Traffic control using V-2-V Based Method using Reinforcement Learning

Nowadays with the increase in advancement of traffic network methodology we have potentials to control traffic congestion and hindrance using huge range of traffic management strategies. Feasibly there are two most promising techniques proffered are chaos theory and reinforcement leaning techniques, the goal of this research technique is to make up a model that self-sufficiently learns by itself the optimal policy. In this paper, we use V-2-V based fuzzy node mechanism and chaos theory that notifies where the traffic could get clustered. On other hand, our reinforcement learning agent makes up discretions (signal status) for the proffered environment.