An Intelligent Traffic Light System for Improving Urban Quality of Life: Design and Simulation Scenarios

Growing traffic demand, congestion and transport-related emissions continue to challenge urban mobility and place increasing pressure on existing road infrastructure. This study investigates how smart traffic lights can improve traffic flow and support more efficient urban traffic management. The proposed architecture integrates radar sensors for vehicle and pedestrian detection and vehicle speed estimation with air quality, temperature and humidity monitoring. The CW (HB100) and FMCW (K-LC1a) radar front ends achieve speed resolutions of 0.089 m/s (0.322 km/h) and 0.125 m/s (0.45 km/h), respectively, and the FMCW unit achieves a range resolution of 0.834 m. The study combines simulation models developed in LabVIEW with experimental electronic circuits used to evaluate the system behavior under different traffic conditions. Several scenarios were analyzed in which the signal timing was varied according to the number and type of vehicles, the presence of pedestrians and the air quality. A fuzzy logic controller was also implemented to improve the conditions of pedestrians waiting at signalized intersections. The results demonstrate the operation of the proposed architecture in all investigated scenarios and illustrate how the signal timing adapts to changing traffic and environmental parameters. In 30 simulation runs of 15 min at 1200 veh/h, the proposed controller reduced the mean vehicle waiting time by 20% and the pedestrian waiting time by 13% compared with fixed-time control, whereas the estimated CO2 reduction was small (about 3%). The system is compared with representative approaches from the literature, and its limitations and the remaining research gaps are discussed. The validation presented here is limited to laboratory experiments, LabVIEW simulations and two outdoor gas-sensor campaigns; field tests at real intersections have not yet been performed. Nevertheless, the results indicate that the proposed architecture can support adaptive traffic management and provide a basis for further development and validation in real urban environments.