Smart tourism is rapidly revolutionizing the travel industry, integrating advanced technologies to optimize visitor experiences and resource allocation. Recent research introduces artificial neural networks (ANN) as powerful tools for predicting tourist behavior, enhancing service delivery, and enriching economic efficiency within the tourism sector.
With the rise of information and communication technologies (ICT), tourist demands have evolved significantly, requiring innovative solutions to meet these expectations. This study, leveraging big data and ANN, analyzes visitor information to provide personalized service recommendations and effective resource optimization.
The research gathered extensive data comprising visitor profiles, consumption habits, and satisfaction ratings from renowned smart tourism destinations. By optimizing parameters, including learning rate and batch size, the study successfully developed an efficient ANN model. The findings indicate remarkable performance, outperforming traditional statistical methods across multiple performance metrics such as accuracy, recall, and F1 score.
One of the standout findings is the user satisfaction survey conducted on 300 respondents, showcasing considerable approval of the personalized services; 75% reported satisfaction with recommendations tailor-made to their preferences and needs. Specifically, 34% of users expressed being very satisfied with these service recommendations, and 76% were pleased with the optimized resource allocation.
The overarching methodology combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks within the ANN model. This hybrid architecture is adept at managing multi-source, heterogeneous data, addressing spatial data through CNN and temporal data processing via LSTM, establishing clearer consumer insights and predictive capabilities.
Experiments for evaluating the ANN model's efficacy were conducted on high-performance computing platforms with the aid of GPUs to expedite the computational processes. Results indicated optimal performance at learning rates of 0.001 and batch sizes of 128, employing the Adam optimizer, significantly enhancing prediction precision. Comparisons with traditional models indicated the ANN's superiority, significantly outpacing Decision Trees, Random Forests, and Support Vector Machines on all performance metrics with p-values below 0.05.
The training duration for the model spanned approximately 8 hours with sample sizes up to 100,000 tourist behavior records, supporting its scalability and capacity. During inference, the model predicts visitor behavior at impressive speeds of about 10 milliseconds per tourist, proving beneficial for real-time applications.
While the findings herald transformative possibilities for smart tourism applications, certain limitations persist, including the model's response to rapid changes and noise data influences. Cases of misclassification were noted, particularly among low-frequency visitors and those whose preferences fluctuated swiftly, potentially seen during peak tourist seasons.
The study’s conclusions encourage the travel industry to adopt such predictive modeling to bolster operational efficiency and predict consumer trends. The integration of ANN provides tourism managers with valuable insights, aiding strategies for resource allocation, dynamic pricing, and personalized marketing. It is anticipated these innovations will not only refine visitor experiences but also enable sustainable practices within the tourism sector.
This research, backed by ethical approvals from the School of Business Administration, Jiangxi Vocational College of Industry and Engineering, signifies not only academic advancement but also equitable opportunities for enhancing economic efficiency and visitor satisfaction through informed decision-making.