TRIP: Transport reasoning with intelligence progression — A foundation framework

Abstract

The rapid evolution of intelligent transportation systems faces significant challenges, including incomplete traffic state representation, ineffective fusion of multi-source heterogeneous knowledge, and difficulties in hierarchical decision optimization. Traditional methods often isolate physical dynamics from semantic contexts, leading to fragmented reasoning and suboptimal control. To address these limitations, we propose TRIP (Transport Reasoning with Intelligence Progression), a novel framework grounded in dual state space theory. TRIP decomposes traffic system states into a physical state space and a semantic state space, interconnected via learnable mappings that ensure bidirectional, Lipschitz-continuous alignment. Leveraging advancements in large language models and world models, TRIP employs a hierarchical reinforcement learning approach to enable progressive reasoning—mimicking human expertise by transitioning from semantic understanding to physical prediction and action. Key innovations include cross-modal alignment to bridge data-driven and knowledge-based paradigms, scalable dual state space modeling for efficient long-sequence processing, and theoretical guarantees for stability and robustness. By unifying physical and semantic intelligence, TRIP lays the theoretical foundation for interpretable, real-time transportation systems capable of navigating complex, dynamic environments while balancing global optimization with local constraints. This work bridges a critical gap in ITS, offering a pathway toward adaptive, human-centric urban mobility solutions.