Dubai Metro deploys AI-powered robots to optimize maintenance and safety

Dubai Metro’s AI-powered ARIIS system cuts inspection times by 75%, saving 1,700 hours and reducing maintenance costs by 25%. It enables safer, faster, and more efficient track inspections without service disruption.

Categorized in: AI News Operations
Published on: Aug 18, 2025
Dubai Metro deploys AI-powered robots to optimize maintenance and safety

Dubai Metro Integrates AI-Driven Maintenance for Greater Efficiency

UAE / Transport

Last updated: August 17, 2025 | 23:19 WAM | 2 MIN READ

The Dubai Roads and Transport Authority (RTA) has deployed the Automated Railway Infrastructure Inspection System (ARIIS), a robotic platform that uses artificial intelligence to inspect rail tracks and critical infrastructure without disrupting metro services. ARIIS employs advanced sensors, lasers, and 3D cameras to perform autonomous inspections, reducing the need for traditional manual checks that consume significant time and resources.

Boosting Operational Maintenance and Safety

By integrating AI technologies, the Dubai Metro is advancing its maintenance strategies with a focus on safety and efficiency. ARIIS supports proactive maintenance through real-time data analytics and diagnostics, which helps extend infrastructure lifespan and reduce maintenance costs by up to 25%. Resource management efficiency has improved by 40%, minimizing unnecessary maintenance interventions.

Significant Time and Cost Savings

Since its introduction, ARIIS has cut periodic inspection times by 75%, saving nearly 1,700 human working hours, and decreased traditional inspection operations by up to 70%. Infrastructure condition assessments have improved by 40%, allowing faster maintenance scheduling, fewer emergency repairs, and higher overall reliability of the metro network.

The RTA is currently rolling out ARIIS across select metro lines, with plans to extend it network-wide after successful evaluations. It is also exploring the use of similar AI-driven inspection technologies for other transport modes like trams, depending on infrastructure and operational needs.

Data-Driven Predictive Maintenance

ARIIS collects detailed data on rail conditions, such as cracks, wear, and alignment deviations. This data is analyzed using AI algorithms to enable predictive maintenance, helping to sustain infrastructure quality and extend service life. Inspections are conducted during nightly maintenance windows to avoid service disruption and are coordinated with the control center following strict occupational safety standards.

Thanks to ARIIS, inspections have become faster and more precise, eliminating the need to suspend metro services during checks.

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