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

2026-05-28 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new four-stage simulation framework is proposed for GPS-enhanced tourist mobility modeling, designed to overcome limitations in existing urban transportation planning approaches. This framework integrates month-conditioned spatial priors derived from aggregated, privacy-preserving GPS and survey data, predicts trip extent based on tourist demographics, assigns distance-feasible ward sequences, and generates activity chains using a Large Language Model (LLM) under household and spatial constraints. Unlike previous methods, it accounts for non-routine, attraction-driven tourist travel, trip duration conditioning, month-varying attraction demand, and household co-travel rules. Experiments conducted on tourism in Tokyo demonstrate the framework's effectiveness, showing that its GPS-based tourist cohort extraction accurately recovers spatial visitation signatures consistent with survey references. Furthermore, it produces demographically aligned synthetic schedules whose ward-level visitation shares closely match both survey distributions and staypoint-derived monthly visitation patterns, proving its utility as a geographically grounded and demographically aware approach.

Key takeaway

For urban transportation planners developing future infrastructure, this framework offers a robust method to model tourist mobility. You can generate demographically aligned synthetic schedules and visitation patterns, accounting for seasonal variations and household co-travel. This allows you to anticipate demand more accurately than aggregate measures, informing better resource allocation and infrastructure design. Consider integrating such LLM-enhanced simulation for more granular, privacy-preserving insights into non-routine travel.

Key insights

The framework combines GPS data, demographics, and LLMs to simulate realistic, demographically-aligned tourist mobility for urban planning.

Principles

• Tourist mobility is non-routine and attraction-driven.
• Aggregate GPS data can inform spatial priors.
• Demographics influence trip extent and patterns.

Method

A four-stage simulation: 1) month-conditioned spatial priors from GPS/survey, 2) trip extent prediction from demographics, 3) ward sequence assignment, 4) LLM-based activity chain generation.

In practice

• Model tourist travel for urban planning.
• Generate synthetic tourist schedules.
• Validate models with survey data.

Topics

• Tourist Mobility Modeling
• Urban Transportation Planning
• Large Language Models
• GPS Data
• Activity Chain Generation
• Spatial Priors

Best for: AI Scientist, Research Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.