Source record

Federal Railroad Administration, Machine Vision Data Products for the Automated Track Inspection Program (Washington, DC: U.S. Department of Transportation, 2025).

Method: Literature

A Framework for Trustworthy Deployment: Governance, Human-Automation Teaming, and a Phased Implementation Roadmap for AI in Track Base Management

When the Model Is Wrong: Risks, Hazards, and Failure Modes of AI in Safety-Critical Track Inspection

The Economics of Knowing: Predictive Maintenance, Degradation Forecasting, and the Asset Value Case for Substructure AI

A Framework for Trustworthy Deployment: Governance, Human-Automation Teaming, and a Phased Implementation Roadmap for AI in Track Base Management

When the Model Is Wrong: Risks, Hazards, and Failure Modes of AI in Safety-Critical Track Inspection

The Economics of Knowing: Predictive Maintenance, Degradation Forecasting, and the Asset Value Case for Substructure AI

A Framework for Trustworthy Deployment: Governance, Human-Automation Teaming, and a Phased Implementation Roadmap for AI in Track Base Management

When the Model Is Wrong: Risks, Hazards, and Failure Modes of AI in Safety-Critical Track Inspection

The Economics of Knowing: Predictive Maintenance, Degradation Forecasting, and the Asset Value Case for Substructure AI

A Framework for Trustworthy Deployment: Governance, Human-Automation Teaming, and a Phased Implementation Roadmap for AI in Track Base Management

When the Model Is Wrong: Risks, Hazards, and Failure Modes of AI in Safety-Critical Track Inspection

The Economics of Knowing: Predictive Maintenance, Degradation Forecasting, and the Asset Value Case for Substructure AI