Modelling public transport trips by radial basis function neural networks

Çelikoğlu, Hilmi Berk; Ciğizoğlu, H. Kerem

doi:10.1016/j.mcm.2006.07.002

Cited by 42 publications

(30 citation statements)

References 20 publications

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“…It concludes that the Generalised Regression network is the most accurate and robust. A similar work comparing two ANNs is described in [25]. A Feed-Forward Back Projection and a Radial Basis Function with an autoregressive model was used for the purpose of predicting traffic flow.…”

Section: Traffic Condition Forecastingmentioning

confidence: 99%

Adapting traffic simulation for traffic management: A neural network approach

Passow

Elizondo

Chiclana

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-Static models and simulations are commonly used in urban traffic management but none feature a dynamic element for near real-time traffic control. This work presents an artificial neural network forecaster methodology applied to traffic flow condition prediction. The spatially distributed architecture uses life-long learning with a novel adaptive Artificial Neural Network based filter to detect and remove outliers from training data. The system has been designed to support traffic engineers in their decision making to react to traffic conditions before they get out of control.We performed experiments using feed-forward backpropagation, cascade-forward back-propagation, radial basis, and generalized regression Artificial Neural Networks for this purpose. Test results on actual data collected from the city of Leicester, UK, confirm our approach to deliver suitable forecasts.

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Section: Traffic Condition Forecastingmentioning

confidence: 99%

Adapting traffic simulation for traffic management: A neural network approach

Passow

Elizondo

Chiclana

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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show abstract

“…Since the RBFNN has only one hidden layer and has fast convergence speed, it is widely used for nonlinear mappings between inputs and outputs. Examples include detecting spam e-mail [58], financial distress prediction [26], public transportation [22], classification of active components in traditional medicine [71], classification of audio signals [35], prediction of athletes' performance [57], and face recognition [11].…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

“…The parameters of the nth iteration are updated for the (n ? 1)th iteration according to (20)(21)(22).…”

Section: Update Of the Number Of Pheromonementioning

confidence: 99%

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Mostafa

Toksarı

2011

Neural Comput & Applic

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First performed in 1954, organ transplantation is a universally practiced clinical procedure. This study uses ant colony optimization (ACO), radial basis function neural network (RBFNN), Kohonen's self-organizing maps (SOM), and support vector machines (SVMs) to examine the effect of various cognitive, psychographic, and attitudinal factors on organ donation. ACO, RBFNN, SOM, and SVMs are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are altruistic values, perceived risks/benefits, knowledge, attitudes toward organ donation, and intention to donate organs. The paper shows how it is possible to identify various dimensions of organ donation behavior by uncovering complex patterns in the dataset and also shows the classification and clustering abilities of machine-learning systems.

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“…Let I (1) n (x) and I (2) n (x) be formally given by (14) and (15), respectively. Then I n (D δ ) = I (1) n (x) ∪ I (2) n (x).…”

Section: Approximation Of Exact Solution In Rmentioning

confidence: 99%