions. The study also contributes to the theoretical background by using neutrosophic sets to propose a novel interpretive hierarchical challenge analysis. In this research, the evaluation of truth and indeterminacy membership degrees of a neutrosophic set are gathered separately instead of applying the defined linguistic variable schemes. The case study is a contemporary field of study attracting attention of innovative entrepreneurs, since there is an increased demand of individual car driving due to the isolation requirement arisen from COVID-19 pandemics.

Build it and they will cycle: Causal evidence from the downtown Vancouver Comox Greenway

This study evaluated changes in cycling trips before and after the construction of an urban greenway in Vancouver, Canada. Urban cycling has gained in popularity as a healthy and environmentally friendly mode of transport. Carsharing is also on the rise globally, and it has the potential to disrupt urban mobility, including cycling and public transit. In this study, we hypothesized that cycling trips will increase after the opening of the greenway for residents living within 300 meters of the greenway compared to those living further away. We also posited that carsharing membership would have a positive impact on cycling uptake. Data were drawn from a three-year natural experiment study (2012–2015) of the Comox-Helmcken Greenway (“Comox Greenway”) in Vancouver. An experimental-and-control design was employed to compare pre-post differences in cycling trips for residents within 300 m of the greenway (experimental group, n = 239) and those further away (control group, n = 285). The opening of the greenway resulted in a 251% increase in cycling trips for the experimental group compared to the control group. Ethnically white subjects reported 130% more cycling trips than non-white subjects, and those aged 65 and older reported 79% fewer cycling trips than younger age cohorts. Carshare membership reduced the number of cycling trips after the greenway opening. Results suggest that greenways could be an effective strategy to boost urban cycling, and that carsharing programs may have a countervailing effect on cycling. The findings support recent initiatives to reappropriate road space in urban areas for cycling and walking due to the COVID-19 pandemic.

Predicting shared-car use and examining nonlinear effects using gradient boosting regression trees

Flexible drop-off and pick-up (one-way) carsharing programs provide users with high levels of convenience but meanwhile incurs spatiotemporal imbalances in shared-cars distribution. Predicting shared-car use helps recognize system imbalances beforehand while identifying determinants related to shared-car use helps operators efficiently implement relocation strategies. In this study, a gradient boosting regression model (GBRT) is employed to predict shared-car use at a station level, and partial dependence plots (PDPs) are employed to examine nonlinear relationships between shared-car use and various predictors. Results show: (1) GBRTs predict shared-car use with a high level of accuracy (MSE: 1.1069–1.1648). (2) PDPs present highly consistent results with relationships derived from the traditional statistical model; (3) Time-varying variables account for 89.30%–86.84% importance in shared-cars use prediction, suggesting these variables can greatly enhance prediction accuracy; (4) Other variables like built environment, station attributes, and socioeconomic features, also account for some importance and can enhance prediction accuracy. Findings help carsharing operators accurately predict the station-level shared-car use and optimally identify the best locations for stations, and thus maintain the operational efficiency of carsharing programs.

Examining the impact of car-sharing on private vehicle ownership

Abstract Car-sharing has experienced a significant boom in recent years, with estimates suggesting that car-sharing programs are now operating in over 30 countries worldwide, serving around five million users. The potential to reduce individual vehicle ownership rates is frequently cited as a motive for promoting car-sharing. While some previous studies have argued that customers are indeed willing to reduce the total number of vehicles owned after becoming car-sharing members, the reliability of these findings is tenuous given that many are based on self-selected samples of car-sharing users, resulting in biased estimates. In theory, the availability of car-sharing programs could have limited effect on the general public’s car ownership decisions–or at least have no effect on a large portion of travelers. Whether or not traveler decision processes are significantly influenced by specific attributes of different car-sharing options (e.g., access time, vehicle size, fuel type, etc.) remains an unanswered question, as there are limited quantitative studies on this issue. To contribute to filling this research gap, this paper discusses the findings of a study of 1,500 private households across major Australian cities. A nested logit model is used to investigate the impacts of car-sharing on respondents’ household vehicle ownership decisions. In contrast to the results of some previous studies, we find that the stated availability of car-sharing appears to have minimal impact on respondents’ decision to own a vehicle or not, leading to important policy implications. We agree with prior investigations that car-sharing could potentially reduce private car ownership. However, because this study finds limited impact of the availability of car-sharing on vehicle ownership, and because the majority of respondents did not self-identify as car-sharing users, education and awareness campaigns could be important factors in improving the general public’s preferences towards car-sharing and fully realizing car-sharing’s benefits to society.

