AIGC in Urban Traffic: A Paradigm Shift in Large-Scale State Estimation

As the dynamics of travel demands continue to shift, the accurate prediction of traffic conditions has become increasingly critical. This paper comprehensively charts the evolution of traffic flow prediction methodologies through four distinct phases. Initially, the focus was on assumptions and statistical methods. The second stage advanced to data-driven approaches with in-depth analysis of traffic data. Subsequently, machine and deep learning techniques were introduced, utilizing historical data for future predictions. The current stage explores the potential of Artificial Intelligence Generative Content (AIGC) approaches, including reinforcement learning and generative models for more precise strategies. This paper provides a structured reference for the field, outlining significant literature and advancements in traffic flow prediction.