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Travel increases with urban sprawl leading to increased congestion and emissions. Advances in technologies provides new paradigms of transport such as mobility as a service (MaaS), which is a novel concept providing users with mobility services including ride-hailing, carsharing and bike sharing based on their needs. The goal of transitioning to a low carbon economy and recent development of alternative fuel vehicles (AFVs) including electric and hybrids offers a promising mobility option for MaaS. However, the use decisions of these vehicles, specifically the shared use for their role in MaaS is not well understood. Thus, the study is intended to provide information about travel choice of consumers and shared use of electric and hybrid vehicles for their role in MaaS. This article utilizes over 125,345 observations from the National Household Travel Survey 2017 to calibrate Heckman switching regime models for AFV travel choice decisions, accounting for self-selection and heterogeneity bias and Bayesian logistic regression with random parameter specifications to model the shared mobility decision of AFVs while accounting for unobserved heterogeneity and temporal/spatial variation. On average, 20% AFVs and 26% conventional vehicles are used for shared mobility in Uber and Lyft. Additional increase in ride-hailing involving AFVs are observed to substantially reduce net greenhouse gas emissions (GHGs). The use of AFVs for travel depends on factors such as personal interest in technologies. Furthermore, travelers using ride-hailing apps increase the likelihood of using AFVs for shared mobility purposes. This research has implications for making policy decisions and placing incentives to promote the purchase, and shared mobility use of AFVs for MaaS.

Using behavioral data to understand shared mobility choices of electric and hybrid vehicles