Next Article in Journal
A Mobility-Assisted Localization Algorithm for Three-Dimensional Large-Scale UWSNs
Next Article in Special Issue
Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory
Previous Article in Journal
Integrated Use of Measurements for the Structural Diagnosis in Historical Vaulted Buildings
Previous Article in Special Issue
Secure Route-Obfuscation Mechanism with Information-Theoretic Security for Internet of Things
Open AccessArticle

An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

School of Information & Engineering, Lanzhou University, Lanzhou 730000, China
School of Computing and Information Technology, University of Wollongong, Wollongong 2522, Australia
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4291;
Received: 30 June 2020 / Revised: 24 July 2020 / Accepted: 29 July 2020 / Published: 31 July 2020
(This article belongs to the Special Issue Internet of Things, Big Data and Smart Systems)
With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment. View Full-Text
Keywords: ITS; IoT; reinforcement learning; MRAL; multi-agent; MAAC; edge computing ITS; IoT; reinforcement learning; MRAL; multi-agent; MAAC; edge computing
Show Figures

Figure 1

MDPI and ACS Style

Wu, Q.; Wu, J.; Shen, J.; Yong, B.; Zhou, Q. An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control. Sensors 2020, 20, 4291.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop