Fuzzy Cellular Automata Model for Signalized Intersections

Chai, Chunlei; Wong, Yiik Diew

doi:10.1111/mice.12181

Cited by 50 publications

(23 citation statements)

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“…Fuzzy clustering is a class of algorithms for cluster analysis in which the assigning of objects to groups is not 'hard' but 'fuzzy' in the same sense as fuzzy logic (Siddique and Adeli, 2013;Chai and Wong, 2015;Hsu, 2015;PonzTienda et al, 2015). This approach allows features to be assigned into more than one cluster with different membership degrees.…”

Section: Fuzzy Clusteringmentioning

confidence: 99%

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Mirzaei

Adeli

2016

Reviews in the Neurosciences

114

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AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

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Section: Fuzzy Clusteringmentioning

confidence: 99%

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Mirzaei

Adeli

2016

Reviews in the Neurosciences

114

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“…For example, Horni et al (2013) used CA model to conduct parking simulation, and they incorporated agent-based techniques as well [13]. Chai et al (2015) made use of CA model to simulate traffic streams at signalized intersections. The thing incorporated is fuzzy logic, and results show the model can well replicate decision-making processes [14].…”

Section: Introductionmentioning

confidence: 99%

“…Chai et al (2015) made use of CA model to simulate traffic streams at signalized intersections. The thing incorporated is fuzzy logic, and results show the model can well replicate decision-making processes [14]. CA model can even be used to evaluate vehicle load effect in bridge construction [15].…”

Section: Introductionmentioning

confidence: 99%

Micro-simulation model of two-lane freeway vehicles for obtaining traffic flow characteristics including safety condition

Yang

Luo

2016

J. Mod. Transport.

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Unidirectional two-lane freeway is a typical and the simplest form of freeway. The traffic flow characteristics including safety condition on two-lane freeway is of great significance in planning, design, and management of a freeway. Many previous traffic flow models are able to figure out flow characteristics such as speed, density, delay, and so forth. These models, however, have great difficulty in reflecting safety condition of vehicles. Besides, for the cellular automation, one of the most widely used microscopic traffic simulation models, its discreteness in both time and space can possibly cause inaccuracy or big errors in simulation results. In this paper, a micro-simulation model of two-lane freeway vehicles is proposed to evaluate characteristics of traffic flow, including safety condition. The model is also discrete in time but continuous in space, and it divides drivers into several groups on the basis of their preferences for overtaking, which makes the simulation more aligned with real situations. Partial test is conducted in this study and results of delay, speed, volume, and density indicate the preliminary validity of our model, based on which the proposed safety coefficient evaluates safety condition under different flow levels. It is found that the results of this evaluation coincide with daily experience of drivers, providing ground for effectiveness of the safety coefficient.

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“…In recent years, emerging computing paradigms, such as machine learning, including arti cial neural networks, fuzzy logic [241][242][243], genetic algorithms, and support vector machines, have been applied for prediction and assessment of natural disasters. GIS and GPS are also being used [244].…”

Section: Discussionmentioning

confidence: 99%

Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

2017

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Abstract. This article presents a state-of-the-art review of di erent methods, signal and image processing techniques, and statistical analyses used for prediction and assessment of natural disasters including earthquakes, tsunamis, volcanic eruptions, hurricanes, tornadoes, and oods. Application of the big data paradigm to the aforementioned natural disasters is also discussed. The research for increasingly more sophisticated computational models will continue to achieve more accurate predictions of various natural disasters.

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Fuzzy Cellular Automata Model for Signalized Intersections

Cited by 50 publications

References 50 publications

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Micro-simulation model of two-lane freeway vehicles for obtaining traffic flow characteristics including safety condition

Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

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