Self-adaptive search optimization-based vehicle path prediction and traffic light controller in vehicular ad hoc network

In recent days, VANET is considered as the main hopeful equipment in the system of transportation since traffic congestion arises regularly and thus occurred road accidents very easily. Besides, the network traffic is increasing due to the huge quantity of information generated in region of urban. Therefore, one of the primary challenges faced by Intelligent Transportation System (ITS) is ensuring the accurate transmission of information in both vehicle-to-vehicle (V2V) and vehicle-to-road (V2R) sensing unit communications. So, here, an adaptive routing controller (ARC) is utilized to improve the data transmission between vehicle and road sensing unit (RSU) and to minimize the traffic density in VANET, and the self-adaptive search optimization is developed in VANET clustering to prioritize vehicles in the lane, in which the multi-objective function is intended depending on the energy of the node, acceleration, jitter, priority, velocity, and trust factors. The traffic light control is done to facilitate the effective communication in the network. Thus, the proposed technique is calculated in terms of throughput, jitter, quadratic mean of acceleration (QMA), and spatially distributed travel time (SDTT), which acquired the values of 0.53 s, 36.07 kmph, 3.472 s, and 43.572%, respectively, while using 50 vehicles at 50 s. The research addresses ITS challenges in accurate info transmission in V2V and V2R communication within VANETs, focusing on urban areas with high traffic. A self-adaptive search optimization technique is proposed for VANET clustering, prioritizing vehicles based on energy, acceleration, jitter, priority, velocity, and trust. The technique's evaluation values are 0.53 s throughput, 36.07 km/h velocity, 3.472 s jitter, and 43.572% SDTT with 50 vehicles in a 50 s test that highlights its potential for improved VANET communication and traffic management.image