Parking Without Building: Agentic AI for Parking Planning Through Urban Contradiction Reasoning

Cities are in the middle of a parking transition. Minimum parking requirements are being reduced or eliminated, curbs are being repriced, and the goal of planning is shifting from supplying more parking to making better use of the parking that already exists. Yet most parking analytics still answer a question that this transition has retired: where should we build more? We argue that the distinctive value of agentic AI in parking is not better prediction of where to build, but the ability to expose contradictions that conventional workflows suppress—when demand says build but policy says restrain; when inherited rules say comply but theory says question; when market logic says maximize but equity says redistribute; and when stated public frustration says “parking crisis” but utilization data say the supply is ample and mispriced. Parking planning should be reconceptualized as a dynamic, theory-grounded, policy-constrained, human-supervised decision process, organized around a loop between parking theory, parking policy, urban data, agent reasoning, human deliberation, and policy revision—and ultimately answering a political question: what kind of city do we want to be? Under this view, an agentic parking system must be able to recommend shared parking, existing-stock reuse, curb and price reform, and deliberate non-construction, not only new supply. Using the Phoenix Parking Lot Planner as a critical demonstration—critical because its current weighted-factor scoring is precisely the kind of reasoning the proposed loop is meant to transcend—we outline a research agenda and five evaluation standards: contradiction detection, intervention comparison, justification quality, restraint capability, and policy traceability. Parking, precisely because it is measurable, theory-rich, policy-contested, and intervention-ready, may be the most realistic near-term testbed for agentic urban planning.