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We estimate the effect of carsharing on travel behavior (specifically, household vehicle holdings, frequency of transit usage, and frequency of biking and walking) using data from the 2011–2012 California Household Travel Survey (CHTS). The effect of carsharing on vehicle ownership is a dynamic process that plays out over a period of time—past ownership influences enrollment decisions, which in turn influence holdings in a later period. Representing this process using cross-sectional data conflates causal effects with simultaneity bias. Further, members and non-members differ in various observed and unobserved ways—demographics, built environment of residential and workplace location, and attitudes—raising the potential for self-selection bias in comparing travel behavior between the two groups given the observational nature of the data. Drawing on established methods for dealing with each bias individually, we develop a method to help control for this joint occurrence of self-selection and simultaneity biases. Restricting the analysis to employed respondents residing in the San Francisco Bay Area, we find that 80% of the observed difference of 0.9 units in average vehicle holdings between carsharing non-members and members may be explained by the biases listed above. The remaining difference of 0.17 units reflects the estimated effect of carsharing, which is the equivalent of shedding one vehicle by about one out of every six households whose member(s) are enrolled in carsharing. The effect on transit usage and walking and biking frequency is positive, albeit small and statistically non-significant. We identify factors that may affect the internal and external validity of our results. Our methods cannot completely replace randomized experiments or panel data. However, the methods used here provide a way to help control for the joint occurrence of self-selection and simultaneity biases, and provide a ballpark estimate of causal effects, for large-scale, general-purpose, cross-sectional datasets such as the CHTS. [ABSTRACT FROM AUTHOR]

Over the past decade, carsharing has grown considerably in the United States, particularly in major metropolitan areas. This innovative business model offers individuals the opportunity to rent cars by the hour, providing them with greater flexibility for their mobility. Previous work on carsharing suggests that its adoption leads to a decline in household vehicle ownership, vehicle miles traveled, and associated greenhouse gas emissions. Utilizing representative data from the 2010–2012 California Household Travel Survey, this paper presents an analysis of travel behavior and vehicle ownership among carshare members versus non-members in the San Francisco Bay Area, focusing on a subsample of the population with access to carsharing at the U.S. census tract level. Consistent with previous findings on vehicle reduction, these results show that carsharing members own significantly fewer vehicles than non-members. However, lower levels of vehicle ownership are only found among households living in urban areas. In dense, urban neighborhoods, households with carsharing membership own 0.58 vehicles per household as compared with 0.96 vehicles of a control group. Suburban carshare members drive less than their non-carshare member counterparts – although the extent to which this difference can be attributed to self-selection it is unknown. This study also finds that among carsharing households that do own vehicles, a greater share of those vehicles are alternative vehicles (e.g., hybrid, plug-in hybrid electric, and battery electric). Among vehicles owned by the subsample examined in this study, electric drive vehicles represent 18.3% of those owned by carshare member households, as compared with 10.2% of the vehicles owned by non-carsharing households. This analysis finds that not only are urban carshare members likely to own fewer vehicles than the rest of the population, if they do own vehicles, they are more likely to own a vehicle with a smaller environmental footprint. [ABSTRACT FROM AUTHOR]

Mobility services including carsharing and transportation network company (TNC) services have been growing rapidly in North America and around the world. Measuring the effects of these services on traveler behavior is challenging because the results of any such analysis are sensitive to how (1) outcomes are measured and (2) counterfactuals are constructed. The lack of good control groups or randomization of assignment leaves lingering uncertainty over the contributions of selection bias and treatment effects to reported differences in travel behavior between users and non-users of these services. This paper reports on two approaches for measuring the effects of mobility service adoption on travel rate and car ownership. We first tried a pretest-posttest randomized encouragement experiment to deal with the shortcomings of poor control groups. Then, we turned to the approach of self-reported effects based on hypothetical controls to investigate whether variations in survey question presentation could influence respondents' answers and thus lead to changes in estimated effects. The data to conduct this study came from two sources: a panel survey administered by the authors at the University of Washington (UW), and a survey by Populus Technologies, Inc. (Populus). Various statistical tests were applied to analyze the data, and the results highlight the pivotal role that the research design plays in influencing the outcomes, and manifest the fundamental challenge of establishing credible estimates of the causal effects of adopting mobility services on travel behaviors. • Establishing valid estimates of causal effects of mobility services is challenging. • A randomized encouragement experiment in a pretest-posttest format offers promise. • Question framing is shown to influence self-reported effects of mobility services. • Research design plays a pivotal role in influencing estimated outcomes. [ABSTRACT FROM AUTHOR]