Mathematical Foundations for Trustworthy AI Applications in Transportation Systems

Editors

Description

This special issue focuses on the mathematical and computational foundations necessary for developing trustworthy artificial intelligence applications in transportation systems. As AI increasingly influences critical transportation decisions affecting safety, efficiency, and equity, there is an urgent need for rigorous mathematical frameworks that ensure these systems are reliable, interpretable, and accountable.

The special issue welcomes contributions that advance the theoretical understanding and practical implementation of trustworthy AI in transportation. This includes work on mathematical models that enhance transparency, ensure robustness against adversarial attacks, quantify uncertainties, and address fairness concerns in transportation algorithms and systems.

Submissions should demonstrate how mathematical and computational approaches contribute to building trustworthy AI systems that stakeholders can understand, verify, and depend upon for critical transportation applications.

Potential topics

• Mathematical frameworks for explainability and interpretability in transportation AI
• Formal verification and validation methods for autonomous vehicles
• Trustworthy machine learning algorithms for traffic prediction and management
• Robustness and adversarial resilience in transportation systems
• Uncertainty quantification in AI-based transportation applications
• Fairness and bias mitigation in transportation decision-making systems
• Privacy-preserving techniques in transportation data analytics
• Game-theoretic approaches to multi-agent transportation systems
• Causal inference methods for transportation research
• Probabilistic and Bayesian methods in transportation AI