earticle

영어

Addressing traffic congestion in Vehicular Adhoc Networks (VANETs) is crucial for ensuring safety, social welfare, and economic progress. This study introduces a novel approach utilizing transfer learning in conjunction with the Gradient Boosting algorithm to optimize information transmission within VANETs. By leveraging pre-trained nodes as information sources, the proposed model effectively trains newly registered nodes, enhancing congestion control performance. Simulation results conducted in Python demonstrate the model's effectiveness, showcasing reduced execution times compared to traditional fuzzy logic-based methods. Integration of this model into existing congestion control systems promises real-time congestion screening capabilities. The study highlights the importance of further research collaboration to tackle realtime implementation challenges and advance traffic congestion management using AI-based techniques. Simulation results have indicated that the proposed system model achieves a performance of 95.43% accuracy. It also noted that the use of the proposed system in producing the HRA results is more accurate compared to the past methods.