Urban traffic congestion remains a major challenge for city mobility. This study addresses this issue by presenting an intelligent traffic control system that unifies speed control, parking management, and turn permissions using Deep Reinforcement Learning with Proximal Policy Optimization (DRL + PPO). The approach is applied to a Manhattan-style grid of 16 intersections across 4 traffic scenarios and evaluated under three control methods (static, rule-based, and PPO) over approximately 4 M training steps. Using a SUMO–Python simulation environment, the system dynamically adjusts speed limits, parking zones, and intersection turns based on real-time input variables to prevent bottlenecks. The system analyzes multiple traffic indicators to make coordinated decisions and evaluates its effectiveness across diverse congestion patterns. Results show that the PPO system improves traffic performance across all scenarios, demonstrating scalability in terms of traffic demand (within the tested topology), achieving: CO 2 (CO 2 Emissions) ↓19–31%, QL (Queue length) ↓18–35%, ATT (Average Travel Time) ↓8–18%, and TF (Traffic Flow) ↑5–7%, compared with rule-based speed adjustment and static baselines. Furthermore, joint management of all three controls outperforms the best individual strategy, achieving additional improvements of 17–32% depending on the case. These findings demonstrate a clear coordination dividend, where unified control of speed, parking, and turns yields superior performance compared to operating them independently, offering a modular and potentially adaptable alternative for the development of more sustainable cities and the intelligent modification of traffic rules.

As our cities become more complex and traffic demand grows, managing such traffic efficiently becomes challenging. Hence, solutions that allow building upon the current traffic light systems and that can be readily deployed are of global interest. In this work, we address the challenge of improving traffic light management at intersections. We propose an agent-based traffic light control system where an agent, one per intersection, dynamically regulates the light’s phase cycle depending on the current traffic conditions. To this end, we will rely on Deep Networks to adequately train agents to make good decisions. Simulation results in a realistic scenario using SUMO show that our proposed approach can significantly reduce waiting times, improving transit times by 44% compared to the standard fixed-timing method. Additionally, to assess the effectiveness and reliability of our control algorithm, we introduce new performance metrics.

In Distributed Control Systems (DCS) the distribution of sensor data needs to be optimized in order to reduce the client process redundancy, i.e.: same sensor data processing in different clients. This paper presents an implementation of Embedded Networked Sensors as Smart Resources that allows to access to high-level information abstracting the user from low level mechanisms. The distributed services provided by Smart Resources implement self-aware adaptation mechanisms in order to suit the performance to the context. Information integrity must be also ensured during this adaptation. The Smart Resource implementation is based on a RGBD sensor. Through a set of experiments is evaluated the performance of the reconfiguration and adaptation mechanisms and detailed the influence of the data integrity according to the provided service.