Robert Binder

This study examines how Autonomous Vehicles (AVs) could affect urban land use, focusing on which types of parcels and land uses are most likely to change under future AV adoption scenarios. Using Los Angeles County as a case study, the research combines a review of existing AV and land use literature with a parcel-level spatial analysis to explore where redevelopment pressures and land use transitions may emerge. The report is intended to help cities and planners better understand the potential — and limitations — of AV-driven land use change.

When digitizing curb management, it is essential to ensure the process does not exacerbate inequities or introduce new ones. Curb data often helps to optimize space, reduce inefficiencies, and improve urban mobility as long as they are implemented holistically within a city and not just within better-resourced communities. This section highlights key considerations related to equity in curb data collection, policy design, and implementation.

Curb data can include both real-time information on curb use as well as static inventories of existing space and regulations. Real-time curb data can support long-term goals, but can also cause cities issues related to cost, accuracy, upkeep, and integration. Static curb data, however, can represent immediate opportunities to support and streamline ongoing city operations, reduce staff and time burdens, and provide a resource to other external users. Boston's SMART grant utilizes artificial intelligence (AI) and machine learning to digitize and analyze street sign images to generate curb data across the city. Digitizing helps to generate a curb inventory available to both city staff and external end users. This report synthesizes existing curb research alongside interviews with 17 City of Boston staff, industry and end-user representatives, and staff from other US cities with experience in curb digitization and management.