Traffic control through fuzzy logic and neural networks

Traffic control through fuzzy logic and neural networks

Author:

Publisher:

Published: 2002

Total Pages:

ISBN-13:

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Este trabalho apresenta a utilização de lógica fuzzy e de redes neurais no desenvolvimento de um controlador de semáforos o FUNNCON. O trabalho realizado consiste em quatro etapas principais: estudo dos fundamentos de engenharia de tráfego; definição de uma metodologia para a avaliação de cruzamentos sinalizados; definição domodelo do controlador proposto; e implementação com dados reais em um estudo de caso. O estudo sobre os fundamentos de engenharia de tráfego aborda a definição de termos, os parâmetros utilizados na descrição dos fluxos de tráfego, os tipos de cruzamentos e seus semáforos, os sistemas de controle de tráfego mais utilizados e as diversas medidas dedesempenho. Para se efetuar a análise dos resultados do FUNNCON, é definida uma metodologia para a avaliação de controladores. Apresenta-se, também, uma investigação sobre simuladores de tráfego existentes, de modo a permitir a escolha do mais adequado para o presente estudo. A definição do modelo do FUNNCON compreende uma descrição geral dos diversos módulos que o compõem. Em seguida, cada um destes módulos é estudado separadamente: o uso de redes neurais para a predição de tráfego futuro; a elaboração de um banco de cenários ótimos através de um otimizador; e a criação de regras fuzzy a partir deste banco. No estudo de caso, o FUNNCON é implementado com dados reais fornecidos pela CET-Rio em um cruzamento do Rio de Janeiro e comparado com o controlador existente. É constatado que redes neurais são capazes de fornecer bons resultados na predição dotráfego futuro. Também pode ser observado que as regras fuzzy criadas a partir do banco de cenários ótimos proporcionam um controle efetivo do tráfego no cruzamento estudado. Uma comparação entre o desempenho do FUNNCON e o do sistema atualmente em operação é amplamente favorável ao primeiro.


Traffic Control and Transport Planning:

Traffic Control and Transport Planning:

Author: Dusan Teodorovic

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 401

ISBN-13: 9401144036

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When solving real-life engineering problems, linguistic information is often encountered that is frequently hard to quantify using "classical" mathematical techniques. This linguistic information represents subjective knowledge. Through the assumptions made by the analyst when forming the mathematical model, the linguistic information is often ignored. On the other hand, a wide range of traffic and transportation engineering parameters are characterized by uncertainty, subjectivity, imprecision, and ambiguity. Human operators, dispatchers, drivers, and passengers use this subjective knowledge or linguistic information on a daily basis when making decisions. Decisions about route choice, mode of transportation, most suitable departure time, or dispatching trucks are made by drivers, passengers, or dispatchers. In each case the decision maker is a human. The environment in which a human expert (human controller) makes decisions is most often complex, making it difficult to formulate a suitable mathematical model. Thus, the development of fuzzy logic systems seems justified in such situations. In certain situations we accept linguistic information much more easily than numerical information. In the same vein, we are perfectly capable of accepting approximate numerical values and making decisions based on them. In a great number of cases we use approximate numerical values exclusively. It should be emphasized that the subjective estimates of different traffic parameters differs from dispatcher to dispatcher, driver to driver, and passenger to passenger.


Fuzzy Logic Control

Fuzzy Logic Control

Author: H. B. Verbruggen

Publisher: World Scientific

Published: 1999

Total Pages: 344

ISBN-13: 9789810238254

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Fuzzy logic control has become an important methodology in control engineering. This volume deals with applications of fuzzy logic control in various domains. The contributions are divided into three parts. The first part consists of two state-of-the-art tutorials on fuzzy control and fuzzy modeling. Surveys of advanced methodologies are included in the second part. These surveys address fuzzy decision making and control, fault detection, isolation and diagnosis, complexity reduction in fuzzy systems and neuro-fuzzy methods. The third part contains application-oriented contributions from various fields, such as process industry, cement and ceramics, vehicle control and traffic management, electromechanical and production systems, avionics, biotechnology and medical applications. The book is intended for researchers both from the academic world and from industry.


An Intelligent Traffic Controller

An Intelligent Traffic Controller

Author:

Publisher:

Published: 1995

Total Pages: 6

ISBN-13:

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A controller with advanced control logic can significantly improve traffic flows at intersections. In this vein, this paper explores fuzzy rules and algorithms to improve the intersection operation by rationalizing phase changes and green times. The fuzzy logic for control is enhanced by the exploration of neural networks for families of membership functions and for ideal cost functions. The concepts of fuzzy logic control are carried forth into the controller architecture. Finally, the architecture and the modules are discussed. In essence, the control logic and architecture of an intelligent controller are explored.


Fuzzy Set Theory

Fuzzy Set Theory

Author: George J. Klir

Publisher:

Published: 1997

Total Pages: 264

ISBN-13:

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Fuzzy Set Theory: Foundations and Applications serves as a simple introduction to basic elements of fuzzy set theory. The emphasis is on a conceptual rather than a theoretical presentation of the material. Fuzzy Set Theory also contains an overview of the corresponding elements of classical set theory - including basic ideas of classical relations - as well as an overview of classical logic. Because the inclusion of background material in these classical foundations provides a self-contained course of study, students from many different academic backgrounds will have access to this important new theory.