“I like it, but I don't use it”: Impact of carsharing business models on usage intentions in the sharing economy

AbstractCarsharing is often promoted as a potentially environmental‐friendly alternative to individual car ownership. However, various carsharing programs have displayed limited success in the past. An initial field study of a new carsharing service is such a story of failure: The introduction of this new service at a medium‐sized German university generated unexpectedly low adoption rates so that the service was eventually scaled down and then suspended. Quantitative field study results as well as additional qualitative focus groups reveal that missing compatibility is a key barrier to adoption. Drawing on extant conceptual frameworks of user participation in sharing business models, a factorial survey identifies the importance of different dimensions of carsharing business models for their acceptance. The results reveal that a set of convenience and lifestyle dimensions influences usage intentions, including mode of drive, pickup and drop‐off mode, service level, price model, availability, and type of market mediation. In contrast, vehicle fleet does not appear to influence carsharing models' acceptance. These findings contribute to research on business model configuration as well as the attitude–behavior gap in the sharing economy by determining relevant dimensions of a carsharing business model that can bridge the gap between basically positive attitudes and usage resistance. Thereby, they also serve for concrete managerial recommendations.

Preference heterogeneity in mode choice for car-sharing and shared automated vehicles

Membership-based short-term car rental–more popularly referred to in the marketplace and popular media as “car-sharing”–has been expanding rapidly worldwide. The advent of self-driving vehicles is likely to facilitate the growth of car-sharing by addressing a couple of the current barriers, including limited dedicated parking and non-competitive access times. Despite the growing importance of this topic, however, the patterns emerging in the market penetration of automated vehicles in car-sharing programs have not received much attention in the literature because of the relative newness of the automated vehicle technologies enabling them. Towards addressing this research gap in this increasingly important area, this paper presents the results of an Australian survey with a focus on consumer preferences towards car-sharing. A stated preference (SP) methodology was adopted to elicit consumers’ valuation of specified mode-choice related factors. In particular, vehicle self-driving capability, a factor rarely examined in the literature, was provided as an option to participants in the SP survey. To increase the realness of the experiment, SP tasks were pivoted from respondents’ most recent trips. The travel preferences data were analyzed using a random parameter (mixed) logit model. To explore preference heterogeneity, socio-demographic and other factors were interacted with alternative-specific attributes, and their influences on marginal utilities were extracted using simulation analysis. Preference heterogeneity across individuals towards shared automated vehicles (SAVs) was identified. Consistent with the literature, consumers’ experience of using car-sharing appears to have significant influence on household mode choices, increasing the probability of using a diversified mode tools (e.g., TWS and Taxi) and decreasing the likelihood of choosing to use privately owned travel tools, such as private car. Although it has been argued in the literature that females, non-drivers, and the elderly are the most likely to benefit from SAVs, the results of this study reveal that these user groups in fact hold negative opinions about SAVs. The findings highlight the challenges policymakers may encounter in maximizing the social and individual benefits of SAVs.

Location Suitable for the Implementation of Carsharing in the City of São Paulo

This study aimed to analyze the relationship between carsharing and urban mobility in the context of smart cities. These themes converge with the current trend of the city and the introduction of new technologies to make life easier for citizens. The smart cities offer strategic principles aligning to the three main dimensions integration of infrastructures and technology-mediated services, social learning for strengthening human infrastructure, and governance for institutional improvement and citizen engagement. A growing number of cities are implementing carsharing programs to increase this transportation model, and São Paulo (Brazil) is one of these cities around de world. That model provides the general public with affordable access to public car, and can promote the integration of public transport systems and carsharing programs to improve mobility, traffic and environment related problems. One of the key factors for the success of such programs is the location of car stations in relation to potential transport demand. This study proposes a GIS-based method to calculate the spatial distribution of the potential demand for trips, locate stations using location-allocation models, and determine the best location to settle car-sharing stations. The results obtained shows which are the best places for the implementation of carsharing and its possible partnerships with several types of commercial establishments in the city of São Paulo.

