Optimizing Service Resource Allocation Strategies in Smart Tourism Scenic Spots Using Deep Reinforcement Learning
Main Article Content
Abstract
Efficient service resource allocation in smart tourism environments requires accurate perception of dynamic spatiotemporal information and adaptive decision-making under uncertain conditions. This study proposes a deep reinforcement learning framework that formulates scenic resource scheduling as a partially observable Markov decision process (POMDP) and implements optimization using a customized Rainbow Deep Q-Network (Rainbow DQN). The proposed architecture integrates Distributional Reinforcement Learning, Dueling Network, Prioritized Experience Replay, Multi-step Learning, Double DQN, and Noisy Networks with a hybrid CNN–LSTM feature extraction module to jointly model spatial dependencies and temporal evolution from heterogeneous environmental observations. A multiobjective reward function is designed to simultaneously minimize visitor waiting time, improve service resource utilization, and reduce operational costs while penalizing service violations. Extensive simulations under multiple visitor -flow and environmental scenarios demonstrate that the proposed method consistently outperforms representative reinforcement learning baselines, achieving visitor waiting times of 3.9–8.2 min and resource utilization rates of 76.9%– 88.3% with strong robustness under adverse conditions. Beyond intelligent tourism management, the proposed spatiotemporal decision framework provides methodological insights for adaptive resource scheduling in distributed sensing systems, wireless communication infrastructures, and electromagnetic information acquisition networks, where reliable real-time perception and dynamic optimization are essential for efficient system operation.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
X. L. Ma, Z. N. Le, J. X. Guo, and Y. F. Yu, “Research on tourist classification of smart scenic spots based on tourist perception: a case study of Tengwangge scenic spot,” Journal of Central China Normal University (Natural Science Edition), vol. 59, no. 3, pp. 462-470, 2025, doi: 10.19603/j.cnki.1000-1190.2025.03.017.
L. Z. Yu, R. Z. Liu, Y. Chen, C. X. Liu, X. R. Hu, Y. Hu, et al., “Exploration of the application of BIM technology in smart tourism: A case study of Taizishan Ecological Tourism Area in Hubei Province,” Journal of Wildland Fire Science, vol. 43, no. 1, pp. 155, 2025, doi: 10.3969/j.issn.1002-2511.2025.01.036.
H. H. Zhang, L. Y. Zhao, and S. Y. Zheng, “Research on strategies to improve tourism transportation in existing areas: A case study of Shenzhen Phoenix District,” Urbanism & Architecture/Chengshi Jianzhu, vol. 22, no. 499, pp. 48, 2025.
T. Hu, C. Z. Zhang, Y. Zhao, H. G. Xu, and X. D. Cui, “The impact of time distribution of tourist flow in mountain cultural tourism destinations: Big data analysis based on Mount Tai’s tourist flow,” Mountain Research (10082786), vol. 41, no. 3, pp. 435-445. doi:, 2023.
N. Kumar, S. Mittal, V. Garg, and N. Kumar, “Deep reinforcement learning-based traffic light scheduling framework for SDN-enabled smart transportation system,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2411-2421, 2021, doi: 10.1109/TITS.2021.3095161.
C. Shyalika, T. Silva, and A. Karunananda, “Reinforcement learning in dynamic task scheduling: A review,” SN Computer Science, vol. 1, no. 6, pp. 306, 2020, doi: 10.1007/s42979-020-00326-5.
L. Li, Wang Al, Z. T. Wang, and Y. C. Dou, “Research on comprehensive monitoring and operation management system of urban rail transit personnel based on multi-source data fusion,” Railway Computer Applications, vol. 30, no. 10, pp. 47-53, 2021, doi: 10.3969/j.issn.1005-8451.2021.10.10.
X. Q. Wang, X. Y. Xu, Y. K. Wu, and J. Liu, “Urban rail short-term passenger flow prediction based on hybrid deep learning model,” Journal of Railway Science & Engineering, vol. 19, no. 12, pp. 3557, 2022, doi: 10.19713/j.cnki.43-1423/u.T20220158.
