Sustainable tourism road network route prediction: A Socio-Ecological-Technological integrated analysis framework (ARTEKS : Jurnal Teknik Arsitektur, 2026)
This research proposes a new Socio-Ecological-Technological Integrated Analysis Framework (SETIA) for sustainable tourism road network route prediction and planning. This paper constructs a multidimensional analysis model which integrates social, ecological and technological factors into sustainability analysis. This paper uses machine learning method to analyze tourism road data and apply SETIA framework into Malaysian tourism road network cases. Results show that SETIA framework has higher prediction accuracy than traditional method in assessing three-way interaction effects of electric vehicle use, road condition and traffic congestion. This study provides theoretical reference and guide for the sustainable tourism road network planning in Malaysia, which is of great significance to promote the development of sustainable tourism and United Nations Sustainable Development Goals.
Keywords: Machine learning; Road networks; Socio-ecological-technological integration; Sustainable tourism