Promoting carsharing attractiveness and efficiency: An exploratory analysis

Carsharing has grown significantly over recent years. Understanding factors related to the usage and turnover rate of shared cars will help promote the growth of carsharing programs. This study sets station-based shared car booking requests and turnover rates as learning objectives, by which generalized additive mixed models are employed to examine various effects. The results are: (1) stations with more parking spaces, longer business hours and fewer nearby stations are likely to receive more booking requests and have a higher turnover rate; (2) an area with a higher population density, a higher percentage of adults, a higher percentage of males, a greater road density, or more mixed land use is associated with more car usage and a higher turnover rate; (3) stations nearby transit hubs, colleges, and shopping centers attract more shared car users; (4) shared cars are often oversupplied at transit hubs; (5) both transit proximity and housing price present high degrees of nonlinearity in relation to shared car usage and turnover rates. Findings provide evidence for optimizing the usage and efficiency of carsharing programs: carsharing companies should identify underserved areas to initiate new businesses; carsharing seems more competitive in a distance to a bus stop between 1.2 km and 2.4 km, and carsharing is more effectively served in areas with constraints in accessing metro services; carsharing should be optimally discouraged at transit hubs to avoid the oversupply of shared cars; local authorities should develop a location-based and geographically differentiated quota in managing carsharing programs.

Minimum Cost Matching for Autonomous Carsharing

Carsharing programs provide an alternative to private vehicle ownership. Combining car-sharing programs with autonomous vehicles would improve user access to vehicles thereby removing one of the main challenges to widescale adoption of these programs. While the ability to easily move cars to meet demand would be significant for carsharing programs, if implemented incorrectly it could lead to worse system performance. In this paper, we seek to improve the performance of a fleet of shared autonomous vehicles through improved matching of vehicles to passengers requesting rides. We consider carsharing with autonomous vehicles as an assignment problem and examine four different methods for matching cars to users in a dynamic setting. We show how applying a recent algorithm (Scalable Collision-avoiding Role Assignment with Minimal-makespan or SCRAM) for minimizing the maximal edge in a perfect matching can result in a more efficient, reliable, and fair carsharing system. Our results highlight some of the problems with greedy or decentralized approaches. Introducing a centralized system creates the possibility for users to strategically mis-report their locations and improve their expected wait time so we provide a proof demonstrating that cancellation fees can be applied to eliminate the incentive to mis-report location.

The effect of carsharing on vehicle holdings and travel behavior: A propensity score and causal mediation analysis of the San Francisco Bay Area

We examine the impacts of carsharing on travel behavior utilizing a San Francisco Area subsample of the 2010–2012 California Household Travel Survey. We control for self-selection bias due to differences in observed characteristics of the respondents using propensity-score based matching. We find that vehicle holdings of carsharing members are substantially and significantly lower than for non-members with similar characteristics in terms of individual and household demographics and built environment features of both residential and job location. These differences increase as the propensity to enroll in carsharing programs increases. A latent construct, which measures the propensity to own or utility from owning vehicles and rises with numbers of vehicles owned, is lower for members by 0.3–1.3 standard deviations relative to non-members. Members are also likely to walk, bike, and use transit more frequently than non-members. However, these differences are relatively minor and tend to be statistically non-significant. Future research should control for self-selection bias arising from differences in unobserved characteristics of respondents, as well as simultaneity bias whereby decisions concerning vehicle ownership both influence and are influenced by the decision to join carsharing programs.

Traffic-related air pollution and obesity formation in children: a longitudinal, multilevel analysis.

BackgroundBiologically plausible mechanisms link traffic-related air pollution to metabolic disorders and potentially to obesity. Here we sought to determine whether traffic density and traffic-related air pollution were positively associated with growth in body mass index (BMI = kg/m2) in children aged 5–11 years.MethodsParticipants were drawn from a prospective cohort of children who lived in 13 communities across Southern California (N = 4550). Children were enrolled while attending kindergarten and first grade and followed for 4 years, with height and weight measured annually. Dispersion models were used to estimate exposure to traffic-related air pollution. Multilevel models were used to estimate and test traffic density and traffic pollution related to BMI growth. Data were collected between 2002–2010 and analyzed in 2011–12.ResultsTraffic pollution was positively associated with growth in BMI and was robust to adjustment for many confounders. The effect size in the adjusted model indicated about a 13.6% increase in annual BMI growth when comparing the lowest to the highest tenth percentile of air pollution exposure, which resulted in an increase of nearly 0.4 BMI units on attained BMI at age 10. Traffic density also had a positive association with BMI growth, but this effect was less robust in multivariate models.ConclusionsTraffic pollution was positively associated with growth in BMI in children aged 5–11 years. Traffic pollution may be controlled via emission restrictions; changes in land use that promote jobs-housing balance and use of public transit and hence reduce vehicle miles traveled; promotion of zero emissions vehicles; transit and car-sharing programs; or by limiting high pollution traffic, such as diesel trucks, from residential areas or places where children play outdoors, such as schools and parks. These measures may have beneficial effects in terms of reduced obesity formation in children.

