EXPLORING THE ROLE OF AI IN PREDICTIVE MAINTENANCE FOR ROAD TRANSPORT INFRASTRUCTURE

Abstract

Predictive maintenance has revolutionized vehicle and infrastructure management and reduced maintenance costs and downtime for intelligent transportation systems. This study investigates the potential use of AI in smart transportation systems to predict equipment failures and schedule repair. Predictive algorithms use real-time sensor data from vehicles and infrastructure to identify possible issues before they become serious enough to require costly repairs or system failure. Among these models are neural networks and scheme. Transportation systems may move from reactive to proactive maintenance practices, supporting efficiency, safety, and dependability, by embracing AI-driven solutions. For real-time operations, anomaly detection is done using unsupervised learning techniques, and we employ supervised learning techniques to look for trends in historical maintenance data. Predictive maintenance using AI is also being researched for usage in smart cities, where IoT is connecting transportation networks. The difficulties of integrating data are also discussed in the study, along with the significance of scaling algorithms and ensuring cybersecurity in these systems. According to our research, artificial intelligence (AI) has the potential to improve operational efficiency, save labor and material costs associated with equipment maintenance and repairs, and slow down the rate of interruptions in smart transportation systems.