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Published in: The Journal of Supercomputing 5/2021

12-10-2020

Intelligent traffic light under fog computing platform in data control of real-time traffic flow

Authors: Haoshu Qin, Huimei Zhang

Published in: The Journal of Supercomputing | Issue 5/2021

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Abstract

As the global economy develops rapidly, traffic congestion has become a major problem for first-tier cities in various countries. In order to address the problem of failed real-time control of the traffic flow data by the traditional traffic light control as well as malicious attack and other security problems faced by the intelligent traffic light (ITL) control system, a multi-agent distributed ITL control method was proposed based on the fog computing platform and the Q learning algorithm used for the reinforcement learning in this study, and the simulation comparison was conducted by using the simulation platform jointly constructed based on the VISSIM-Excel VBA-MATLAB software. Subsequently, on the basis of puzzle difficulty of the computational Diffie–Helleman (CDH) and Hash Collision, the applicable security control scheme of ITL under the fog computing was proposed. The results reveal that the proposed intelligent control system prolongs the time of green light properly when the number of vehicles increases, thereby reducing the delay time and retention rate of vehicles; the security control scheme of ITL based on the puzzle of CDH is less efficient when the vehicle density increases, while that based on the puzzle of Hash collision is very friendly to the fog equipment. In conclusion, the proposed control method of ITL based on the fog computing and Q learning algorithm can alleviate the traffic congestion effectively, so the proposed method has high security.

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Metadata
Title
Intelligent traffic light under fog computing platform in data control of real-time traffic flow
Authors
Haoshu Qin
Huimei Zhang
Publication date
12-10-2020
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 5/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-020-03443-3

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