The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios

Carsharing programs that operate as short-term vehicle rentals (often for one-way trips before ending the rental) like Car2Go and ZipCar have quickly expanded, with the number of US users doubling every 1–2years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service used on demand. The advent of autonomous or fully self-driving vehicles will address many current carsharing barriers, including users’ travel to access available vehicles.This work describes the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use. The model operates by generating trips throughout a grid-based urban area, with each trip assigned an origin, destination and departure time, to mimic realistic travel profiles. A preliminary model run estimates the SAV fleet size required to reasonably service all trips, also using a variety of vehicle relocation strategies that seek to minimize future traveler wait times. Next, the model is run over one-hundred days, with driverless vehicles ferrying travelers from one destination to the next. During each 5-min interval, some unused SAVs relocate, attempting to shorten wait times for next-period travelers.Case studies vary trip generation rates, trip distribution patterns, network congestion levels, service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed.

Modeling Station-Based and Free-Floating Carsharing Demand

Carsharing, in any form, is still growing around the world. One of the effects is the increasing number of cities in which multiple carsharing operators are competing. The carsharing industry has never been as competitive as it is now: the present is a good time for researchers to invest efforts in providing tools for the assessment and planning of carsharing programs. Nevertheless, efforts in this direction are still scarce, in particular for some of the newest forms in which carsharing has been implemented, such as free-floating carsharing. This paper reports on a study that made use of MATSim, an agent-based simulation software that had already been used to model station-based carsharing, to evaluate different carsharing scenarios for the city of Berlin. The main findings are the existing high potential to extend carsharing services further in Berlin and the apparent complementarity of station-based and free-floating carsharing. On the methodological level, the work introduces a new tool for the modeling of free-floating carsharing along with improvements of the previously existing station-based carsharing model.

Carsharing in a University Community

As of September 2007, more than 70 colleges and universities in the United States have partnered with carsharing organizations, and this market segment is expected to continue growing. To maximize the benefits of these partnerships, it is important to understand both the unique features of academic institutions as markets for carsharing and ways to predict university-based demand for carsharing services. A study was done to estimate the potential carsharing market at the University of Wisconsin–Madison by ( a) using a stated preference survey to collect information on university affiliates’ transportation habits and carsharing preferences, ( b) developing a set of probabilistic models of willingness to join a carsharing program based on the stated preference survey data, and ( c) applying these models to predict the potential market share under different conditions. Through this process, the relative impact of respondents’ socioeconomic characteristics, current travel habits, attitudes on transportation and the environment, and familiarity with carsharing on their decisions to use carsharing were examined. The results show that a respondent's status at the university (e.g., faculty, student, or staff) had a strong influence over her individual acceptance of car-sharing, even more so than socioeconomic variables such as income or vehicle ownership, and that people's attitudes play an important role in their decision making. Furthermore, the ease of accessing a car is also a critical factor. Although the University of Wisconsin–Madison population was the focus of the analysis, the findings provide useful insights for targeting carsharing programs in other university communities.

North American Carsharing

Carsharing (or short-term auto use) organizations provide members access to a fleet of shared vehicles on an hourly basis, reducing the need for private vehicle ownership. Since 1994, 50 carsharing programs have been deployed in North America–-33 are operational and 17 defunct. As of July 1, 2008, there were 14 active programs in Canada and 19 in the United States, with approximately 319,000 carsharing members sharing more than 7,500 vehicles in North America. Another six programs were planned for launching in North America by January 2009. The four largest providers in the United States and Canada support 99% and 95.2% of total membership, respectively. A 10-year retrospective examines North America's carsharing evolution from initial market entry and experimentation (1994 to mid-2002) to growth and market diversification (mid-2002 to late 2007) to commercial mainstreaming (late 2007 to present). This evolution includes increased competition, new market entrants, program consolidation, increased market diversification, capital investment, technological advancement, and greater interoperator collaboration. Ongoing growth and competition are forecast. Rising fuel costs and increased awareness of climate change likely will facilitate this expansion.