Supplementary file 1_Governing generative AI in urban mobility: a five-pillar framework and twelve falsifiable propositions for operations, accountability, and public trust.pdf

Background<p>Generative artificial intelligence (GenAI), encompassing large language models (LLMs), multimodal foundation models, diffusion-based traffic simulators, and agentic planners, is entering urban mobility operations across signal control, dynamic routing, demand forecasting, customer service, and autonomous fleet management. This integration has moved ahead of the governance instruments available to transit agencies, planning organizations, and oversight bodies.</p>Hypothesis<p>Conventional transport-AI governance, designed for narrow predictive models with closed deterministic decision envelopes, struggles when applied to generative systems whose outputs are open-ended, context-sensitive, probabilistic, and directly legible to human operators and citizens.

This mismatch produces a tractable governance vacuum that cross-sector instruments cannot resolve without transport-specific lifecycle scaffolding.</p>Theory<p>A five-pillar lifecycle governance framework is proposed in this study, comprising transparency and explainability, accountability and liability, equity and non-discrimination, auditability and oversight, and human-in/on-the-loop control. Each pillar is operationalized through an original five-level maturity scale (levels 0–4) anchored in transport failure scenarios.</p>Methods<p>Twelve falsifiable propositions link pillar maturity to observable outcomes (incident severity, mean-time-to-resolution, disparate-impact detection latency, override-exercise rate, regulatory survival, and public trust).