A Stochastic Approach Towards Travel Route Optimization and Recommendation Based on Users Constraints Using Markov Chain

Accurate analysis of tourist movement is essential for a country to devise sustainable policies for promoting and growing tourism. From the activities of tourists and the spots they visit, the amount of revenue generated for a particular region can be predicted. However, the tourist preferences evolve and vary from one user to another, and thus, a tourist spot favorite for one set of users is not preferred by another set of users. This paper aims to design and implement a novel application to recommend an optimal travel route based on user constraints. The user constraints can be the maximum time, distance, and popularity of a particular place. The real data are collected from the Wi-Fi routers installed at different tourist spots of Jeju Island, South Korea. We apply a Markov chain model to predict the popularity of different places on the short- and long-term bases. The popularity index alongside user constraints is provided to find optimal routes. A responsive web-based prototype is developed to collect user constraints, and, in response, recommends optimal routes using the Google Maps directory services. The results indicate the difference between the short- and long-term popularities to prove the effectiveness of the Markov chains in forecasting long-term behavior. The system is made responsive for all sizes of screens to make it uniformly serviceable on mobile phones. The accuracy of the system is computed based on the historical data and the recommendation system, and it is ascertained to fall between 95% and 100% all the time. Furthermore, the results are compared with popular state-of-the-art methods, and they are found to be significantly better than that in the long-term location prediction.