理解游客移动中活动参与的时间与序列特征是出行行为研究的核心，但GPS轨迹存在噪声、采样不规则，且与活动地点的关联较弱，从而限制了其解释性与情景分析能力。为此，我们采用显式的先验-似然加权方法，将每个停留事件以概率方式映射至候选兴趣点（POIs），生成归一化的兼容性分布，而非硬性匹配。基于2021年11月日本箱根地区高密度游客轨迹数据，我们依据观测所得场所类别标签（MID10）构建语义化停留事件序列，并通过按小时-类别的分布剖面、类别间转移关系及期望停留模式刻画移动节奏。在此节奏特征基础上，我们开发了一种节奏一致的半马尔可夫模拟器，可生成具有时间条件化转移与类别依赖型停留行为的合成停留事件序列。在实测数据中，按小时-类别的汇总统计通过对软标签进行概率加权聚合获得；在模拟中，每个事件均以离散类别及采样所得停留时长生成，从而支持聚合后的一致性对比。我们进一步开展反事实POI名录情景分析，量化假设性POI配置变化对不同时段、类别及空间（尤其是枢纽与主干廊道周边）停留强度的影响。观测数据与模拟结果的对比显示，二者在时间分布剖面与类别分布上高度吻合，表明概率化标注与节奏一致模拟既保留了关键移动结构，又为交通地理学情景评估提供了可解释的基础。

Understanding the timing and sequencing of activity participation in tourist mobility is central to travel behavior research, yet GPS trajectories are noisy, irregularly sampled, and only weakly linked to activity locations, which limits interpretation and scenario analysis. We address this by mapping each stay event to candidate points of interest (POIs) probabilistically, using explicit prior-likelihood weighting that yields a normalized compatibility distribution rather than hard matching. Using one month of high-density tourist trajectories in Hakone, Japan (November 2021), we construct semantic stay-event sequences based on observed place-category labels (MID10) and describe mobility rhythms through hour-by-category profiles, category transitions, and expected dwell patterns. Building on these rhythm signatures, we develop a rhythm-consistent semi-Markov simulator that generates synthetic stay-event sequences with time-conditioned transitions and category-dependent dwell behavior. In the observed data, hour-by-category summaries are computed by probability-weighted aggregation over soft labels; in simulation, each event is generated with a discrete category and a sampled dwell duration, enabling like-for-like comparison after aggregation. We further conduct counterfactual POI-inventory scenarios to quantify how hypothetical POI configuration changes shift stay intensity across time, categories, and space, particularly around hubs and main corridors. Observed-simulated comparisons show close agreement in temporal profiles and category distributions, indicating that probabilistic labeling and rhythm-consistent simulation preserve key mobility structure while providing an interpretable basis for transport-geography scenario evaluation.