Description

This theme establishes a comprehensive scientific, computational, mathematical, and engineering framework for investigating the application of artificial intelligence (AI), machine learning (ML), deep learning, and advanced data analytics to road traffic engineering and intelligent transportation systems. It examines how data-driven computational methods can be developed, trained, validated, interpreted, and integrated into transportation decision-making systems to predict traffic conditions, identify congestion patterns, classify accident severity, reconstruct traffic states, detect operational anomalies, and support intelligent network management. Particular emphasis is placed on the integration of transportation engineering principles with advanced learning architectures capable of representing nonlinear relationships, spatial dependencies, temporal variations, heterogeneous traffic behaviour, and complex interactions across interconnected road networks.