Geographic Information System-Based Comprehensive Shared Micromobility Station Siting Optimization for Small Urban Areas

Abstract

Shared micromobility travel modes such as dockless e-scooters and bikeshare programs have become increasingly popular in the U.S. in the last decade because of their potential to improve multi-modal accessibility within communities. Smaller urbanized areas with lower population densities and fewer resources for system planning, operation, and maintenance present unique challenges with siting optimal station/service area locations. This research develops a comprehensive geographic information system (GIS)-based methodology for optimizing micromobility stations/service area locations using available and rasterized geospatial data to capture bikeshare demand indicators. Inputs are prioritized by overall importance according to the results of a survey of transportation professionals, with weights calculated by an analytic hierarchy process. These different factors are combined to create a new spatial index value to identify hotspots for candidate station/service area locations, which can be further analyzed to choose optimal locations based on the budgeted quantity of station/service area locations and ideal spacing between stations/service areas. The case study of the methodology is presented on a bikeshare station siting study in Iowa City, Iowa, U.S., using data from the Metropolitan Planning Organization of Johnson County. This research seeks to improve shared micromobility station/service area planning to better orient service to a variety of transportation goals including regular/commuting use, recreational use, equity, first/last mile connection to transit, and operational partnership opportunities. Multimodal travel times and job accessibility in the study area are evaluated both before and after the introduction of bikeshare, and both greatly improve with the introduction of optimal stations. Public agencies could expect to benefit from this comprehensive methodology because it uses easily obtainable data sources and provides the flexibility to weight the importance of factors differently to fit their communities’ specific transportation goals.