Predicting Night Cultural Tourism Consumption and Generating Personalized Tourism Scenes: An Evolutionary-Optimized Conditional GAN Framework

Abstract

Oleksandr P. Krupskyi, Yuliia M. Stasiuk and Maria Nascimento Cunha

The night-time economy has emerged as an increasingly important component of contemporary urban development, contributing to tourism activity, consumption opportunities, and urban vitality [1]. Within this broader economic ecosystem, night cultural tourism represents a distinctive domain of consumption, encompassing a wide range of activities and experiences, including sightseeing, cultural participation, gastronomy, shopping, entertainment, and leisure [2]. Recent research indicates that night-time tourism consumption is shaped by a combination of spatial, socioeconomic, infrastructural, and technological factors. Xiong et al for example, identified significant spatial disparities in national night cultural tourism consumption agglomeration zones in China, highlighting the influence of social development, tourism industry development, economic conditions, and transportation [1]. Similarly, Zhang et al. demonstrated the value of integrating multi-source data to assess the spatial distribution and suitability of nighttime tourism environments [2].

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