Artificial intelligence, machine learning and deep learning in advanced transportation systems, a review
Siavash Saki, Mohsen Soori
Abstract
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing transportation systems, addressing critical challenges such as congestion, inefficiency, safety, and sustainability. This paper provides a comprehensive review of these transformative technologies, exploring their applications across various domains, including traffic management, autonomous vehicles, smart parking systems, public transit optimization, freight and logistics, sustainability initiatives, safety enhancements, and infrastructure monitoring. Real-world implementations are examined, highlighting their successes and limitations in practical contexts. While AI-driven solutions have demonstrated significant potential, they face persistent challenges, including data scarcity, limited model generalization, and high computational demands that hinder scalability and reliability. Ethical and regulatory issues, including bias, accountability, and privacy concerns, further complicate adoption. This paper identifies these challenges and discusses emerging research opportunities, such as federated learning, multimodal transportation optimization, and energy-efficient AI systems, to address gaps and advance the field. By synthesizing current advancements, identifying limitations, and proposing future directions, this paper emphasizes the critical role of AI, ML, and DL in shaping smarter, safer, and more sustainable transportation systems.