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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 …