Interconnected digital twins of subway stations for decision support systems

This article examines a digital twin (DT) for complex infrastructure assets using the subway system as a case study. The proposed solution includes twins for each station, which are interconnected to interact during various operational scenarios. Existing digital twins for subway systems are examined, and their shortcomings—primarily the latency in operator information retrieval due to slow model rebuilding—are identified. The study addresses decision support during medium- and large-scale incidents requiring the synchronization of multiple departments. The authors propose a digital twin architecture based on data collected from cameras, microphones, and sensors, processed within a fog computing environment. A separate digital twin is developed for each station, capable of utilizing templates from previously built assets. An accompanying mobile application with an intelligent assistant displays detected events and answers queries via voice interaction. Implementation results indicate that personnel response time to emerging events improved by 17.4% compared to the REPEAT platform. The proposed architecture is applicable to subway systems and other public transportation networks requiring coordinated personnel actions during hazardous situations.