AI-DRIVEN PREDICTIVE MAINTENANCE FOR ROAD TRANSPORT INFRASTRUCTURE
Keywords:
Artificial intelligence, Machine learning, Internet of Things, Convolutional Neural Networks, Light Detection and RangingAbstract
Predictive maintenance is transforming vehicle and infrastructure management by reducing costs and downtime in intelligent transportation systems. This study explores the application of AI in smart transport to anticipate equipment failures and optimize repair schedules. Using real-time sensor data, predictive algorithms—such as neural networks—identify issues before they lead to costly breakdowns. Unsupervised learning enables real-time anomaly detection, while supervised models analyze historical maintenance data for patterns. AI-powered predictive maintenance supports a shift from reactive to proactive practices, enhancing efficiency, safety, and reliability. The study also examines its integration within IoT-connected smart cities, addressing challenges like data integration, algorithm scalability, and cybersecurity. Findings suggest AI can significantly improve operational efficiency, lower maintenance costs, and reduce system disruptions in modern transportation networks.
