AI-driven predictive maintenance in autonomous public transit systems for smart cities

Abstract

The introduction of artificial intelligence (AI) in the city transport infrastructure is an innovative initiative on the way to creating resilient and efficient smart cities. Predictive maintenance of autonomous public transit systems is among the most–promising applications The real-time diagnostics and machine learning models are used to predict equipment failures and enhance the optimality of the fleet-wide performance. Compared to conventional maintenance procedures, based on regular inspection checks or afterthought reactions, predictive maintenance employs the in-feed of sensor data, telemetry, and past trends to fore-tell system declining conditions before failures set in. Not only will this switch cause the reduction of operational downtimes but also enhance the safety of passengers, cost-efficiency, and the sustainability of services. The current paper will discuss how predictive AI will have a defining role in the maintenance management of autonomous fleets of buses, shuttles, and trams within a networked urban setting. It analyzes AI solutions Deep learning, anomaly detection, and digital twin modeling in the context of vehicle-to-infrastructure (V2I) communication and the Internet of Things (IoT). Europe, Asia, and North America case studies are examined to show real-life deployments and quantifiable results. There is also discussion about ethical issues, cybersecurity risks, programs designed to regulate them, and the urgency of transparent AI generalized to reflect the notion of public accountability and smart city governance. The paper has ended with the roadmap of the strategic approach to advance scalable, trusted predictive maintenance systems that can pre-qualify the autonomous subways of the future in the ever-developing city street scenes.