AI-driven eco-routing and tourist behavior modeling for sustainable destination marketing

Type of the article: Research Article

 Abstract
The growing pressure on tourism systems caused by overtourism, environmental degradation, and uneven spatial distribu-tion of tourist flows highlights the need for adaptive and sustainability-oriented management approaches. This study develops and tests an AI-driven eco-routing and tourist behavior modelling framework (ER-TBM) as a choice-architecture tool for sustainable destination marketing. The framework combines a fixed graph representation of a synthetic destination, Markov decision states, tabular Q-learning, and congestion-sensitive recommendations. To address reproducibility, the benchmark specifies ten points of interest, 22 undirected edges, all edge attributes, tourist preference and fatigue distributions, Poisson arrivals, recommendation compliance, congestion and emission functions, training settings, stopping rules, and random seeds. Five baselines and ER-TBM were evaluated under normal, peak, and event-demand conditions using 30 paired independent runs per scenario with 120, 180, and 240 agents, respectively. Relative to Dijkstra routing, ER-TBM reduced mean congestion exposure by 5.1-5.3% and increased tourist utility by 0.6-0.7%, while mean CO2 remained statistically indistinguishable and monetary route cost was 9.4-9.8% higher. The simulation-based Tourism Sustainability Index ranged from 0.712 to 0.732 for ER-TBM and did not show practically meaningful superiority over optimized baselines. The findings indicate that behavior-aware eco-routing can redistribute simulated visitor flows without a statistically significant increase in emissions, although this benefit is accompanied by higher monetary route costs. From a sustainable destination marketing perspective, the proposed framework represents a computational choice-architecture proposition. Its potential to influence actual tourist behavior, recommendation acceptance, destination image, satisfaction, and revisit intention requires further field-based validation.

Acknowledgment
The research was conducted within the framework of the prospective development plan for the scientific field “Social Sci-ences” No. BF/25-2026 on the topic “Formation of a Competitive Development Model for the Ukrainian Economy: Human Capital, Financial Stability, and the Development of Innovative Industries amid War-Related Challenges and the Emergence of the Sixth Technological Paradigm”.