Collaborative management of dynamic carrying capacity of tourist destinations based on multi-agent deep reinforcement learning and spatio-temporal graph neural networks

Jingying Li

doi:10.1504/ijict.2026.10078160

January 1, 2026International Journal of Information and Communication TechnologyOpen Access

Collaborative management of dynamic carrying capacity of tourist destinations based on multi-agent deep reinforcement learning and spatio-temporal graph neural networks

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Key Points

This research aims to improve the management of tourist destinations using innovative technologies.
Utilized multi-agent deep reinforcement learning techniques
Applied spatio-temporal graph neural networks for data analysis
Developed dynamic models for tourist destination capacity management
Identified effective management strategies for tourist destinations
Demonstrated the capability of algorithms to enhance capacity management

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Implication

The study demonstrates effective management strategies for tourist destinations, indicating benefits of advanced algorithms.

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

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Cite This Study

Jingying Li (2026) studied this question.

synapsesocial.com/papers/69fbf004164b5133a91a44e9 https://doi.org/10.1504/ijict.2026.10078160

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Collaborative management of dynamic carrying capacity of tourist destinations based on multi-agent deep reinforcement learning and spatio-temporal graph neural networks

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Collaborative management of dynamic carrying capacity of tourist destinations based on multi-agent deep reinforcement learning and spatio-temporal graph neural networks

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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