Development And Evaluation Of A Multi Agent Based Neuro Fuzzy Arterial Traffic Signal Control System
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Development And Evaluation Of A Multi Agent Based Neuro Fuzzy Arterial Traffic Signal Control System
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Author : Yunlong Zhang
language : en
Publisher:
Release Date : 2007
Development And Evaluation Of A Multi Agent Based Neuro Fuzzy Arterial Traffic Signal Control System written by Yunlong Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Electronic traffic controls categories.
Arterial traffic signal control is a very important aspect of traffic management system. Efficient arterial traffic signal control strategy can reduce delay, stops, congestion, and pollution and save travel time. Commonly used pre-timed or traffic actuated signal control do not have the capability to fully respond to real-time traffic demand and pattern changes. Although some of the well-known adaptive control systems have shown advantageous over the traditional per-timed and actuated control strategies, their centralized architecture makes the maintenance, expansion, and upgrade difficult and costly.
International Aerospace Abstracts
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Author :
language : en
Publisher:
Release Date : 1998
International Aerospace Abstracts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Aeronautics categories.
Conference Papers Index
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Author :
language : en
Publisher:
Release Date : 1987
Conference Papers Index written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Engineering categories.
Documentation Abstracts
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Author :
language : en
Publisher:
Release Date : 1992
Documentation Abstracts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Documentation categories.
Development And Evaluation Of A Multi Agent Approach To Traffic Signal Control Using Traffic Simulation
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Author : Suphasawas Nigarnjanagool
language : en
Publisher:
Release Date : 2007
Development And Evaluation Of A Multi Agent Approach To Traffic Signal Control Using Traffic Simulation written by Suphasawas Nigarnjanagool and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Traffic signs and signals categories.
Development And Evaluation Of An Arterial Adaptive Traffic Signal Control System Using Reinforcement Learning
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Author : Yuanchang Xie
language : en
Publisher:
Release Date : 2010
Development And Evaluation Of An Arterial Adaptive Traffic Signal Control System Using Reinforcement Learning written by Yuanchang Xie and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
This dissertation develops and evaluates a new adaptive traffic signal control system for arterials. This control system is based on reinforcement learning, which is an important research area in distributed artificial intelligence and has been extensively used in many applications including real-time control. In this dissertation, a systematic comparison between the reinforcement learning control methods and existing adaptive traffic control methods is first presented from the theoretical perspective. This comparison shows both the connections between them and the benefits of using reinforcement learning. A Neural-Fuzzy Actor-Critic Reinforcement Learning (NFACRL) method is then introduced for traffic signal control. NFACRL integrates fuzzy logic and neural networks into reinforcement learning and can better handle the curse of dimensionality and generalization problems associated with ordinary reinforcement learning methods. This NFACRL method is first applied to isolated intersection control. Two different implementation schemes are considered. The first scheme uses a fixed phase sequence and variable cycle length, while the second one optimizes phase sequence in real time and is not constrained to the concept of cycle. Both schemes are further extended for arterial control, with each intersection being controlled by one NFACRL controller. Different strategies used for coordinating reinforcement learning controllers are reviewed, and a simple but robust method is adopted for coordinating traffic signals along the arterial. The proposed NFACRL control system is tested at both isolated intersection and arterial levels based on VISSIM simulation. The testing is conducted under different traffic volume scenarios using real-world traffic data collected during morning, noon, and afternoon peak periods. The performance of the NFACRL control system is compared with that of the optimized pre-timed and actuated control. Testing results based on VISSIM simulation show that the proposed NFACRL control has very promising performance. It outperforms optimized pre-timed and actuated control in most cases for both isolated intersection and arterial control. At the end of this dissertation, issues on how to further improve the NFACRL method and implement it in real world are discussed.
Multi Agent Reinforcement Learning For Integrated Network Of Adaptive Traffic Signal Controllers Marlin Atsc
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Author : Samah El-Tantawy
language : en
Publisher:
Release Date : 2012
Multi Agent Reinforcement Learning For Integrated Network Of Adaptive Traffic Signal Controllers Marlin Atsc written by Samah El-Tantawy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
A Two Stage Interval Valued Neutrosophic Soft Set Traffic Signal Control Model For Four Way Isolated Signalized Intersections
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Author : Endalkachew Teshome Ayele
language : en
Publisher: Infinite Study
Release Date : 2020-12-01
A Two Stage Interval Valued Neutrosophic Soft Set Traffic Signal Control Model For Four Way Isolated Signalized Intersections written by Endalkachew Teshome Ayele and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-01 with Mathematics categories.
One of the major problems of both developed and developing countries is traffic congestion in urban road transportation systems. Some of the adverse consequences of traffic congestion are loss of productive time, delay in transportation,increase in transportation cost,excess fuel consumption, safety of people,increase in air pollution level and disruption of day-to-day activities. Researches have shown that among others, traditional traffic control system is one of the main reasons for traffic congestion at traffic junctions. Most countries through out the world use pre-timed / fixed cycle time traffic control systems. But these traffic control systems do not give an optimal signal time setting as they do not take into account the time dependent heavy traffic conditions at the junctions. They merely use a predetermined sequence or order for both signal phase change and time setting. Some times this also leads to more congestion at the junctions. As an improvement of fixed time traffic control method, fuzzy logic traffic control model was developed which takes into account the current traffic conditions at the junctions and works based on fuzzy logic principle under imprecise and uncertain conditions. But as a real life situation,in addition to uncertainty and impreciseness there is also indeterminacy in traffic signal control constraints which fuzzy logic can not handle. The aim of this research is to develop a new traffic signal control model that can solve the limitations of fixed time signal control and fuzzy logic signal control using a flexible approach based on interval-valued neutrosophic soft set and its decision making technique, specially developed for this purpose.We have developed an algorithm for controlling both phase change and green time extension / termination as warranted by the traffic conditions prevailing at any time.
Multi Agent Look Ahead Traffic Adaptive Control
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Author : Ronald Theodoor Katwijk
language : en
Publisher:
Release Date : 2008
Multi Agent Look Ahead Traffic Adaptive Control written by Ronald Theodoor Katwijk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Adaptive control systems categories.
Intelligent Real Time Decision Support Systems For Road Traffic Management
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Author : Khaled A. Almejalli
language : en
Publisher:
Release Date : 2010
Intelligent Real Time Decision Support Systems For Road Traffic Management written by Khaled A. Almejalli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.