Keywords
Smart tourist attractions
Passenger flow forecast
Commercial operation
Submitted : 2025-09-14
Accepted : 2025-09-29
Published : 2025-10-14
Abstract
In order to improve the competitiveness of smart tourist attractions in the tourism market, this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot. Firstly, this paper introduces the big data-driven forecast model of scenic spot passenger flow. Based on the traditional autoregressive integral moving average model and artificial neural network model, it builds a big data analysis and forecast model. Through the analysis of data source, model building, scenic spot passenger flow accuracy, and modeling time comparison, it affirms the advantages of big data analysis in forecasting scenic spot passenger flow. Finally, it puts forward four commercial operation optimization strategies: adjusting the ticket pricing of scenic spots, upgrading the catering and accommodation services in scenic spots, planning and designing play projects, and formulating accurate scenic spot marketing strategies, in order to provide references for the optimization and upgrading of smart tourist attractions in the future.
References
Zhang X, 2025, Research on the Management Path of Tourist Attractions from the Perspective of Smart Tourism. Vitality, 43(15): 100–102.
Guo X, Yu W, Liu Q, et al., 2025, Study on the Planning and Planning of Historical and Cultural Scenic Spots under the Cultural Background of Large Scenic Spots—Taking Jinyang Temple Scenic Spot in Beijing as an Example. Urban Architectural Space, 32(7): 69–71.
Long L, Long S, Xie W, 2025, Research on the Forecasting Method of Parking Demand in Tourist Attractions—Taking Tianmen Mountain Scenic Spot in Zhangjiajie as an Example. People’s Bus, (12): 17–19.
Fang F, Liang R, 2025, Study on the Operation and Audience Preference of WeChat in WeChat Official Account——A Case Study of 5A Scenic Spots in Anhui Province. Journal of West Anhui University, 41(1): 90–96.
Yu J, Ye X, He X, et al., 2025, Forecasting Method of GRU-BiLSTM Tourist Flow Based on Seasonal Adjustment. Journal of China West Normal University (Natural Science Edition), 46(03): 318–324.
Guo X, Liu X, Yao R, 2024, Short-Term Forecast of Jiuzhaigou Scenic Spot Based on SCBANet Model. Journal of Xinxiang University, 41(3): 32–38.
Kang Z, Li X, 2024, Study on Passenger Flow Forecast of Tianchi Scenic Spot in Tianshan Mountain Based on Baidu Index. Vocational Technology, 23(2): 99–108.
Dong M, Jiang W, Wang X, et al., 2023, Research on Passenger Flow Forecast Based on Multi-Attribute Importance Weighted K-Nearest Neighbor Algorithm. Guangdong Communication Technology, 43(5): 10–13 + 20.
Li Y, Li Y, 2022, Study on Tourist Forecast of Scenic Spots Considering the Influence of Holidays-Hybrid Forecasting Method Based on Prophet-NNAR. System Science and Mathematics, 42(6): 1537–1550.
Cui H, Yang X, Yu Y, 2023, Forecast of Tourist Flow Based on EMD-GRU Model—Taking Black Valley Scenic Spot in Chongqing as an Example. Journal of China West Normal University (Natural Science Edition), 44(2): 179–185.