Data-driven Analysis of Shared Micromobility Ridership and Service Inequities

This dissertation uses large-scale data to analyze shared micromobility systems (SMS), such as dockless scooters and bikes. Through a combination of meta-analysis, spatial modeling, and equity assessments, I identify key factors driving SMS ridership and evaluate how equitably these services are distributed across neighborhoods in U.S. cities. The research reveals insights into the roles of sociodemographics, urban infrastructure, and land use in shaping SMS usage and producing disparities in access to SMS vehicles. These findings aim to inform more inclusive and efficient urban mobility strategies, promoting equitable and sustainable transportation solutions for diverse communities.