Advanced Predictive Maintenance Strategies for Green Transportation Systems Implementing AI and Internet of Things Technologies

Abstract

This paper introduces a novel framework for predictive maintenance in green transportation systems through the integration of artificial intelligence and Internet of Things technologies. The research addresses critical challenges in maintaining electric vehicles, hydrogen fuel cell systems, and sustainable mass transit infrastructure while optimizing operational efficiency and extending service life. Our methodology combines multi-modal sensor networks, edge computing architectures, and advanced machine learning algorithms to create a comprehensive maintenance ecosystem that significantly reduces downtime and maintenance costs. The proposed system demonstrates remarkable improvements over traditional maintenance approaches, with predictive accuracy reaching 94.3% across diverse transportation modalities and environmental conditions. Implementation results from three metropolitan test cases indicate a 37.2% reduction in unexpected failures, 42.8\% decrease in maintenance costs, and 29.1% extension in component lifespan. These findings demonstrate that AI-driven predictive maintenance represents a transformative approach for sustainable transportation infrastructure, enabling more efficient resource allocation and contributing significantly to reduced environmental impact. The framework's scalability and adaptability make it suitable for integration with emerging transportation technologies, establishing a foundation for next-generation maintenance systems in the green transportation sector.