Based on Road Green Wave Effect of Collaborative Strategy of Signal Timing Fuzzy Control

2013 ◽  
Vol 321-324 ◽  
pp. 1836-1841
Author(s):  
Yi Zhang ◽  
Geng Sheng Huang
Keyword(s):  
Fuzzy Control ◽  
Traffic Control ◽  
Road Traffic ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Wave Effect ◽  
Density Correlation ◽  
Dynamic Coordination

With growth of Urban Road Traffic Volume and the increase of Road Network Density, correlation between adjacent road intersections is becoming more and more obvious. An intersection traffic signal adjustment tends to affect the health of a number of adjacent intersections road traffic flow. Its congestion may over time gradually spread to within a few blocks and regions all around the intersection. Therefore increasingly high demands of urban traffic signal control make a variety of advanced control technology integration, achieve the purpose to adjust a control parameter, in order to achieve dynamic coordination within the city - wide traffic control, to satisfy traffic demands, and then let the road traffic and the transport demand make a new balance. And This article introduces is the use of the green wave effect collaborative strategies adjacent green extension of fuzzy control in order to solve the problem of coupling between intersections road. This algorithm makes Signal Timing to be more flexible.

A Study of Signal Timing Control Based on the Collaborative Strategy of Road Green Wave Effect and Path Selection Entropy

2013 ◽  
Vol 753-755 ◽  
pp. 1970-1975 ◽  
Author(s):  
Gang Wang ◽  
Yi Zhang ◽  
Geng Sheng Huang
Keyword(s):  
Road Traffic ◽  
Path Selection ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Timing ◽  
Coordination Control ◽  
Timing Control ◽  
Wave Effect ◽  
Density Correlation ◽  
Trunk Road

With the growth of urban road traffic volume and the increase of road network density, correlation between adjacent road intersections is becoming more and more obvious. An intersection traffic signal adjustment tends to affect the smooth traffic flow of a number of adjacent intersections. Its congestion may gradually spread to a few blocks and regions all around the intersection. Therefore, increasingly high demands for urban traffic signal control make a variety of advanced control technology integrated, so as to adjust the intersection control parameter and accordingly achieve a dynamic coordination control over the city traffic. Hopefully, it can satisfy traffic demands and achieve a new dynamic balance between road traffic and transport demand. This paper focuses on the signal timing control over green time extension at adjacent intersections of trunk road by using the collaborative strategy of road green wave effect and path selection entropy, in order to solve the problem of coupling between adjacent intersections, so as to realize the signal coordination control on trunk road. This algorithm makes Signal Timing more flexible.

Study on Automatic Control of Urban Traffic Based on Computer Vision

2013 ◽  
Vol 760-762 ◽  
pp. 1164-1168
Author(s):  
Peng Sun ◽  
Tong Qiang Ding ◽  
Li Li Zheng
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Traffic Control ◽  
Control Method ◽  
Urban Traffic ◽  
Traffic Signal Control ◽  
Programmable Logic ◽  
Real Time Control ◽  
Time Control ◽  
Urban Traffic Control

Urban traffic control is not only a real-time control system but also random, non-linear, uncertainty complex system; the article discusses the use of PLC (programmable logic controller) control technology and fuzzy control method for traffic signal control system and how to complete the complex traffic control by using communications link technology between computers and application of fuzzy control theory, make vehicle and road navigation to realize intelligent.

Research of Traffic Signal Control Strategy Based on the Fuzzy Control

2014 ◽  
Vol 651-653 ◽  
pp. 486-490 ◽  
Author(s):  
Xue Bo Yan
Keyword(s):  
Control Strategy ◽  
Traffic Congestion ◽  
Dynamic Change ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Time Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Control Effect

To ease the traffic pressure on urban traffic signal control strategy research started. Dynamic change prediction analysis of traffic flow through the flow of information as a basis for fuzzy reasoning, automatically adjust the signal cycle, green ratio and phase control parameters, real-time signal timing to generate optimal solutions for optimal control effect. The results show that this method can effectively alleviate traffic congestion, meet the design expectations.

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 Multi-Objective Optimization of Traffic Signal Timing for Oversaturated Intersection

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Li ◽  
Lijie Yu ◽  
Siran Tao ◽  
Kuanmin Chen
Keyword(s):  
Simulation Software ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Simulation Environment ◽  
Multi Objective Optimization ◽  
Good Efficiency ◽  
Multi Objective ◽  
Traffic Signal Timing

For the purpose of improving the efficiency of traffic signal control for isolate intersection under oversaturated conditions, a multi-objective optimization algorithm for traffic signal control is proposed. Throughput maximum and average queue ratio minimum are selected as the optimization objectives of the traffic signal control under oversaturated condition. A simulation environment using VISSIM SCAPI was utilized to evaluate the convergence and the optimization results under various settings and traffic conditions. It is written by C++/CRL to connect the simulation software VISSIM and the proposed algorithm. The simulation results indicated that the signal timing plan generated by the proposed algorithm has good efficiency in managing the traffic flow at oversaturated intersection than the commonly utilized signal timing optimization software Synchro. The update frequency applied in the simulation environment was 120 s, and it can meet the requirements of signal timing plan update in real filed. Thus, the proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain under both normal condition and over-saturated condition.

