GPS-Enhanced Tourist Mobility Modeling with Seasonal Spatial Priors and LLM-Based Activity Chain Generation

Researchers present a four-stage framework for modeling tourist mobility in urban areas using GPS data, spatial priors, demographic analysis, and LLM-based activity generation. The approach privacy-preservingly synthesizes individual tourist schedules that align with survey data and observed visitation patterns, demonstrated through case study analysis in Tokyo.

This research addresses a genuine gap in urban transportation planning by developing methods to simulate individual tourist travel patterns rather than aggregate statistics. Tourist mobility differs fundamentally from resident commuting in its unpredictability, seasonal variation, and dependence on attractions and group composition—factors that traditional transportation models poorly capture. The framework's innovation lies in combining multiple data sources intelligently: GPS-derived spatial priors inform where tourists travel, demographic profiling predicts trip extent, network algorithms assign feasible location sequences, and large language models generate contextually appropriate activity chains reflecting household constraints.

The methodology demonstrates strong privacy preservation by using only aggregated GPS patterns rather than individual trajectories, addressing growing concerns about location data handling. The Tokyo case study validates that synthetically generated schedules reproduce both survey-reported visitation distributions and staypoint-derived monthly patterns, suggesting the model captures real behavioral structure. This represents meaningful progress in computational urban planning, where synthetic mobility data enables scenario testing and infrastructure planning without relying solely on aggregate statistics that obscure individual decision-making patterns.

The application potential extends beyond tourism to broader mobility modeling challenges. City planners, transportation authorities, and tourism boards could use such frameworks to forecast demand during peak seasons, optimize transit services for visitor flows, and design attractions strategically. The LLM component adds flexibility compared to purely algorithmic approaches, allowing incorporation of complex behavioral rules and temporal constraints. However, the framework's effectiveness depends heavily on input data quality and the completeness of constraint specifications. Future development should explore generalization across different cities and seasonal contexts.

• →GPS-based spatial priors combined with LLM generation create realistic individual tourist mobility schedules while preserving privacy
• →Synthetic tourist schedules align closely with survey data and observed monthly visitation patterns in Tokyo validation
• →The framework addresses seasonal variation and household co-travel rules, capturing tourist-specific behavioral patterns missing from traditional models
• →Privacy-preserving use of aggregated GPS data reduces concerns about individual location tracking in tourism research
• →Application potential spans urban planning, transit optimization, and attraction design for tourism-dependent cities