Exploring efficiency and spatial equity trade-offs in docked bike-sharing

This study addresses limitations in bike-sharing planning by integrating a disaggregated demand model based on individual user behavior, generated through agent-based simulations, with a supply model derived from spatial analysis. These inputs are applied to the maximal covering location problem to create a comprehensive framework that evaluates interactions between spatial configurations, station size, scale, and design variables while examining trade-offs between efficiency and equity objectives. Applied in Vienna, Austria, the methodology simulates 1,492 individual demand points, optimizes 1,172 candidate locations, and tests 17 scenarios. By enabling precise distance measurements between users and stations, this approach improves station density predictions and resource allocation. Metrics such as daily trips per station, average bike usage, and population coverage highlight performance at the most granular level. Special attention is given to vulnerable groups, such as low-income populations and immigrants, offering insights into the inherent conflict between efficiency and equity to inform urban mobility planning.