Table 1.
Summary of Research Gap Analysis in Existing Works.
Fig 1.
Schematic Representation of Proposed TourVaRNN Model.
Table 2.
Summary of Tourism Dataset.
Fig 2.
Learning Tourism Patterns from Hidden State Representation.
Fig 3.
Integration of VAE and RNN Architecture.
Table 3.
International Visitor Analysis for Marketing in Different Regions.
Table 4.
Comparison of Models: Limitations Vs. TourVaRNN Advantages.
Table 5.
Hyperparameter Parameter Setting.
Fig 4.
Direct Expenditure Vs. Economic Impact (2017-2021).
Table 6.
Economic Impact Assessment by Age Group Comparison.
Table 7.
ANOVA Validation for Economic Impact Across Age Groups.
Fig 5.
(a). The Efficiency of Visitor Segmentation Over a Different Age Population. (b). Efficiency of Visitor Segmentation Over a Year.
Table 8.
Confidence Intervals and Variance Analysis for TourVaRNN Predictions.
Table 9.
Visitor Segmentation Validation Accuracy.
Fig 6.
(a). Inference Time Analysis on Distinct Age Groups. (b). Inference Time Analysis Across Years.
Fig 7.
(a). BAU Comparison on Different Age Groups. (b). BAU Comparison on Varying Years.
Table 10.
Segment-wise Spending Analysis.
Table 11.
Marketing Campaign Analysis.
Fig 8.
(a) Avg Expenditure Vs. Age Groups (b) Fig. Dynamic Budget Allocation.
Table 12.
Dynamic Budget Allocation Analysis (Pre-Post Optimization).