Exploring Artificial Intelligence Innovations in Transportation Systems: A Review of Applications and Research Gaps

Artificial Intelligence (AI) has become a key area of science and industry over recent decades, expanding its applications across various sectors, including urban systems, transportation, healthcare, energy, and education. This review paper offers a thorough examination of recent innovations, methodologies, and new applications of AI in different transportation fields. It analyzes recent studies and publications to identify development trends, algorithmic frameworks, and emerging technologies. Additionally, the review addresses major challenges such as ethical concerns, model transparency, data quality requirements, and computational limitations. The findings show a growing trend in transportation research toward integrating deep learning, reinforcement learning, and large language models in multidisciplinary applications. Studies show that the future path in the field of intelligent transportation should move from single models and single-source data to hybrid systems. Also, the use of multi-source data plays an important role in increasing the accuracy and stability of artificial intelligence models. Finally, the paper highlights future research directions and existing gaps to guide ongoing investigations and support the development of smarter, more sustainable transportation systems.