研究人员开发了一个整合了隐半马尔可夫模型（HSMM）和生存分析的新框架，用于研究长期的地铁使用模式。该方法应用于2021-2024年的上海地铁数据，识别出五个不同的出行状态，并分析了用户退出和重新进入的动态。研究结果表明，虽然退出系统与状态相关但与持续时间无关，但重新进入的风险随着不活跃时间的延长而显著降低。 AI
排序理由 该集群包含一篇学术论文，详细介绍了分析长期动态的新方法框架。[lever_c_demoted from research: ic=2 ai=0.4]
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.17939v1 Announce Type: cross Abstract: Understanding how individual metro usage evolves over multi-year horizons is essential for transit planning and passenger retention. However, existing approaches typically characterize mobility patterns as static clusters or short…
Understanding how individual metro usage evolves over multi-year horizons is essential for transit planning and passenger retention. However, existing approaches typically characterize mobility patterns as static clusters or short-term variability, leaving the lifecycle dynamics …