Enterprise AI Analysis

Problem: Traditional mobility simulations lack cognitive depth and semantic coherence, failing to capture the 'why' behind human movement. While LLMs offer rich descriptions, they struggle to balance creative reasoning with the structural compliance needed for realistic activity plans and dynamic adaptation.

Solution: The Narrative-to-Action framework operationalizes a multi-layer cognitive architecture. It first generates rich, diary-style narratives (macro-level desire), then parses them into structured activity plans (intention). During execution, a reflective decision module, parameterized by MEO, enables agents to adapt their plans based on contextual feedback and individual occupational flexibility. Finally, agents perform micro-level actions (location/transport selection) grounded in environmental constraints.

Impact: This approach yields highly realistic synthetic trajectories that not only mirror real-world patterns but also provide interpretable representations of human decision logic. It marks a significant shift from data-driven to cognition-driven simulation, offering a scalable pathway for understanding, predicting, and synthesizing complex urban mobility behaviors with enhanced fidelity and explainability.