The Enhanced Analysis of FACT-Based Framework for Traffic Prediction in V2V Networks

The vehicular-to-car (V2V) communique networks have enabled a new generation of urban transportation phase. Vehicular conversation technology can offer scalability, dependability, and accuracy in various vehicle offerings. Even though a few modern studies exist on predicting traffic behavior in vehicular networks, only some are primarily based on an analytical method., this paper presents a better analytical method primarily based on the Fuzzy Adaptive Cluster-based total model (fact) for visitor prediction in V2V networks. The truth-based total version includes an extensive fuzzy-common sense-based device to degree the continued conduct and site visitors for a selected target node and then extrapolate the conduct and site visitors for the upcoming frames. Moreover, the model also contains a k-method algorithm to prepare the visitor's statistics into clusters for analysis. The effects of the simulation indicate that the proposed truth-based version is capable of addressing greater precisely and accurately the site visitors' prediction in dynamic site visitor's conditions, attaining higher performance metrics in terms of prediction accuracy, latency, and jitter as compared to techniques, like k-Nearest Neighbor (KNN), assist Vector Machines (SVM), and Multi-Layer Perceptron (MLP). The model has primary importance for its capability as it should be degree and expects V2V site visitors in V2V communications.