scholarly journals Dynamic Phase Signal Control Method for Unstable Asymmetric Traffic Flow at Intersections

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xiancai Jiang ◽  
Yu Jin ◽  
Yanli Ma
Keyword(s):  
Traffic Flow ◽  
Control Method ◽  
Signal Control ◽  
Average Delay ◽  
Lower Efficiency ◽  
Variable Phase ◽  
Dynamic Phase ◽  
Sequencing Studies ◽  
Phase Signal ◽  
Delay Variations

This paper addresses the limitations that the phases proposed in variable phase sequencing studies for stochastic traffic flow are all predetermined and that the variable phase sequencing is only suitable for low traffic volume environment. It presents a dynamic phase signal control method for unstable asymmetric traffic flow with two primary operational objectives: the realization of a dynamic phase scheme in each cycle and optimization of signal control parameters. First, an asymmetric state of traffic flow at signalized intersections is defined, rules governing the generation of the dynamic phase of each cycle based on asymmetric state are proposed, and the delay variations of intersections adopting dynamic phase schemes are modeled. Next, a signal control parameter adjustment algorithm for the dynamic phase is constructed to maximize the positive benefits of delay variation. Last, the operational performance of the proposed method is validated using data collected from an intersection in Harbin, China, by VISSIM simulation. Furthermore, it is found that a higher asymmetric coefficient leads to lower efficiency of a symmetrical release phase scheme at intersections, and the increase of average delay becomes significant when the asymmetric coefficient threshold is greater than 0.2.

Study on Cooperative Control Method among Large Flow and Long-Distance Intersections

2015 ◽  
Vol 744-746 ◽  
pp. 2006-2011
Author(s):  
Jian Lu ◽  
Jin Gang Gu ◽  
Qiang Fu ◽  
Ya Li
Keyword(s):  
Traffic Flow ◽  
Cooperative Control ◽  
Control Method ◽  
Flow Characteristics ◽  
Signal Control ◽  
Coordination Control ◽  
Long Distance ◽  
Average Delay ◽  
Traffic Flow Characteristics ◽  
Large Flow

Large flow and long distance intersections are very difficult to bring out coordination control and very easy to emerge traffic problems such as traffic congestion due to the large flow and long distance and traffic discrete. It’s necessary to analysis the traffic flow characteristics among large flow and long distance intersections and take measure to improve the signal control. This paper analyzed the traffic flow characteristics of the large flow and long distance intersections, such as the speed would be faster than the normal road between near distance intersection, the headway between cars would be increased, and long traffic queue would be easily occurred at the downstream intersection. In order to realize the coordination control between far intersections, measures for example puts forward signal light at an appropriate position between the intersections, set up fences between vehicles and bicycles, and intersection channelized were put forward to rebuild the traffic flow to adjust the signal control. Those measures were applied to the intersections in Danyang which located in Jiangsu province, the results shows that the queue length was reduced by 67.4% at downstream intersection, the average delay was reduced by 60.3%, the traffic flow saturation was reduced to0.67, and the travel speed and travel time would all become better than before. It suggested that those methods could realize the coordination control and its effect was good, and have good feasibility and practicability.

scholarly journals Design of real-time actuated control system for modern tram at arterial intersections based on logic rules

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881542
Author(s):  
Yun Li ◽  
Qiyan Cai ◽  
Yujie Xu ◽  
Weihua Shi ◽  
Yibao Chen
Keyword(s):  
Control Strategy ◽  
Control Method ◽  
Control Model ◽  
Simulation Software ◽  
Signal Control ◽  
Average Delay ◽  
Absolute Priority ◽  
Control Scheme ◽  
Logic Rule ◽  
Trigger Detector

This article is on the purpose of developing an isolated tram signal priority control strategy based on logic rule for modern tram system. The designed method is presented with features that can ensure the intersection operates in a proper manner on the premise of tram priority and avoid vehicle queue overflow when the tram passes. In this study, the new method description consists of two parts: (1) the detector locations are determined, which include the upstream detector, the upstream trigger detector, the downstream detector, and the queuing detector upon entry approach to the intersection; (2) the corresponding priority logical algorithm for signal control is designed. The proposed method is experimentally examined in a tram intersection in Huaian city, China. In the process of the experiment, the detector layout scheme and optimal priority control model are simulated and verified using the VisVAP module of the Vissim simulation software. In the experimental results, the designed scheme significantly decreases average delay than the fixed timing signal control method, while it also can prevent the vehicle queue from overflowing compared to the absolute priority actuated control scheme.

