There is an increasing emphasis on enhancing the efficiency traffic management systems. Information is exchanged between the vehicular nodes to efficiently monitor and control huge volumes of vehicle. All existing applications in this area have focused on reliable data exchange and authentication process of vehicular nodes to forward messages. This study proposes a new entity centric trust framework using decision tree classification and artificial neural networks. Decision tree classification is used to derive rules for trust calculation and artificial neural networks are used to self-train the vehicular nodes, when expected value is not met. This model uses multifaceted role and distance based metrics like Euclidean distance to estimate the trust. The proposed entity centric trust model, uses a versatile new direct and recommended trust evaluation strategy to compute trust values. The suggested model is simple, reliable and efficient in comparison to the other popular entity centric trust models.
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