Advances of artificial intelligence in green transportation

Artificial Intelligence (AI), with its advanced data analysis and decision-making capabilities, provides new opportunities for improving transportation efficiency and promoting sustainable development. This paper presents a systematic review of recent advances in AI applications for green transportation, focusing on five key areas: intelligent scheduling and traffic optimization, unmanned technologies, green transportation infrastructure, shared mobility services, and policy support. The review highlights that AI technologies can effectively improve traffic management, optimize resource allocation, reduce energy consumption, and decrease carbon emissions through intelligent decision-making and data-driven approaches. Furthermore, AI-enabled autonomous systems, smart infrastructure, and shared mobility services contribute to the development of safer, more efficient, and low-carbon transportation systems. However, several challenges remain, including data privacy, system integration, model robustness, and policy coordination. Future research should focus on strengthening interdisciplinary collaboration, integrating emerging technologies, and developing effective governance frameworks to support large-scale deployment. This review provides comprehensive insights into the current progress, challenges, and future directions of AI-driven green transportation, offering valuable guidance for researchers and policymakers.