Researchers have developed a new framework integrating Hidden Semi-Markov Models (HSMM) with survival analysis to study long-term metro usage patterns. This method, applied to Shanghai metro data from 2021-2024, identifies five distinct mobility states and analyzes user disengagement and re-entry dynamics. The findings indicate that while exit from the system is state-dependent but not duration-dependent, the hazard of re-entry decreases significantly with longer inactivity periods. AI
RANK_REASON The cluster contains an academic paper detailing a new methodological framework for analyzing long-term dynamics. [lever_c_demoted from research: ic=2 ai=0.4]
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