If pooling with a discount were available for the last solo-ridehailing trip, how much additional travel time would users have accepted and for which types of trips?

If pooling with a discount were available for the last solo-ridehailing trip, how much additional travel time would users have accepted and for which types of trips?

Pooled trips in private vehicles, or pooling, can significantly reduce environmental impacts and make more efficient use of limited roadway capacity in an auto-oriented society such as the United States. However, pooling has been historically unpopular in the US, and has become more unpopular for commuting over the past several decades. Ridehailing, i.e. on-demand door-to-door transportation services requested and paid for through smartphone apps, has the potential to facilitate more pooling on congested urban streets, if users accept their pooled ridehailing service options (e.g. UberPOOL, Lyft Share). However, the share of pooled ridehailing is smaller than one might hope, with approximately 20 percent of all ridehailing trips successfully matched and pooled in the most populous cities in the country. The literature still lacks in an in-depth understanding of the preferences for and adoption of pooled-ridehailing, and planners and policymakers are limited in their ability to make informed decisions about the promotion of pooled ridehailing. In this context, the researchers will investigate under what conditions user attributes and trip characteristics could lead ridehailing users to accept/reject a pooled ride for their last solo ridehailing trip, under a hypothetical situation in which pooling were available with discounted fares and longer travel times. In this study, researchers analyze a unique survey data set, collected July–November 2019 in four regions in the Southern US, through coordinated research efforts by four top research universities in these regions, supported by the US Department of Transportation (DOT). The investigators believe the preferences and willingness to accept a pooled ride in the data to be heterogeneous across various users and trips, and that complex and nuanced patterns would emerge especially when we take into account general attitudes and preferences as well as perceived benefits and limitations of ridehailing. Thus, researchers estimate a latent-class choice model, with which we identify several unobserved groups in the sample of ridehailing users, whose preferences related to pooled ridehailing are homogeneous within each group, but heterogeneous across different groups. With the estimated model, investigators compute reduced environmental impacts through an increase in the pooled ride, accepted under the hypothetical situation, in terms of energy consumption, GHG emissions, and criteria pollutant emissions. Findings and implications from this research will inform planners and policymakers of effective measures for the promotion of pooled ridehailing and other sustainable travel behaviors.

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