Real time automatic urban traffic management framework based on convolutional neural network under limited resources constraint

Abstract

Automatic traffic flow monitoring and control systems have become one of the most in-demand tasks due to the massive growth of the urban population, particularly in large cities. While numerous methods are available to address this issue with an unconstrained use of computational resources, a resource-constrained solution is yet to become publicly available. This paper aims to propose a real-time system framework to control the traffic flow and signals dealing with resource limitation constraints. Experimental results showed a high accuracy performance on the desired task and the scalability of the proposed framework.