Applications of Fuzzy Logic

Applications of Fuzzy Logic

Author: Mohammad Jamshidi

Publisher: Prentice Hall

Published: 1997

Total Pages: 458

ISBN-13:

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Every year, Captain Kangaroo sets the contestants on their way in the great balloon race. All the animals are there the platypus, the wombats, the koalas and the emus are all there. But when the contestants bump into a dust cloud, Captain Kangaroo has to step in to steer them back on course. Which of his emergency aircraft will he choose? And can you find the animals who have stowed away inside each basket?


Network Traffic Control Based on Modern Control Techniques

Network Traffic Control Based on Modern Control Techniques

Author: Jungang Liu

Publisher:

Published: 2014

Total Pages:

ISBN-13:

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This thesis presents two modern control methods to address the Internet traffic congestion control issues. They are based on a distributed traffic management framework for the fast-growing Internet traffic in which routers are deployed with intelligent or optimal data rate controllers to tackle the traffic mass. The first one is called the IntelRate (Intelligent Rate) controller using the fuzzy logic theory. Unlike other explicit traffic control protocols that have to estimate network parameters (e.g., link latency, bottleneck bandwidth, packet loss rate, or the number of flows), our fuzzy-logic-based explicit controller can measure the router queue size directly. Hence it avoids various potential performance problems arising from parameter estimations while reducing much computation and memory consumption in the routers. The communication QoS (Quality of Service) is assured by the good performances of our scheme such as max-min fairness, low queueing delay and good robustness to network dynamics. Using the Lyapunov's Direct Method, this controller is proved to be globally asymptotically stable. The other one is called the OFEX (Optimal and Fully EXplicit) controller using convex optimization. This new scheme is able to provide not only optimal bandwidth allocation but also fully explicit congestion signal to sources. It uses the congestion signal from the most congested link, instead of the cumulative signal from a flow path. In this way, it overcomes the drawback of the relatively explicit controllers that bias the multi-bottlenecked users, and significantly improves their convergence speed and throughput performance. Furthermore, the OFEX controller design considers a dynamic model by proposing a remedial measure against the unpredictable bandwidth changes in contention-based multi-access networks (such as shared Ethernet or IEEE 802.11). When compared with the former works/controllers, such a remedy also effectively reduces the instantaneous queue size in a router, and thus significantly improving the queueing delay and packet loss performance. Finally, the applications of these two controllers on wireless local area networks have been investigated. Their design guidelines/limits are also provided based on our experiences.


Development and Evaluation of a Multi-agent Based Neuro-fuzzy Arterial Traffic Signal Control System

Development and Evaluation of a Multi-agent Based Neuro-fuzzy Arterial Traffic Signal Control System

Author: Yunlong Zhang

Publisher:

Published: 2007

Total Pages: 126

ISBN-13:

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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.


Fuzzy Logic Traffic Signal Controller Enhancement Based on Aggressive Driver Behavior Classification

Fuzzy Logic Traffic Signal Controller Enhancement Based on Aggressive Driver Behavior Classification

Author: Shaimaa Mohammed Hegazy

Publisher:

Published: 2017

Total Pages: 206

ISBN-13:

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Abstract: The rise in population worldwide and especially in Egypt, together with the increase in the number of vehicles present serious complications regarding traffic congestion and road safety. The elementary solution towards improving congestion is to expand road capacities by building new lanes. This, however, requires time and effort and therefore new methodologies are being implemented. Intelligent transportation systems (ITS) try to approach traffic congestion through the application of computational and engineering techniques. Traffic signal control is a branch of intelligent transportation systems which focuses on improving traffic signal conditions. A traffic signal controllers’ main objective is to improve this assignment in a way which reduces delays. This research proposes a new approach to enhancing traffic signal control and reducing delays of a single intersection, through the integration of an aggressive driving behavior classifier. Previous approaches dealt with traffic control and driver behavior separately, and therefore their successful integration is a new challenging area in the field. Multiple experiment sets were conducted to provide an indication to the effectiveness of our approach. Firstly, an aggressive driver behavior classifier using feed-forward neural network was successfully built utilizing Virginia Tech 100-car naturalistic driving study data. Its performance was compared against long short-term memory recurrent neural networks and support vector machines, and it resulted in better performance as shown by the area under the curve. To the best of our knowledge, this classifier is the first of its kind to be built on this 100-car study data. Secondly, a representation of aggressive driving behavior was constructed in the simulated environment, based on real life data and statistics. Finally, Mamdani’s fuzzy logic controller was modified to accommodate for the integration of the aggressive behavior classifier. The integration results were encouraging and yielded significant improvements at higher traffic flow volumes when compared against the built Mamdani’s controller. The results are promising and provide an initial step towards the integration of driver behavior classification and traffic signal control.