Extremum-Seeking for Adaptation of Urban Traffic Signal Control

2014 ◽  
Vol 47 (3) ◽  
pp. 5067-5072 ◽  
Author(s):  
Ronny Kutadinata ◽  
Will Moase ◽  
Chris Manzie ◽  
Lele Zhang ◽  
Tim Garoni
Keyword(s):  
Traffic Signal ◽  
Urban Traffic ◽  
Extremum Seeking ◽  
Signal Control ◽  
Traffic Signal Control

Use of Big Data to Evaluate and Improve Performance of Traffic Signal Systems in Resource-Constrained Countries

2017 ◽  
Vol 2620 (1) ◽  
pp. 20-30 ◽  
Author(s):  
Yang Carl Lu ◽  
Holly Krambeck ◽  
Liang Tang
Keyword(s):  
Traffic Control ◽  
Time Of Day ◽  
Traffic Signal ◽  
Signal Timing ◽  
Gps Data ◽  
Resource Constrained ◽  
Physical Sensors ◽  
Constrained Environments ◽  
Traffic Signal Timing ◽  
High Level

Deployment of an adaptive area traffic control system is expensive; physical sensors require installation, calibration, and regular maintenance. Because of the high level of technical and financial resources required, area traffic control systems found in developing countries often are minimally functioning. In Cebu City, Philippines, for example, the Sydney Coordinated Adaptive Traffic System was installed before 2000, and fewer than 35% of detectors were still functioning as of January 2015. To address this challenge, a study was designed to determine whether taxi company GPS data are sufficient to evaluate and improve traffic signal timing plans in resource-constrained environments. If this work is successful, the number of physical sensors required to support those systems may be reduced and thereby substantially lower the costs of installation and maintenance. Taxi GPS data provided by a regional taxi-hailing app were used to design and implement methodologies for evaluating the performance of traffic signal timing plans and for deriving updated fixed-dynamic plans, which are fixed plans (with periods based on observable congestion patterns rather than only time of day) iterated regularly until optimization is reached. To date, three rounds of iterations have been conducted to ensure the stability of the proposed signal timings. Results of exploratory analysis indicate that the algorithm is capable of generating reasonable green time splits, but cycle length adjustment must be considered in the future.

scholarly journals Investigating the Applicability of Upstream Detection Strategy at Pedestrian Signalised Crossings

2017 ◽  
Vol 29 (5) ◽  
pp. 503-510 ◽  
Author(s):  
Sitti A Hassan ◽  
Nick B Hounsell ◽  
Birendra P Shrestha
Keyword(s):  
Traffic Control ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Flows ◽  
Detection Strategy ◽  
Vehicle Delay ◽  
Simulation Results ◽  
The Uk ◽  
User Friendly

In the UK, the Puffin crossing has provision to extend pedestrian green time for those who take longer to cross. However, even at such a pedestrian friendly facility, the traffic signal control is usually designed to minimise vehicle delay while providing the crossing facility. This situation is rather contrary to the current policies to encourage walking. It is this inequity that has prompted the need to re-examine the traffic control of signalised crossings to provide more benefit to both pedestrians and vehicles. In this context, this paper explores the possibility of implementing an Upstream Detection strategy at a Puffin crossing to provide a user friendly crossing. The study has been carried out by simulating a mid-block Puffin crossing for various detector distances and a number of combinations of pedestrian and traffic flows. This paper presents the simulation results and recommends the situations at which Upstream Detection would be suitable.

scholarly journals Multi-Agent Reinforcement Learning for Optimizing Traffic Signal Timing

2021 ◽  
Author(s):  
Areej Salaymeh ◽  
Loren Schwiebert ◽  
Stephen Remias
Keyword(s):  
Reinforcement Learning ◽  
Traffic Signals ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Timing ◽  
Model Free ◽  
Proposed Model ◽  
Multi Agent ◽  
Traffic Signal Timing

Designing efficient transportation systems is crucial to save time and money for drivers and for the economy as whole. One of the most important components of traffic systems are traffic signals. Currently, most traffic signal systems are configured using fixed timing plans, which are based on limited vehicle count data. Past research has introduced and designed intelligent traffic signals; however, machine learning and deep learning have only recently been used in systems that aim to optimize the timing of traffic signals in order to reduce travel time. A very promising field in Artificial Intelligence is Reinforcement Learning. Reinforcement learning (RL) is a data driven method that has shown promising results in optimizing traffic signal timing plans to reduce traffic congestion. However, model-based and centralized methods are impractical here due to the high dimensional state-action space in complex urban traffic network. In this paper, a model-free approach is used to optimize signal timing for complicated multiple four-phase signalized intersections. We propose a multi-agent deep reinforcement learning framework that aims to optimize traffic flow using data within traffic signal intersections and data coming from other intersections in a Multi-Agent Environment in what is called Multi-Agent Reinforcement Learning (MARL). The proposed model consists of state-of-art techniques such as Double Deep Q-Network and Hindsight Experience Replay (HER). This research uses HER to allow our framework to quickly learn on sparse reward settings. We tested and evaluated our proposed model via a Simulation of Urban MObility simulation (SUMO). Our results show that the proposed method is effective in reducing congestion in both peak and off-peak times.

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