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Author: Siew Mun Leong
Publisher:
ISBN:
Category :
Languages : en
Pages : 190
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Author: Siew Mun Leong
Publisher:
ISBN:
Category :
Languages : en
Pages : 190
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Author:
Publisher:
ISBN:
Category : Adaptive control systems
Languages : en
Pages : 256
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Author: Jonathan Corey
Publisher:
ISBN:
Category :
Languages : en
Pages : 192
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Book Description
With over 300,000 traffic signals in the United States, it is important to everyone that those traffic signals operate optimally. Unfortunately, according to the Institute of Transportation Engineers over 75% of traffic signal control systems are in need of retiming or upgrade. Agencies and practitioners responsible for these signals face significant budgeting and procedural challenges to maintain and upgrade their systems. Transportation professionals have traditionally lacked accessible and effective tools to identify when and where the greatest benefits may be generated through retiming and system feature selection. They have also lacked methods and tools to identify, select and defend choices of new traffic signal control systems. This is especially true for adaptive traffic signal control systems which are generally more expensive and whose adaptive algorithms are proprietary, invalidating many traditional analysis methods. To address these challenges, a new theoretical framework including queuing and traffic signal control models has been developed in this study to predict the impacts of signal control technology on a given corridor. This framework has been implemented in the STAR Lab Toolkit for Analysis of Traffic and Intersection Control Systems (STATICS) that uses an underlying queuing model interacting with simulated traffic signal control logic to develop traffic measures of effectiveness under different traffic signal control strategies and settings. The STATICS toolkit has been employed by the Oregon Department of Transportation and several other transportation agencies to analyze their corridors and select advanced traffic signal control systems. Furthermore, a new cost-effective adaptive traffic signal control system called the Swarm-Intelligence Based Adaptive Signal System (SIBASS) is proposed to address situations where optimum optimization strategies change with traffic conditions. Compared to the existing adaptive signal control systems, SIBASS carries an important advantage that makes it robust under communication difficulties. It operates at the individual intersection level in a flat hierarchy that does not use a central controller. Instead, each intersection self-assigns a role based on current traffic conditions and the current roles of neighboring intersections. Each role uses different optimization goals, allowing SIBASS to change intersection optimization criteria based on the current role chosen by that intersection. By designing cooperative features into SIBASS it is possible to create corridor coordination and optimization. This is accomplished using the characteristics of the swarm rather than external imposition to create order. SIBASS is evaluated via simulation under varied traffic conditions. SIBASS consistently outperformed the existing systems tested in this study. On average, SIBASS reduced system average per vehicle delay by approximately 3.5 seconds and system average queue lengths by 20 feet in the tested scenarios. New approaches to tailoring traffic signal control optimization strategies to current traffic conditions and desired operational goals are enabled by SIBASS. Combined, STATICS and SIBASS offer a solid basis upon which to build future tools and methods to analyze traffic signal control systems. Future STATICS analytical modules may include estimating environmental performance and costs as well as improvements to pedestrian modeling and mobility analysis. Environmental and pedestrian considerations also present opportunities for improvement of SIBASS. New optimization roles can be created for SIBASS to address environmental and pedestrian optimization issues.
Author: Gang Liu
Publisher:
ISBN:
Category : Traffic flow
Languages : en
Pages : 139
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Book Description
Under congested conditions, the road traffic states of different arterial links will interact with each other; therefore, it is necessary to understand the behavior of traffic corridors and to investigate corridor-wide traffic coordinated control strategies. In order to achieve this, traffic flow models are applied in signal control to predict future traffic states. Optimization tools are used to search for the best sequence of future control decisions, based on predictions by traffic flow models. A number of model-based adaptive control strategies have been presented in the literature and have been proved effective in practice. However, most studies have modeled the traffic dynamic either at a link-based level or at an individual movement-based level. Moreover, the efficiency of corridor-wide coordination algorithms for congested large-scale networks still needs to be further improved. A hierarchical control structure is developed to divide the complex control problem into different control layers: the highest level optimizes the cycle length, the mid layer optimizes the offsets, and the Model Predictive Control (MPC) procedure is implemented in the lowest layer to optimize the split. In addition, there is an extra multi-modal priority control layer to provide priority for different travel modes. Firstly, MPC is applied to optimize the signal timing plans for arterial traffic. The objectives are to increase the throughput. A hybrid urban traffic flow model is proposed to provide relatively accurate predictions of the traffic state dynamic, which is capable of simulating queue evolutions among different lane groups in a specific link. Secondly, this study expands the dynamic queue concept to the corridor-wide coordination problem. The ideal offset and boundary offsets to avoid spillback and starvation are found based on the shockwave profiles at each signalized intersection. A new multi-objective optimization model based on the preemptive goal programming is proposed to find the optimal offset. Thirdly, the priority control problem is formulated into a multi-objective optimization model, which is solved with a Non-dominated Sorting Genetic Algorithm. Pareto-optimal front results are presented to evaluate the trade-off among different objectives and the most appropriate solution is chosen with high-level information. Performance of the new adaptive controller is verified with software-in-the-loop simulation. The applied simulation environment contains VISSIM with the ASC/3 module as the simulation environment and the control system as the solver. The simulation test bed includes two arterial corridors in Edmonton, Alberta. The simulation network was well calibrated and validated. The simulation results show that the proposed adaptive control methods outperform actuated control in increasing throughput, decreasing delay, and preventing queue spillback.
Author: Paul Brian Wolshon
Publisher:
ISBN:
Category : Electronic traffic controls
Languages : en
Pages : 292
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 148
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 168
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Author: Michael P. Hunter
Publisher:
ISBN:
Category : Electronic traffic controls
Languages : en
Pages : 282
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 174
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Author: Chalam
Publisher: Routledge
ISBN: 1351468987
Category : Technology & Engineering
Languages : en
Pages : 544
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Book Description
impossible to access. It has been widely scattered in papers, reports, and proceedings ofsymposia, with different authors employing different symbols and terms. But now thereis a book that covers all aspects of this dynamic topic in a systematic manner.Featuring consistent terminology and compatible notation, and emphasizing unifiedstrategies, Adaptive Control Systems provides a comprehensive, integrated accountof basic concepts, analytical tools, algorithms, and a wide variety of application trendsand techniques.Adaptive Control Systems deals not only with the two principal approachesmodelreference adaptive control and self-tuning regulators-but also considers otheradaptive strategies involving variable structure systems, reduced order schemes, predictivecontrol, fuzzy logic, and more. In addition, it highlights a large number of practical applicationsin a range of fields from electrical to biomedical and aerospace engineering ...and includes coverage of industrial robots.The book identifies current trends in the development of adaptive control systems ...delineates areas for further research . : . and provides an invaluable bibliography of over1,200 references to the literature.The first authoritative reference in this important area of work, Adaptive ControlSystems is an essential information source for electrical and electronics, R&D,chemical, mechanical, aerospace, biomedical, metallurgical, marine, transportation, andpower plant engineers. It is also useful as a text in professional society seminars and inhousetraining programs for personnel involved with the control of complex systems, andfor graduate students engaged in the study of adaptive control systems.