M. Jiang, Y. Zhang, and Y. Zhang, “Optimal electric bus scheduling under travel time uncertainty: A robust model and solution method,” Journal of Advanced Transportation, vol. 2021, no. 1, Art. no. 1191443, 2021, doi: 10.1155/2021/1191443.
H. Tang and M. Lu, “UE-Based Digital Twin Engineering Management System for Water Conservancy Scenic Area: A Case of Yijiang River Water Conservancy Scenic Area,” Urbanism & Architecture/Chengshi Jianzhu, vol. 22, no. 496, pp. 195, 2025, doi: 10.19892/j.cnki.csjz.2025.11.43.
J. Chen, T. Peng, Y. S. Cao, and K. L. Liu, “Tourist passenger transport demand prediction model driven by social network data,” Journal of Chongqing Jiaotong University (Natural Science Edition), vol. 41, no. 10, pp. 41, 2022, doi: 10.3969/j.issn.1674-0696.2022.10.06.
Q. Xie, M. Lu, and C. S. Xie, “High-temporal-frequency Forecast of Tourist Flow for Tourist Attraction based on LBS and Deep Learning,” Journal of Geo-Information Science, vol. 25, no. 2, pp. 298-310, 2023, doi: 10.12082/dqxxkx.2023.220231.
S. M. Mortazavi Azad and A. Ramazani, “Smart control of traffic lights based on traffic density in the multiintersection network by using Q learning,” Discover Artificial Intelligence, vol. 3, no. 1, pp. 39, 2023, doi: 10.1007/s44163-023-00087-z.
J. Liu, S. Qin, Y. Luo, Y. Wang, and S. Yang, “Intelligent traffic light control by exploring strategies in an optimised space of deep Q-learning,” IEEE Transactions on Vehicular Technology, vol. 71, no. 6, pp. 5960-5970, 2022, doi: 10.1109/TVT.2022.3160871.
A. Al-Hilo, M. Samir, C. Assi, S. Sharafeddine, and D. Ebrahimi, “UAV-assisted content delivery in intelligent transportation systems-joint trajectory planning and cache management,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 8, pp. 5155-5167, 2020, doi: 10.1109/TITS.2020.3020220.
H. Gao, W. Huang, T. Liu, Y. Yin, and Y. Li, “PPO2: Location privacy-oriented task offloading to edge computing using reinforcement learning for intelligent autonomous transport systems,” IEEE transactions on intelligent transportation systems, vol. 24, no. 7, pp. 7599-7612, 2022, doi: 10.1109/TITS.2022.3169421.
C. Mu, Y. Shi, N. Xu, X. Wang, Z. Tang, H. Jia, et al., “Multi-objective interval optimization dispatch of microgrid via deep reinforcement learning,” IEEE Transactions on Smart Grid, vol. 15, no. 3, pp. 2957-2970, 2023, doi: 10.1109/TSG.2023.3339541.
Y. Zheng, Q. Hao, J. Wang, C. Gao, J. Chen, D. Jin, et al., “A survey of machine learning for urban decision making: applications in planning, transportation, and healthcare,” ACM Computing Surveys, vol. 57, no. 4, pp. 1-41, 2024, doi: 10.1145/3695986.
H. Ge, X. Hu, and M. Cheng, “DHDRDS: A Deep Reinforcement Learning-Based Ride-Hailing Dispatch System for Integrated Passenger– Parcel Transport,” Sustainability, vol. 17, no. 9, pp. 4012, 2025, doi: 10.3390/su17094012.
J. Terven, “Deep reinforcement learning: a chronological overview and methods,” AI, vol. 6, no. 3, pp. 46, 2025, doi: 10.3390/ai6030046.
R. X. Zhang, C. X. Wu, T. C. Sun, and Z. S. Zhao, “Research progress in deep reinforcement learning and path planning,” Journal of Computer Engineering & Applications, vol. 57, no. 19, pp. 44, 2021, doi: 10.3778/j.issn.1002-8331.2104-0369.
