scholarly journals Signal Control Strategies of Tramcar Priority Based on Coordinate Control

2015 ◽  
Vol 04 (02) ◽  
pp. 13-17
Author(s):  
春晖 郑
Keyword(s):  
Control Strategies ◽  
Signal Control ◽  
Coordinate Control

scholarly journals ESTIMATING THE EFFECTS OF PUBLIC TRANSPORT PRIORITY MEASURES AT SIGNAL CONTROLLED INTERSECTIONS

2017 ◽  
Vol 12 (3) ◽  
pp. 187-192
Author(s):  
Vytautas Dumbliauskas ◽  
Vytautas Grigonis ◽  
Jūratė Vitkienė
Keyword(s):  
Public Transport ◽  
Control Strategies ◽  
Adverse Impact ◽  
Signal Control ◽  
Simulation Environment ◽  
Graphical Programming ◽  
Traffic Delays ◽  
The World ◽  
Common Signal ◽  
Programming Interface

Most of the Lithuanian cities try to achieve public transport priority by the provision of special dedicated public transport lanes. However, the possible priority measures at signalised intersections receive less attention. This paper explains common signal control strategies applied at isolated intersections in the cities around the world and estimated their effects on the travel times of both, public transport and general traffic. The analysis employs well recognised PTV VISSIM simulation environment and VisVAP graphical programming interface to implement and test priority measures under question. The results indicate that priority actions significantly reduce public transport delays up to 60%, without high adverse impact on general traffic delays.

Coordinate Signal Control in Urban Traffic of Two-Direction Green Wave Based on Genetic BP Neural Network

2013 ◽  
Vol 823 ◽  
pp. 665-668 ◽  
Author(s):  
Shao Jiao Lv ◽  
Chun Gui Li ◽  
Zhe Ming Li ◽  
Qing Kai Zang
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Coordinate Control ◽  
Analytical Algorithm ◽  
Trunk Road

To maximize the bandwidth of green wave of trunk road is a main issue in the research of signal control in urban traffic. However, the traditional analytical algorithmcan not be applied in actual traffic widely. A novel dynamic two-direction green wave coordinate control strategy was proposed to overcome the problem. By combining the genetic BP neural network with the traditional analytical algorithm, the urban traffic of two-direction was controlled coordinately online. Finally, an actual example was presented. It shows that not only the green wave bandwidth, the phase difference of each intersection and the critical cycle of trunk road were optimized according to real-time traffic flow, but also our algorithm can be used in different traffic condition by adjusting the parameters of the model.

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.

Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

2016 ◽  
Vol 27 (04) ◽  
pp. 1650045 ◽  
Author(s):  
Fei Yan ◽  
Fuli Tian ◽  
Zhongke Shi
Keyword(s):  
Control Strategies ◽  
High Capacity ◽  
Road Networks ◽  
Iterative Learning ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Fundamental Diagram ◽  
Urban Road ◽  
Modeling Uncertainties

Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

scholarly journals Evaluation of Reinforcement Learning Traffic Signalling Strategies for Alternative Objectives: Implementation in the Network of Nicosia, Cyprus

2020 ◽  
Vol 21 (4) ◽  
pp. 295-302
Author(s):  
Haris Ballis ◽  
Loukas Dimitriou
Keyword(s):  
Reinforcement Learning ◽  
Environmental Sustainability ◽  
Urban Areas ◽  
Traffic Management ◽  
Large Scale ◽  
Smart Cities ◽  
Control Strategies ◽  
Urban Traffic ◽  
Signal Control ◽  
Urban Network

AbstractSmart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.

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