Control Systems for Real-Time Traffic Management in Smart Cities

Abstract

Rapid urbanization and the proliferation of connected mobility are intensifying demands on urban traffic networks. Smart-city control systems—combining advanced sensing, communications, and optimization—enable real-time traffic management to reduce congestion, emissions, and delays while improving safety and resilience. This article synthesizes the control-theoretic foundations and systems architecture of real-time traffic management, spanning perception (IoT/edge sensing), communication (V2X and fiber backbones), decision (model-based and learning-based control), and actuation (adaptive signal control, ramp metering, dynamic lane assignment). We discuss state estimation under uncertainty, multi-objective optimization for mobility–safety–sustainability trade-offs, and cyber-physical security. Five focal sections outline practical deployment blueprints: (1) sensing and data fusion, (2) communications and edge–cloud orchestration, (3) control algorithms and decision policies, (4) operational integration and governance, and (5) robustness, safety, and cybersecurity. We conclude with implementation guidance and research directions toward human-centric, equitable, and climate-aligned mobility systems.