🤖 AI Summary
This study addresses the challenges of interpreting tourist behavior from GPS trajectories, which are often hindered by ambiguous activity sequences, substantial noise, and irregular sampling. To overcome these issues, the authors propose a rhythm-consistent semi-Markov generative model for synthetic trajectory creation. The method employs a probabilistic event–POI soft-matching mechanism to map staypoints to candidate points of interest (POIs), thereby constructing semantic stay sequences based on the MID10 POI taxonomy. It further integrates hourly category distributions, time-conditioned transition matrices, and category-dependent dwell-time models to capture human mobility rhythms. Experimental results demonstrate that the generated trajectories closely reproduce the temporal and categorical characteristics of real-world data, effectively quantifying how changes in POI configurations influence spatiotemporal visitation intensity. This approach transcends the limitations of conventional hard-matching strategies and static models, enabling interpretable transportation and geographic scenario simulations.
📝 Abstract
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.