From Predictive Accuracy to Human-Centric Decision Support: An Operational HCT-ML Framework for Intelligent Transportation Systems

Abstract

Machine learning in intelligent transportation systems (ITS) is commonly evaluated through predictive performance, yet deployment decisions also depend on whether outputs are relevant to a defined decision, understandable to intended users, equitable across affected groups, uncertainty-aware, subject to appropriate human authority, and actionable within operational constraints. This Perspective develops the Human-Centric and Trustworthy Machine Learning (HCT-ML) framework as an operational decision-support profile for ITS. Its novelty is not the invention of new responsible artificial intelligence (AI) principles; rather, HCT-ML integrates established requirements around four transport-specific constructs—decision owner, decision horizon, intervention pathway, and consequence of error—and translates six human-centric dimensions (relevance, explainability, fairness, uncertainty, human oversight, and actionability) into evidence requirements, candidate indicators, context-specific thresholds, and non-compensatory deployment gates. The framework is benchmarked against the National Institute of Standards and Technology (NIST) AI Risk Management Framework, Organisation for Economic Co-operation and Development (OECD) AI Principles, the European Union (EU) AI Act, Institute of Electrical and Electronics Engineers (IEEE) 7000-series standards, and United States Department of Transportation (USDOT) AI-assurance guidance. We further provide a formal operationalization, application-specific priority profiles, real-world safety cases from automated-driving investigations, and a worked municipal road-safety protocol using the Montréal open-data context. The article does not claim empirical validation or causal safety gains; instead, it provides an evidence-informed and reproducible protocol that can be tested through stakeholder studies, simulations, and field deployments.