AI-in-the-Loop Planning for Transportation Electrification: Case Studies from Austin, Texas

Abstract

This study explores the integration of AI in transportation electrification planning in Austin, TX, focusing on the use of Geospatial AI (GeoAI), Generative AI (GenAI), and Large Language Models (LLMs). GeoAI enhances site selection, localized GenAI models support meta-level estimations, and LLMs enable scenario simulations. These AI applications require human oversight. GeoAI outputs must be evaluated with land use data, GenAI models are not always accurate, and LLMs are prone to hallucinations. To ensure accountable planning, human planners must work alongside AI agents. Establishing a community feedback loop is essential to audit automated decisions. Planners should place Community Experience (CX) at the center of Urban Planning AI.