DESIGN OF TRAFFIC SIGNAL CONTROL SYSTEMS BASED ON FUZZY CONTROL

2020 ◽  
Vol 4 (1) ◽  
pp. 24-27
Author(s):  
Ahmad Fadzli Abd Aziz
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Fuzzy Control System ◽  
Urban Road ◽  
Traffic Efficiency

With a gradual increase in urban road traffic volume and traffic congestion degree, how to improve and solve the current traffic pressures has been a problem requiring urgent solution. To improve the traffic congestion status in urban road intersections and heighten the road traffic efficiency, the principle of fuzzy control is employed in this paper. Moreover, the multi-phase signal traffic control of intersections is performed together with vehicle queue lengths in lanes corresponding to the key traffic flow, and a traffic signal fuzzy control system is designed. Finally, this paper compares the simulation results between the fuzzy control system designed herein and the existing fixed traffic signal control methods. The comparative test results have shown that the fuzzy control method can well better actual congestion and traffic efficiency at intersections to a greater degree than the fixed timing method does.

scholarly journals Research on Urban Traffic Signal Control Systems Based on Cyber Physical Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Li-li Zhang ◽  
Qi Zhao ◽  
Li Wang ◽  
Ling-yu Zhang
Keyword(s):  
Real Time ◽  
Control Strategy ◽  
Road Traffic ◽  
Control Strategies ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
General Control ◽  
Urban Road ◽  
New System

In this paper, we present a traffic cyber physical system for urban road traffic signal control, which is referred to as UTSC-CPS. With this proposed system, managers and researchers can realize the construction and simulation of various types of traffic scenarios, the rapid development, and optimization of new control strategies and can apply effective control strategies to actual traffic management. The advantages of this new system include the following. Firstly, the fusion architecture of private cloud computing and edge computing is proposed for the first time, which effectively improves the performance of software and hardware of the urban road traffic signal control system and realizes information security perception and protection in cloud and equipment, respectively, within the fusion framework; secondly, using the concept of parallel system, the depth of real-time traffic control subsystem and real-time simulation subsystem is realized. Thirdly, the idea of virtual scene basic engine and strategy agent engine is put forward in the system design, which separates data from control strategy by designing a general control strategy API and helps researchers focus on control algorithm itself without paying attention to detection data and basic data. Finally, considering China, the system designs a general control strategy API to separate data from control strategy. Most of the popular communication protocols between signal controllers and detectors are private protocols. The standard protocol conversion middleware is skillfully designed, which decouples the field equipment from the system software and achieves the universality and reliability of the control strategy. To further demonstrate the advantages of the new system, we have carried out a one-year practical test in Weifang City, Shandong Province, China. The system has been proved in terms of stability, security, scalability, practicability and rapid practice, and verification of the new control strategy. At the same time, it proves the superiority of the simulation subsystem in the performance and simulation scale by comparing the different-scale road networks of Shunyi District in Beijing and Weifang City in Shandong Province. Further tests were conducted using real intersections, and the results were equally valid.

scholarly journals IoT Based Traffic Prediction and Traffic Signal Control System for Smart City

2021 ◽  
Author(s):  
Neelakandan S ◽  
Berlin M A ◽  
Sandesh Tripathi ◽  
Brindha Devi V ◽  
Indu Bhardwaj ◽  
...  
Keyword(s):  
Control System ◽  
Traffic Congestion ◽  
Smart City ◽  
Traffic Signal ◽  
Traffic Prediction ◽  
Traffic Density ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Environmental Damage ◽  
Direction Information

Abstract Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80286 microprocessor for a smart city. The proposed system consists of '5' phases, namely, IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data is collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80286 microprocessor. The experimental results show that the proposed system outperforms state-of-the-art methods.

scholarly journals Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network

2022 ◽  
Vol 12 (1) ◽  
pp. 425
Author(s):  
Hyunjin Joo ◽  
Yujin Lim
Keyword(s):  
Control System ◽  
Standard Deviation ◽  
Traffic Congestion ◽  
Queue Length ◽  
Smart Cities ◽  
Traffic Signal ◽  
Optimal Order ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Smart Transportation

Traffic congestion is a worsening problem owing to an increase in traffic volume. Traffic congestion increases the driving time and wastes fuel, generating large amounts of fumes and accelerating environmental pollution. Therefore, traffic congestion is an important problem that needs to be addressed. Smart transportation systems manage various traffic problems by utilizing the infrastructure and networks available in smart cities. The traffic signal control system used in smart transportation analyzes and controls traffic flow in real time. Thus, traffic congestion can be effectively alleviated. We conducted preliminary experiments to analyze the effects of throughput, queue length, and waiting time on the system performance according to the signal allocation techniques. Based on the results of the preliminary experiment, the standard deviation of the queue length is interpreted as an important factor in an order allocation technique. A smart traffic signal control system using a deep Q-network , which is a type of reinforcement learning, is proposed. The proposed algorithm determines the optimal order of a green signal. The goal of the proposed algorithm is to maximize the throughput and efficiently distribute the signals by considering the throughput and standard deviation of the queue length as reward parameters.

Research on Strategy Intelligent Control of Traffic Signal

2015 ◽  
Vol 713-715 ◽  
pp. 889-892
Author(s):  
Yan Min Zhang ◽  
Hai Bing Luo ◽  
Jian Qiang Wang
Keyword(s):  
Control System ◽  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Control Method ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Parameters ◽  
Existing Problems ◽  
Vehicle Capacity

In order to make the traffic flow smoothly through the intersection, to minimize the delay time, must ensure that the control decision of the traffic signal is real-time and accuracy. Aiming at the existing problems of the traditional timing control method, analysis of traffic signal control parameters, performance indicators, put forward the fuzzy inference is applied to the traffic signal control system, puts forward an intelligent traffic signal control system based on fuzzy control; the vehicle queue length, inlet flow rate, green extension as the control parameters and simulation the results show that the proposed control method can effectively improve the traffic congestion, improve the vehicle capacity.

scholarly journals Software-defined Architecture for Urban Regional Traffic Signal Control

2020 ◽  
Vol 32 (2) ◽  
pp. 229-236
Author(s):  
Songhang Chen ◽  
Dan Zhang ◽  
Fenghua Zhu
Keyword(s):  
Traffic Congestion ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
The Novel ◽  
Simulation Experiments ◽  
Microscopic Traffic Simulation ◽  
Control Devices ◽  
Innovation Platform

Regional Traffic Signal Control (RTSC) is believed to be a promising approach to alleviate urban traffic congestion. However, the current ecology of RTSC platforms is too closed to meet the needs of urban development, which has also seriously affected their own development. Therefore, the paper proposes virtualizing the traffic signal control devices to create software-defined RTSC systems, which can provide a better innovation platform for coordinated control of urban transportation. The novel architecture for RTSC is presented in detail, and microscopic traffic simulation experiments are designed and conducted to verify the feasibility.

Sign in / Sign up

Export Citation Format

Share Document