What question did this study set out to answer?

Develop an intelligent optimization framework utilizing deep reinforcement learning to balance cultural tourism resource allocation and dynamic tourist flow guidance.

August 16, 2026Advanced ElectromagneticsOpen Access

Research on the Optimization of Cultural Tourism Resource Allocation and Tourist Flow by Integrating Reinforcement Learning

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Authors

MHM. L. Huo

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Implication

Computational modeling study demonstrates optimized resource allocation and tourist flow distribution across urban networks, suggesting enhanced sustainability for smart tourism management.

Key Points

Develop an intelligent optimization framework utilizing deep reinforcement learning to balance cultural tourism resource allocation and dynamic tourist flow guidance.
Constructed a dynamic environment model incorporating resource attractiveness, capacity constraints, and tourist behavioral prediction.
Implemented an improved Deep Q-Network integrated with a multi-agent collaborative mechanism and a multi-objective reward function across simulated environments from five major tourist cities.
Resource utilization increased from 68.7% to 93.3%.
Peak tourist concentration decreased to 40.9%, while overall tourist satisfaction improved to 0.84.

Cite This Study

M. L. Huo (2026) studied this question.

synapsesocial.com/papers/6a817ab4f2fb91fc834aec13 https://doi.org/10.7716/aem.v15i3.3865

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Implication

Computational modeling study demonstrates optimized resource allocation and tourist flow distribution across urban networks, suggesting enhanced sustainability for smart tourism management.

Research on the Optimization of Cultural Tourism Resource Allocation and Tourist Flow by Integrating Reinforcement Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

Research on the Optimization of Cultural Tourism Resource Allocation and Tourist Flow by Integrating Reinforcement Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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