Route Generation and Built Environment Behavioral Mechanisms of Generation Z Tourists: A Case Study of Macau

Personalized travel experiences have become a growing priority for tourists, while the built environment increasingly shapes tourists’ behavior. However, limited research has integrated behavioral drivers with algorithmic travel route optimization, particularly in the context of Generation Z tourists. To address this gap, this study proposes a hybrid framework that combines behavioral modeling with enhanced algorithmic techniques to generate customized travel itineraries for Generation Z. A behavioral influencing factors model is first constructed based on the Theory of Planned Behavior (TPB) and Social Influence Theory (SIT), identifying media influence (MI), subjective norms (SNs), and perceived built environment (PBE) as potential determinants of travel behavioral intention (BI). A Structural Equation Model (SEM) is then applied to empirically validate the hypothesized relationships. Results reveal that all three factors have a significant and positive impact on BI (p < 0.05). Building on this behavioral mechanism, an interest-based Ant Colony Optimization (ACO) algorithm is implemented by incorporating point-of-interest (POI) preferences and distance matrices to improve personalized route generation. Comparative analysis using social media keyword data demonstrates that the proposed method outperforms conventional travel route planning approaches in terms of route relevance and overall path satisfaction. This study offers a novel integration of psychological theory and computational optimization, providing both theoretical insights and practical implications for urban tourism planning and the development of smart tourism services.