Special issue on emerging artificial intelligence in smart transportation

Artificial intelligence is rapidly reshaping transportation science by expanding the ways in which complex mobility systems can be perceived, predicted, optimised, and governed. This special issue brings together 24 papers on emerging AI methods and their applications across smart transportation systems, covering traffic state estimation, travel behaviour modelling, urban mobility prediction, autonomous driving, connected vehicle perception, low-carbon transport operations, shared mobility, logistics, ports, mining, and infrastructure management. Together, the contributions show how machine learning, graph neural networks, reinforcement learning, interpretable AI, computer vision, foundation models, and hybrid physics-learning approaches can support safer, greener, more efficient, and more equitable transportation systems. The papers offer both methodological advances and practical insights for the continued development of AI-enabled transportation science.