2026-10-08T05:35:36Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33991033 2026-09-24T22:03:19Z category_554 portal_316 item_type_3 month_year_09_2026

Supplementary file 1_Governing generative AI in urban mobility: a five-pillar framework and twelve falsifiable propositions for operations, accountability, and public trust.pdf Nirmal Acharya (25105879) Padmaja Kar (7371830) Transport Engineering AI risk management algorithmic accountability foundation models generative artificial intelligence mobility justice transit operations urban mobility governance 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). Candidate designs include partial least squares structural equation modeling (PLS-SEM), survival analysis, difference-in-differences, and scenario-based public surveys.</p>Implications<p>The framework offers transport authorities an incremental procurement standard (T2/A2/E2/A’-2/H2 baseline), vendors design-phase boundaries, and regulators a lifecycle conformity-assessment scaffold, while supplying researchers with an empirical agenda.</p> 2026-09-24T22:03:19Z Dataset Dataset 10.3389/ffutr.2026.1951756.s001 https://figshare.com/articles/dataset/Supplementary_file_1_Governing_generative_AI_in_urban_mobility_a_five-pillar_framework_and_twelve_falsifiable_propositions_for_operations_accountability_and_public_trust_pdf/33991033 CC BY 4.0