Modelling Travel Patterns and Predicting Spatial Temporal Movement of Inbound Tourists to India - A Markov Chain Approach

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.

Keywords: Tourism Demand Modelling, DMO, Foreign Tourist Arrivals, Growth, Inbound Tourism, Markov Chain, Projection

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