GPS-Enhanced Tourist Mobility Modeling with Seasonal Spatial Priors and LLM-Based Activity Chain Generation
arXiv:2605.29578v1 Announce Type: new Abstract: Tourist mobility poses a distinct challenge for urban transportation planning. Unlike resident commuting, tourist travel is largely non-routine, attraction driven, and highly sensitive to trip purpose, travel season, and trip member composition. Existing approaches either measure aggregate tourist spatial patterns without generating individual schedules, or synthesize mobility without tourist specific structure such as trip duration conditioning, month varying attraction demand, and household co-travel rules. To address these challenges, we propo
The proliferation of GPS data and advancements in AI, particularly LLMs, are enabling more sophisticated and personalized urban modeling techniques at an accelerated pace.
This development allows for more accurate and granular understanding of tourist behavior, which can significantly improve urban planning, resource allocation, and targeted marketing strategies for cities and tourism-dependent economies.
The ability to generate individual, attraction-driven tourist schedules, considering various exogenous factors, moves beyond aggregate spatial patterns to predictive, personalized mobility simulations.
- · Urban Planners
- · Tourism Boards
- · Smart City Initiatives
- · Personalized Travel Platforms
- · Traditional Tourism Research Firms
- · One-size-fits-all Urban Infrastructure
Improved urban infrastructure and service provision tailored to dynamic tourist flows.
Increased competition among destinations leveraging advanced predictive analytics for tourism income.
Potential for privacy concerns and ethical debates around the use of highly granular personal mobility data for commercial and governmental purposes.
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Read at arXiv cs.AI