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Adaptive Traffic Signal Control System Acs Lite For Wolf Road New York
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Book Synopsis Adaptive Traffic Signal Control System (ACS-LITE) for Wolf Road, New York by : Xuegang Ban
Download or read book Adaptive Traffic Signal Control System (ACS-LITE) for Wolf Road, New York written by Xuegang Ban and published by . This book was released on 2014 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive Control Software Lite (ACS-Lite) is a traffic signal timing optimization system that dynamically adjusts traffic signal timings to meet current traffic demands. The purpose of this research project was to deploy and evaluate the ACS-Lite adaptive traffic control system on a congested urban corridor in New York State (NYS). In this case, the Wolf Road Corridor in Albany, New York, was chosen. The primary goal was to document the experiences and key lessons learned from the deployment and evaluation regarding how an adaptive control system can be deployed, the advantages and disadvantages of the system, and whether it is suitable for use in other corridors in NYS. The results of the project showed that for heavily congested corridors adaptive control can improve flow within its own system, but may cause extra delays at the boundaries where there are interactions with other traffic control systems. Therefore, a more comprehensive control/management framework may be needed in some cases. The specific ACS-Lite software also needed to be upgraded and improved in order to work for the selected corridor, which caused delays to this project.
Book Synopsis Adaptive Signal Control Gradually Emerging as a New Way to Decrease Costs Associated with Delays, Stops and Fuel Consumption by : Kenneth A. Winter
Download or read book Adaptive Signal Control Gradually Emerging as a New Way to Decrease Costs Associated with Delays, Stops and Fuel Consumption written by Kenneth A. Winter and published by . This book was released on 2007 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the 1960s time-of-day/day-of-week strategy has been used to select traffic control system timing plans. More recently, adaptive control systems (ACSs) have been developed. In these systems, signal timing parameters are generated in real-time based on detector measurements. ACS strategies can be applied at a single location, along arterial routes, or in grid networks. Decisions about timing plans are either made at a central location or at the local intersections, or both. Florida, Minnesota and Wisconsin DOTs are experimenting with adaptive signal control to determine whether the benefits of ACS justify their cost (estimated at $10,000-$40,000 per intersection). In 2001 FHWA developed a low-cost adaptive traffic signal timing system (ACS-Lite) designed to take advantage of the closed loop architecture estimated to comprise 90% of the traffic signal systems in the U.S. FHWA's Arterial Management Program Web Site can provide practitioners with guidance, recommended practices, manuals and other technical resources to help improve their knowledge of traffic signal timing processes is also crucial in enhancing the state-of-the-practice. In addition to the specific citations listed in this RSB, the FHWA site may be the best starting place to learn more about how practitioners are using ACS in the United States. For more information, see: http://www.ops.fhwa.dot.gov/arterial_mgmt/pubs.htm.
Author :Aleksandar Stevanovic Publisher :Transportation Research Board National Research ISBN 13 : Total Pages :116 pages Book Rating :4.3/5 (555 download)
Book Synopsis Adaptive Traffic Control Systems by : Aleksandar Stevanovic
Download or read book Adaptive Traffic Control Systems written by Aleksandar Stevanovic and published by Transportation Research Board National Research. This book was released on 2010 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB's National Cooperative Highway Research Program (NCHRP) Synthesis 403: Adaptive Traffic Control Systems: Domestic and Foreign State of Practice explores the state of practice of adaptive traffic control systems (ATCSs), also known as real-time traffic control systems, which adjust, in real time, signal timings based on traffic conditions, demand, and system capacity --
Book Synopsis Adaptive Traffic Signal Control for Tarrytown Road in White Plains, New York by : New York State Energy Research and Development Authority
Download or read book Adaptive Traffic Signal Control for Tarrytown Road in White Plains, New York written by New York State Energy Research and Development Authority and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Adaptive Traffic Signal Control System by : Chih-Chiang Su
Download or read book An Adaptive Traffic Signal Control System written by Chih-Chiang Su and published by . This book was released on 1994 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Development of a Platoon-based Adaptive Traffic Signal Control System by : Yi Jiang (Writer on engineering)
Download or read book Development of a Platoon-based Adaptive Traffic Signal Control System written by Yi Jiang (Writer on engineering) and published by . This book was released on 1999 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Foundation for Self-tuning Closed-loop Progressive System by :
Download or read book Foundation for Self-tuning Closed-loop Progressive System written by and published by . This book was released on 2004 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Guidelines for the Evaluation of Adaptive Traffic Signal Control Systems by :
Download or read book Guidelines for the Evaluation of Adaptive Traffic Signal Control Systems written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Connected Eco-driving Technologies for Adaptive Traffic Signal Control by :
Download or read book Connected Eco-driving Technologies for Adaptive Traffic Signal Control written by and published by . This book was released on 2019 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adaptive Signal Control, IV by : Peter T. Martin
Download or read book Adaptive Signal Control, IV written by Peter T. Martin and published by . This book was released on 2006 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this project is to deploy and evaluate the effectiveness of the future Utah Department of Transportation (UDOT) Adaptive Traffic Control System (ATCS) on an arterial street network in Park City, Utah that experiences both everyday and unpredictable changes in traffic flow.
