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This paper introduces a semi-Markov simulation model to generate synthetic tourist mobility patterns by probabilistically assigning stay events to Points of Interest (POIs) using prior-likelihood weighting. The model captures mobility rhythms through hour-by-category profiles, category transitions, and dwell patterns derived from a month of GPS trajectories in Hakone, Japan. Counterfactual scenarios demonstrate the model's ability to quantify how POI configuration changes impact stay intensity across time, categories, and space.
Forget hard assignments: probabilistically linking tourist stays to POIs unlocks realistic mobility simulations and reveals how hypothetical changes to POI configurations reshape tourist behavior.
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.