Traffic Flow Data Collection and Signal Control System Based on Internet of Things and Cloud Computing

2013 ◽  
Vol 846-847 ◽  
pp. 1608-1611 ◽  
Author(s):  
Hui Jie Ding
Keyword(s):  
Cloud Computing ◽  
Control System ◽  
Data Collection ◽  
Internet Of Things ◽  
Traffic Flow ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Flow Data ◽  
Traffic Jam ◽  
Sensing Data

As more and more cars are in service, the traffic jam becomes a serious problem in our society. At the same time, more and more sensors make the cars more and more intelligent, and this promotes the development of Internet of things. Real time monitoring the cars will produce massive sensing data, the Cloud computing gives us a good manner to solve this problem. In this paper, we propose a traffic flow data collection and traffic signal control system based on Internet of things and the Cloud computing. The proposed system contains two main parts, sensing data collection and traffic status control subsystem.

scholarly journals An Optimization Model of Multi-Intersection Signal Control for Trunk Road under Collaborative Information

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Li ◽  
Zhengfan Zhao ◽  
Li Liu ◽  
Yao Liu ◽  
Pengfei Li
Keyword(s):  
Optimization Model ◽  
Delay Time ◽  
Traffic Simulation ◽  
Simulation Software ◽  
Transmission Model ◽  
Signal Control ◽  
Cell Transmission Model ◽  
Average Delay ◽  
Main Trunk ◽  
Average Delay Time

We proposed a signal control optimization model for urban main trunk line intersections. Four-phase intersection was analyzed and modeled based on the Cell Transmission Model (CTM). CTM and signal control model in our study had both been improved for multi-intersections by three-phase theory and information-exchanging. To achieve a real-time application, an improved genetic algorithm (GA) was proposed finally, the DISCO traffic simulation software was used for numerical simulation experiment, and comparisons with the standard GA and CTM were reported in this paper. Experimental results indicate that our searching time is less than that of SGA by 38%, and our method needs only 1/3 iteration time of SGA. According to our DISCO traffic simulation processing, compared with SGA, if the input traffic flow is changed from free phase to synchronized phase, for example, less than 900 vel/h, the delay time can reduce to 87.99% by our method, and the minimum delay time is 77.76% of existing method. Furthermore, if input traffic volume is increased to 1200 vel/h or more at the synchronized phase, the summary and minimum values of average delay time are reduced to 81.16% and 75.83%, respectively, and the average delay time is reduced to 17.72 seconds.

scholarly journals A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision

2021 ◽  
Vol 5 (4) ◽  
pp. 80-84
Author(s):  
Luong Anh Tuan Nguyen ◽  
Thanh Xuan Ha
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Traffic Flow ◽  
Density Estimation ◽  
Traffic Density ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Modern Life ◽  
Traffic Jam ◽  
Novel Approach

In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.

scholarly journals Urban Traffic Signal Control Based on Multiobjective Joint Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yongrong Wu ◽  
Yijie Zhou ◽  
Yanming Feng ◽  
Yutian Xiao ◽  
Shaojie He ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Urban Traffic ◽  
Joint Optimization ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Timing Optimization ◽  
Average Delay ◽  
Annealing Algorithm

This paper proposes two algorithms for signal timing optimization of single intersections, namely, microbial genetic algorithm and simulated annealing algorithm. The basis of the optimization of these two algorithms is the original timing scheme of the SCATS, and the optimized parameters are the average delay of vehicles and the capacity. Experiments verify that these two algorithms are, respectively, improved by 67.47% and 46.88%, based on the original timing scheme.

scholarly journals Deep Reinforcement Learning Based Left-Turn Connected and Automated Vehicle Control at Signalized Intersection in Vehicle-to-Infrastructure Environment

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 77 ◽  
Author(s):  
Juan Chen ◽  
Zhengxuan Xue ◽  
Daiqian Fan
Keyword(s):  
Reinforcement Learning ◽  
Control Method ◽  
Signalized Intersection ◽  
Signal Control ◽  
Left Turn ◽  
Automated Vehicle ◽  
Whole Process ◽  
Policy Gradient ◽  
Experience Replay ◽  
Automated Vehicle Control

In order to solve the problem of vehicle delay caused by stops at signalized intersections, a micro-control method of a left-turning connected and automated vehicle (CAV) based on an improved deep deterministic policy gradient (DDPG) is designed in this paper. In this paper, the micro-control of the whole process of a left-turn vehicle approaching, entering, and leaving a signalized intersection is considered. In addition, in order to solve the problems of low sampling efficiency and overestimation of the critic network of the DDPG algorithm, a positive and negative reward experience replay buffer sampling mechanism and multi-critic network structure are adopted in the DDPG algorithm in this paper. Finally, the effectiveness of the signal control method, six DDPG-based methods (DDPG, PNRERB-1C-DDPG, PNRERB-3C-DDPG, PNRERB-5C-DDPG, PNRERB-5CNG-DDPG, and PNRERB-7C-DDPG), and four DQN-based methods (DQN, Dueling DQN, Double DQN, and Prioritized Replay DQN) are verified under 0.2, 0.5, and 0.7 saturation degrees of left-turning vehicles at a signalized intersection within a VISSIM simulation environment. The results show that the proposed deep reinforcement learning method can get a number of stops benefits ranging from 5% to 94%, stop time benefits ranging from 1% to 99%, and delay benefits ranging from −17% to 93%, respectively compared with the traditional signal control method.

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