Book Synopsis Priority, Market-ready Technologies and Innovations by :
Download or read book Priority, Market-ready Technologies and Innovations written by and published by . This book was released on 2008 with total page 2 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Adaptive Signal Control II written by and published by . This book was released on 2003 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report is a comprehensive investigation of adaptive signal control. Traffic signal systems that respond in real-time to changes in traffic patterns are known as "Adaptive." An investigation of the effectiveness of adaptive signal control through simulation and modeling concludes that adaptive control brings immediate delay reduction and improved traffic control
Book Synopsis Simulation-based Decision-making Tool for Adaptive Traffic Signal Control on Tarrytown Road in the City of White Plains by : Joseph D. Tario
Download or read book Simulation-based Decision-making Tool for Adaptive Traffic Signal Control on Tarrytown Road in the City of White Plains written by Joseph D. Tario and published by . This book was released on 2013 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Field Evaluation of InSync Adaptive Traffic Signal Control System in Multiple Environments Using Multiple Approaches by : Md Shafikul Islam Shafik
Download or read book Field Evaluation of InSync Adaptive Traffic Signal Control System in Multiple Environments Using Multiple Approaches written by Md Shafikul Islam Shafik and published by . This book was released on 2017 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: Adaptive Traffic Control System (ATCS), Field Evaluation, Before-after Study, Average-car Method, InSync, BlueMAC, Travel Time, Delay.
Book Synopsis Measuring the Benefits of Adaptive Traffic Signal Control by : Ali Goudarz Eghtedari
Download or read book Measuring the Benefits of Adaptive Traffic Signal Control written by Ali Goudarz Eghtedari and published by . This book was released on 2005 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Flow-based Adaptive Split Signal Control by : Airton Gustavo Kohls
Download or read book Flow-based Adaptive Split Signal Control written by Airton Gustavo Kohls and published by . This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last 35 years many adaptive traffic signal control systems have been developed presenting alternative strategies to improve traffic signal operations. However, less than 1% of all traffic signals in the United States are controlled by adaptive systems today. The extensive infrastructure necessary including reliable communication and complex calibration leads to a time consuming and costly process. In addition, the most recent National Traffic Signal Report Card indicated an overall grade of D for the nation's traffic signal control and operations. Recent economic adversity adds to the already difficult task of proactively managing aged signal timing plans. Therefore, in an attempt to escape the status quo, a flow based adaptive split signal control model is presented, having the principal objective of updating the split table based solely on real-time traffic conditions and without disrupting coordination. Considering the available typical traffic signal control infrastructure in cities today, a non centralized system is proposed, directed to the improvement of National Electrical Manufacturers Association (NEMA) based systems that are compliant with the National Transportation Communications for Intelligent Transportation System Protocol (NTCIP) standards. The approach encompasses the User Datagram Protocol (UDP) for system communication allowing an external agent to gather flow information directly from a traffic signal controller detector status and use it to better allocation of phase splits. The flow based adaptive split signal control was not able to consistently yield significant lower average vehicle delay than a full actuated signal controller when evaluated on an intersection operating a coordinated timing plan. However, the research proposes the ability of an external agent to seamless control a traffic signal controller using real-time data, suggesting the encouraging results of this research can be improved upon.
Book Synopsis Data-driven Adaptive Traffic Signal Control Via Deep Reinforcement Learning by : Tian Tan
Download or read book Data-driven Adaptive Traffic Signal Control Via Deep Reinforcement Learning written by Tian Tan and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive traffic signal control (ATSC) system serves a significant role for relieving urban traffic congestion. The system is capable of adjusting signal phases and timings of all traffic lights simultaneously according to real-time traffic sensor data, resulting in a better overall traffic management and an improved traffic condition on road. In recent years, deep reinforcement learning (DRL), one powerful paradigm in artificial intelligence (AI) for sequential decision-making, has drawn great attention from transportation researchers. The following three properties of DRL make it very attractive and ideal for the next generation ATSC system: (1) model-free: DRL reasons about the optimal control strategies directly from data without making additional assumptions on the underlying traffic distributions and traffic flows. Compared with traditional traffic optimization methods, DRL avoids the cumbersome formulation of traffic dynamics and modeling; (2) self-learning: DRL self-learns the signal control knowledge from traffic data with minimal human expertise; (3) simple data requirement: by using large nonlinear neural networks as function approximators, DRL has enough representation power to map directly from simple traffic measurements, e.g. queue length and waiting time, to signal control policies. This thesis focuses on building data-driven and adaptive controllers via deep reinforcement learning for large-scale traffic signal control systems. In particular, the thesis first proposes a hierarchical decentralized-to-centralized DRL framework for large-scale ATSC to better coordinate multiple signalized intersections in the traffic system. Second, the thesis introduces efficient DRL with efficient exploration for ATSC to greatly improve sample complexity of DRL algorithms, making them more suitable for real-world control systems. Furthermore, the thesis combines multi-agent system with efficient DRL to solve large-scale ATSC problems that have multiple intersections. Finally, the thesis presents several algorithmic extensions to handle complex topology and heterogeneous intersections in real-world traffic networks. To gauge the performance of the presented DRL algorithms, various experiments have been conducted and included in the thesis both on small-scale and on large-scale simulated traffic networks. The empirical results have demonstrated that the proposed DRL algorithms outperform both rule-based control policy and commonly-used off-the-shelf DRL algorithms by a significant margin. Moreover, the proposed efficient MARL algorithms have achieved the state-of-the-art performance with improved sample-complexity for large-scale ATSC.
