Digital Twin-Driven Modeling and Resource Scheduling in Smart City Socio-Technical Systems: A Comprehensive Review

Abstract

This comprehensive review systematically examines the architectural paradigms of digital twin-driven modeling, dynamic resource scheduling, and multi-scenario deployment within smart city complex socio-technical systems (STSs). Recognizing that urban environments are not merely physical aggregates but deeply coupled human-machine matrices, we synthesize recent literature to evaluate how digital twin configurations attempt to capture non-linear social behaviors alongside rigid technical infrastructures. Our analysis indicates that while real-time data synchronization enhances computational visibility, existing modeling frameworks still struggle, to some extent, with multi-scale fidelity and cognitive uncertainty. Considering the inherent unpredictability of urban governance, we categorize current resource allocation algorithms, analyzing their performance boundaries across diverse socio-technical friction points. The reviewed evidence suggests that successful multi-scenario applications are potentially moderated by institutional data silos and algorithmic biases, meaning alternative operational interpretations remain plausible. Ultimately, this review highlights that establishing fully adaptive urban digital twins requires resolving these emergent structural vulnerabilities, thereby emphasizing that further empirical research is needed to transition from descriptive visualization to true predictive synchronization across volatile municipal ecosystems.