Urban traffic management in modern smart cities faces significant challenges due to the highly dynamic, non-linear, and decoupled nature of road networks. Conventional control strategies often operate in isolation, failing to address the interdependencies between multiple variables. In light of this, this work introduces an innovative neuro-symbolic distributed control architecture oriented toward intelligent vehicular flow optimization, which fills a critical gap in the current literature by transcending conventional paradigms and orchestrating an operational synergy between decentralized decision algorithms and formal logic. The framework integrates a high-fidelity traffic data ingestion system that feeds a network of agents, whose decisions are strictly contingent upon topology-level deontic logic. In this scheme, agents form functional coalitions coordinated by a mediator, who arbitrates proposed actions against coalition-level deontic logic to resolve sectoral conflicts. Likewise, the system incorporates a passive monitoring layer and system-level deontic logic; the latter acts as a critical safeguard designed to ensure global integrity in the face of potential failures at lower levels. This inherently distributed design allows for the simultaneous multivariable optimization of traffic light phases, speed limits, and parking management within a single coordinated system, avoiding the dimensional explosion typical of centralized systems. This neuro-symbolic approach not only maximizes transport efficiency but also establishes a standard of robustness and explainability essential for Intelligent Transport Systems, subordinating technical optimization to a logical framework that guarantees operational coherence and urban safety.

Current urban traffic management systems present a critical limitation: the operational dissociation between infrastructure control (traffic signal synchronization) and vehicle dynamics (speed profiles). This work addresses this deficiency by introducing a unified multivariable controller that integrates traffic signal synchronization and dynamic speed regulation within a high-dimensional control framework. Unlike conventional systems, in which vehicles passively adapt to static infrastructure, this architecture applies a dual-action control policy in which a Deep Neural Network (DNN) simultaneously determines lane-level speed profiles and real-time traffic signal phase adjustments. Through this coordinated mechanism, traffic signals maintain authority over the temporal structure of the network, while speed regulation governs vehicle flow, consolidating both control functions into a single system, a capability generally absent in previous works. Our approach incorporates a richer set of input variables and controloriented metrics, enabling a more precise characterization of the system. This architecture leverages this enriched information to modify traffic regulations in real time. The methodology compares autonomous traffic signal control, independent speed limit regulation, and the proposed integrated controller. Implemented in Python and the Simulation of Urban MObility (SUMO) platform, the architecture employs an adaptive feedback loop to synchronize traffic lights and speed limits in real time. Experimental results demonstrate substantial improvements over traditional strategies, achieving a 25.55% reduction in queue lengths, a 24.98% improvement in traffic flow, and a 27.89% reduction in carbon dioxide (CO2 ). By consolidating heterogeneous control actions into a single computational engine, this framework provides a potential scalable foundation for future connected mobility.

This paper summarizes a research line aimed at intelligent urban traffic management through the transition from static controllers to multivariable distributed architectures.It describes the development of the CICADA architecture, which integrates the simultaneous control of traffic lights, speed limits, turns, and parking using deep reinforcement learning and deontic logic to ensure normative safety.Validations conducted in SUMO simulation environments demonstrate that coordinating these variables significantly reduces queues, travel times, and pollutant emissions, outperforming traditional monovariable approaches.The proposed system establishes a scalable foundation for future connected and autonomous mobility in smart cities.