Framework for Large-Scale Urban Traffic State Estimation Based on AIGC

Large-scale urban traffic state estimation is essential in intelligent transportation systems (ITSs), particularly in applications like smart navigation and travel mode recommendations, where the precision of trajectory generation is of utmost importance. In this context, a generated trajectory refers to the macro-level path selection between an origin and a destination, tailored to incorporate real-time, personalized routing preferences that accommodate individual user needs and current traffic conditions. Nevertheless, existing studies frequently fail to account for the continuity of the generated trajectories, leading to an accumulation of errors, and often do not cater to personalized user requirements. This paper presents a framework based on Artificial Intelligence Generated Content (AIGC) to facilitate the generation of personalized, continuous trajectories that accurately mirror real-world conditions and user preferences, thereby avoiding the pitfalls of error accumulation. Departing from conventional grid-based spatial–temporal methods, our framework aligns generated trajectories directly with the actual road network and takes into account surrounding Points of Interest (POIs) that could influence travel decisions. Our approach offers a solution to users unsure about waypoint inclusion in their travel plans, greatly enhancing their experience by providing a range of flexible and personalized options. This represents a substantial advancement in the domain of personalized travel recommendations, signifying a transformative step in the evolution of ITSs.