Development of a smart campus landscape lighting system using space syntax theory

Conventional campus lighting relies on static illumination, causing energy inefficiency and safety concerns. To address this, this study proposes a smart lighting framework integrating Space Syntax analysis with computer vision and Internet of Things (IoT) technologies for real-time pedestrian sensing. Using National Taipei University of Technology (NTUT) as a case study, pathway accessibility values were combined with pedestrian density to develop a dynamic control model using a cloud-based microservice architecture that supports data processing, adaptive dimming, and visualization. Field experiments demonstrate that lighting allocation based solely on spatial integration reduces energy consumption by approximately 27%. Incorporating pedestrian flow dynamics achieves an additional 9% to 12% reduction without compromising visual requirements or perceived safety. The results indicate that spatial configuration provides a robust quantitative basis for adaptive strategies, extending Space Syntax to infrastructure control while integrating human-centered design, energy efficiency, and smart city technologies in campus and urban environments.