Intelligent traffic light under fog computing platform in data control of real-time traffic flow
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Intelligent traffic light under fog computing platform in data control of real‑time traffic flow Haoshu Qin1 · Huimei Zhang2 Accepted: 28 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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 VBAMATLAB 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. Keywords Traffic congestion · Reinforcement learning · Control system of itl · Malicious attack · Security control scheme
* Huimei Zhang [email protected]; [email protected] 1
College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223001, China
2
College of Pharmacy, Chongqing Medical and Pharmaceutical College, Chongqing 401331, China
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H. Qin, H. Zhang
1 Introduction With continuous growth of the population and the increased number of vehicles, a series of traffic problems such as traffic accidents, traffic congestion, and environmental pollution have become prominent. Statistics show that the loss caused by traffic congestion in Beijing Municipality has reached more than 150 billion Yuan, about 8% of the annual gross domestic product (GDP) in Beijing [18, 19, 41, 44]. Traffic congestion can seriously restrict the economic development. It often occurs at intersections, which may increase the number of vehicle parking times, extend the parking time, and bring more exhaust emissions. In addition, vehicle exhaust can aggravate the smog and environmental pollution [1]. Barth and Boriboonsomsin [5] have shown that strategies to alleviate traffic congestion can not only increase the speed of traffic flow, but also reduce CO2 emissions by nearly 20%. At prese
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