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With the rapid development of the global tourism industry and the widespread adoption of mobile internet technologies, personalized travel itinerary planning has emerged as a crucial method to meet the diverse needs of tourists. Traditional planning approaches, relying on fixed routes and preset attractions, often fail to provide flexible and personalized services. In this context, the application of intelligent tourism technologies has garnered increasing attention, facilitating the provision of customized itinerary planning services through mobile technologies and intelligent algorithms. Although existing study has enhanced the intelligence level of itinerary planning to some extent, it still falls short in considering the comprehensive dimensions of time and space, as well as in responding to the dynamic demands of tourists in real time. This study introduces a mobile predictive model tailored for personalized travel itinerary planning, incorporating the components of a spatio-temporal graph convolutional network (STGCN) with an attention mechanism and spatial node embedding vector components. This model effectively captures the dynamic characteristics of time and space in travel itineraries, achieving personalized recommendations and optimizations. It significantly improves the accuracy and response speed of itinerary planning, providing important support for the digital transformation and innovative development of the tourism industry.
Ding et al. (Wed,) studied this question.