Hidden markov model to predict tourists visited place

TL;DR

This paper presents a method using a hidden Markov model, learned via grammatical inference, to predict tourists' future locations based on social network data, demonstrated with Paris.

Contribution

It adapts a grammatical inference algorithm to big data for modeling and predicting tourist movements with a flexible hidden Markov model.

Findings

Effective prediction of tourist movements in Paris.

The model adapts well to new data and is scalable.

Demonstrates the utility of social network data for tourism analytics.

Abstract

Nowadays, social networks are becoming a popular way of analyzing tourist behavior, thanks to the digital traces left by travelers during their stays on these networks. The massive amount of data generated; by the propensity of tourists to share comments and photos during their trip; makes it possible to model their journeys and analyze their behavior. Predicting the next movement of tourists plays a key role in tourism marketing to understand demand and improve decision support. In this paper, we propose a method to understand and to learn tourists' movements based on social network data analysis to predict future movements. The method relies on a machine learning grammatical inference algorithm. A major contribution in this paper is to adapt the grammatical inference algorithm to the context of big data. Our method produces a hidden Markov model representing the movements of a group of tourists. The hidden Markov model is flexible and editable with new data. The capital city of France, Paris is selected to demonstrate the efficiency of the proposed methodology.