A. Correa, A. Jesus, C. Silva, and S. Moniz, “Rainbow Versus Deep Q-Network: A Reinforcement Learning Comparison on The Flexible Job-Shop Problem,” IFAC-PapersOnLine, vol. 58, no. 19, pp. 870-875, 2024, doi: 10.1016/j.ifacol.2024.09.176.
Li Mingyang, Xu Keer, Song Zhiqiang, Xia Qingfeng, and Zhou Peng, “A review of multi-agent reinforcement learning algorithms,” Journal of Frontiers of Computer Science & Technology, vol. 18, no. 8, pp. 1979-1979, 2024, doi: 10.3778/j.issn.1673-9418.2401020.
L. Tian and H. W. Zhong, “Research on the formation factors and action paths of tourist congestion incidents,” Human Geography, vol. 36, no. 4, pp. 159-167, 2021, doi: 10.13959/j.issn.1003-2398.2021.04.019.
X. J. Zhou and S. P. Xi, “Capacity improvement of urban integrated transportation hubs based on resource optimization allocation,” Journal of North China Electric Power University (Social Science Edition), vol. 2, no. 5, pp. 53-61, 2021, doi: 10.14092/j.cnki.cn11-3956/c.2021.05.006.
P. Suanpang, P. Jamjuntr, K. Jermsittiparsert, and P. Kaewyong, “Autonomous energy management by applying deep Q-learning to enhance sustainability in smart tourism cities,” Energies, vol. 15, no. 5, pp. 1906, 2022, doi: 10.3390/en15051906.
W. Wang, N. Kumar, J. Chen, Z. Gong, X. Kong, W. Wei, et al., “Realizing the potential of the internet of things for smart tourism with 5G and AI,” IEEE network, vol. 34, no. 6, pp. 295-301, 2020, doi: 10.1109/MNET.011.2000250.
Q. Sha, L. Ma, and N. Chen, “A Smart Tourism Resource Information Management Platform Based on Multi-Source Data Fusion,” International Journal of High Speed Electronics and Systems, vol. 34, no. 01, Art. no. 2540134, 2025, doi: 10.1142/S0129156425401342.
J. W. Wang, G. Q. Wang, Y. Liu, T. Lei, J. Sun, X. Wang, et al., “Spatio-temporal distribution and network structure of red tourism flow in Jinggangshan,” Journal of Natural Resources, vol. 36, no. 7, pp. 1777-1791, 2021, doi: 10.31497/zrzyxb.20210711.
S. F. Ding, W. Du, J. Zhang, L. L. Guo, and L. Ding, “Research progress of multi-agent deep reinforcement learning,” Chinese Journal of Computers, vol. 47, no. 7, pp. 1547-1567, 2024, doi: 10.11897/SP.J.1016.2024.01547.
A. E. P. Valdiviezo, “El Agente Rainbow en pocas palabras: de RL a la combinacion de mejora de DQN,” Polo del Conocimiento: Revista científico-profesional, vol. 7, no. 7, pp. 2732-2770, 2022, doi: 10.23857/pc.v7i6.
Y. Ji, Y. Wang, H. Zhao, G. Gui, H. Gacanin, H. Sari, et al., “Multi-agent reinforcement learning resources allocation method using dueling double deep Q-network in vehicular networks,” IEEE Transactions on Vehicular Technology, vol. 72, no. 10, pp. 13447-13460, 2023, doi: 10.1109/TVT.2023.3275546.
J. Liu, Y. H. Wang, L. Wang, and Z. Z. Yin, “Non-cooperative multi-user dynamic power control method based on SumTree sampling combined with Double DQN,” Telecommunication Engineering, vol. 63, no. 10, pp. 1603, 2023, doi: 10.20079/j.issn.1001-893x.220428004.
P. Y. Shen, D. M. Wan, and J. X. Li, “The impact of tourism public service quality on tourists’ happiness - based on the perspective of tourism experience,” Tourism Guide, vol. 7, no. 2, pp. 22-54, 2023, doi: 10.12054/lydk.bisu.218.
C. M. Chuang, “A current travel model: smart tour on mobile guide application services,” Current Issues in Tourism, vol. 23, no. 18, pp. 2333-2352, 2020, doi: 10.1080/13683500.2019.1631266.