How to Cite
Abstract
The work relies on a successful collaboration of cellular and short range communication and proposes a solution to control the traffic channel access in an intelligent manner based on Recognition of the traffic flow type at an intersection together with reinforcement learning. The paper, as in the introduction, outlines the contextual background of each traffic control preference, presented as a review of state-of-the-art traffic control algorithms and methods, with a focus on Traffic Signal Control (TSC). This work analyses and points out some error regions in a naive traffic signal controller which are overlooked by existing controllers, including Machine Learning-based methods like Reinforcement Learning (RL), Traffic Flow Search Control, Vehicular Ad-hoc Network (VANET) based methods, Controller with Variable Cycles, and also from Intelligent Transportation System Literature’s Discretized Linear Quadratic Regulator (LQR) Models based control strategies [1].
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