TSBOA: Tangent secretary bird optimization based multiagent driven predictive model for vehicle platooning in urban traffic

Vehicle platooning in urban traffic is employed for enhancing efficacy by minimizing the fuel consumption and improving road safety. Nevertheless, the dynamic and uncertain nature of urban traffic creates various challenges for successful platooning. The major intent of this research is to present a Tangent Secretary Bird Optimization Algorithm (TSBOA) for vehicle platooning in urban traffic scenarios. Here, various multi-agent constraints, namely spacing, velocity range, communication delay, and speed, are included to establish which vehicle platooning effectively increases the throughput at intersections. Here, the Model Predictive Control (MPC) is established by utilizing the TSBOA method, where this model relies on vehicle connectivity through Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2 V) communication. Further, the TSBOA method is developed by integrating two optimization methods, such as Tangent Search Algorithm (TSA) and Secretary Bird Optimization Algorithm (SBOA). Furthermore, the distance, position, spacing error, and velocity error attained by TSBOA are 1.1068, 958.05, 0.0002, and 0.0015 m/s.