Shared micromobility (e.g., e-scooters, bikes, e-bikes) offers moderate-speed, space-efficient, and “carbon-light” mobility, promoting environmental sustainability and healthy travel. While the popularity and use of shared micromobility has grown significantly over the past decade, it represents a small share of total trips in urban areas. To better understand shared micromobility ridership, researchers from across the U.S. and the world have analyzed statistical associations between shared micromobility usage and various explanatory factors, including socio-demographic and -economic attributes, land use and built environment characteristics, surrounding transportation options (e.g., public transit stations), geography (e.g., elevation), and micromobility system characteristics (e.g., station capacity). To understand what these studies collectively mean in terms of expanding shared micromobility usage, we conducted a meta-analysis of 30 empirical studies and then developed robust estimates of factors that encourage ridership across different markets.

The goal of this study is to assess and quantify the potential employment accessibility benefits of Shared Autonomous Mobility Service (SAMS) commute modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study employs a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. This research further captures heterogeneity of workers using latent class analysis (LCA). The LCA model inputs include the socio-demographic characteristics of workers to subsequently account for different worker clusters valuing different types of employment opportunities differently. The accessibility analysis results indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits.