Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

Optimizing traffic control systems at intersections can reduce network-wide fuel consumption as well improve flow. While signals have conventionally been controlled based on predetermined schedules, various adaptive developed recently using advanced sensors such cameras, radars, and LiDARs. By utilizing rich information enabled by the sensors, more efficient or optimal signal is possible in response to varying conditions. This paper proposes an method minimize real-time provided sensors. new employs a priority metric calculated weighted sum of factors, including total number vehicles, vehicle speed, waiting time, road preference. Genetic Algorithm (GA) used global optimization determine weights metric. In order evaluate effectiveness proposed method, simulation model high-fidelity environment called SUMO, real-world network. The flow within this simulated actual measured data from network, enabling comprehensive assessment novel realistic results show that metric-based algorithm significantly compared conventional fixed-time coordinated actuated methods are currently modeled Additionally, incorporating truck leads further improvements reduction.