Context-Aware Crowd Management in Smart Cities: A Scenario-Driven Systematic Review of Sensing, Prediction, and Intervention

In smart cities, crowding in transportation hubs, large event venues, and commercial/tourist districts can rapidly escalate from service congestion to public-safety incidents. Real-world operations are constrained by heterogeneous sensing coverage, delayed statistics, privacy requirements, and the need for accountable multi-agency decisions. Following a rigorous PRISMA protocol, we synthesized 107 primary empirical studies (2020–2026) to systematically review context-aware crowd technologies. Moving beyond isolated algorithmic benchmarks, we organized these advances into a mathematically formalized closed-loop framework (Sensing–Prediction–Intervention–Feedback). Crowd sensing has evolved toward edge-based computer vision, passive mobile signaling, and multimodal fusion to balance operational trade-offs among density applicability, environmental robustness, privacy burdens, and end-to-end latency. Prediction architectures—converging on Spatiotemporal Graph Neural Networks (ST-GNNs) and simulation-augmented digital twins—are critically evaluated against constraints in predictive horizon, computational overhead, and explainability. To bridge theory and practical deployment, we deduce a multidimensional evaluation taxonomy and a hierarchical trigger-and-escalation matrix, tailoring control philosophies (e.g., spatiotemporal capacity synchronization and dynamic demand reshaping) to the three heterogeneous scenarios. Finally, we propose four strategic directions to chart a deployment-oriented roadmap for the integrated Urban Brain: edge-based privacy-preserving fusion, cross-scenario generalization, accountable Explainable Artificial Intelligence (XAI) with human-in-the-loop synergy, and end-to-end outcome-oriented empirical validation.