Xiqun Michael Chen, Jianjun Wu, Jianjun Wu, Lu Zhen, Zhe Liang, Yong Chen, Haodong Yin, Xin Guo, Jingwen Wu, Jingwen Wu, Zhexin Lu, Ziyou Gao
Abstract Rapid urbanization and growing mobility demand are reshaping transportation systems, calling for more advanced intelligence and management capabilities. Artificial intelligence (AI) has emerged as a key enabler for enhancing perception, prediction, and decision-making in transportation. This paper presents a systematic review of AI applications across four major transportation domains: road, rail, air, and maritime systems. Rather than exhaustively surveying all published studies, this review adopts a thematic synthesis approach, organizing representative, recent research by major transportation modes and core AI application scenarios, with an emphasis on influential studies published in leading journals and conferences. The review examines representative applications and key functionalities within each domain, highlighting how AI techniques—such as deep learning, graph-based models, reinforcement learning, and emerging foundation models—are adapted to diverse transportation contexts. Furthermore, this paper analyzes key challenges, including data quality and sparsity, interpretability, uncertainty, and cross-domain generalization, and discusses emerging research directions such as foundation models, physics-informed learning, and continual adaptation. By integrating insights from both methodologies and real-world applications, this review provides insights for advancing intelligent, scalable, and resilient transportation systems.