Rhythm-consistent semi-Markov simulation of tourist mobility rhythms with probabilistic event-to-POI assignment: Hakone, Japan

TL;DR

This paper introduces a probabilistic, rhythm-aware semi-Markov model to simulate tourist mobility patterns using noisy GPS data, validated with data from Hakone, Japan.

Contribution

It develops a novel probabilistic POI assignment method and a rhythm-consistent simulation framework for more accurate tourist mobility modeling.

Findings

Close match between observed and simulated temporal profiles.

Probabilistic labeling preserves key mobility structures.

Scenario analysis reveals shifts in stay intensity due to POI changes.

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.