Congestion Minimization using Fog-deployed DRL-Agent Feedback enabled Traffic Light Cooperative Framework

Abstract

Congestion at signalized intersections can be alleviated by improving traffic signal control system's performance. In this context, Deep Reinforcement Learning (DRL) methods are increasingly gaining attention towards collaborative traffic signal control in vehicular networks for improving the traffic-flow. However, the existing collaborative methods lack in accounting the influence of neighbouring intersections traffic while working at a particular junction as built on the top of traditional client-server architecture. To address this, a Fog integrated DRL-based Smart Traffic Light Controller (STLC) cooperative framework is proposed via TCP/IP based communication among Fog node, Road Side Cameras (RSCs) and STLCs at the edge. The significant contributions of this work are: (1) A Fog node integrated DRL agent is proposed to minimize average waiting time and queue length, at the intersection, by generating Cycle Phase Duration (CPD) for the STLC via an appropriate coordination among neighboring intersections; (2) Utilizing the Fog node generated CPD as the feedback, a max-pressure based algorithm is proposed, for the STLC at the edge to improve the congestion at the intersection; (3) The performance of the proposed framework is analyzed on Indian cities OpenStreetMap utilizing the Simulation of Urban MObility (SUMO) simulator by varying arrival rate of the vehicles. The results demonstrate the effectiveness of the method over same line state-of-the-art methods.