Short- and long-term prediction and determinant analysis of tourism flow networks: A novel steady-state Markov chain method

Abstract

Predicting tourism flow networks offers important insights for destination development but remains a methodological challenge. This study develops a novel steady-state Markov chain method, leveraging trajectory big data to facilitate short- and long-term predictions of interactions and distributions between nodes in tourism flow networks, using Tibet as a case study. The results demonstrate that the tourism flow network effectively elucidates intricate interrelationships. In the short term, the one-step transition probability matrix identifies tourists' potential next destinations, reflecting dynamic network changes. Over the long term, the Markov chain steady-state vector uncovers the stable distribution of tourists, emphasizing shifts in node significance. Additionally, the determinants influencing tourism destinations and different nodes have continuously evolved, whether assessed from a global influence or spatial heterogeneity perspective. Beyond its theoretical contributions, this paper offers practical implications for destination planning and intelligent decision-making management information systems.