Modelling Travel Patterns and Predicting Spatial Temporal Movement of Inbound Tourists to India - A Markov Chain Approach
Bibliographic record
Abstract
Demand for tourism must be predicted in order to optimise management, increase income, and making policies for attracting the tourist’s. Repeat visitation along with managing inbound tourism demand is at the core of a destination marketing strategy. Tourism demand modelling has gained popularity among researchers in the current decade because of its predictive and analytical prowess. The present study, while making use of secondary data on inbound tourism (Foreign Tourists Arrivals- FTAs) spanning more than four decades (i.e. 1981-82 to 2022-23), offers valuable insight for Destination Management Organisations (DMO) towards repeat visitation based on results from Markov Chain Analysis. The analysis reveals that the number of tourists from the USA, Australia, and Canada has consistently grown at a faster rate than in other countries. However, decade-wise results from the Markov Chain Analysis identified that visitors from the UK, USA, and Bangladesh emerged as the most loyal sources of tourists visiting India across each decade. Despite this loyalty, projections for FTAs indicate a declining trend from the UK, USA, France, and Canada for the selected period. The research findings emphasize the need for the government to develop competitive tourism marketing strategies for attracting tourists from developed countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".