Urban Transport and Hybrid Vehicles by Edited by: Seref Soylu

By Edited by: Seref Soylu

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It is evident that a retraining strategy is needed in order for the neural structures to incorporate and learn newly observed traffic flow events. Although the last is not required during the entire real-time operation of the model, research should be focus on the manner the accuracy of iterative predictions decreases over time, as well as the formulation of a mathematical or empirical criterion to evaluate the time neural networks should be retrained. 5. References B. Abdulhai, H. Porwal and W.

For instance, it shows a limited adaptation capability to certain complex scenes in urban environments or, in general, scenes permanently crossed by lots of objects of very different sizes and speeds. In Manzanera and Richefeu (2007), the authors suggest overcoming this problem using the multiple-frequency sigma-delta background estimation. 1. M 0 = I0 // Initialize background model M V0 = 0 // Initialize variance V for each frame t Δt = M t − It // Compute current difference if Δ t ≠ 0 Vt = Vt −1 + sgn (N ⋅ Δ t − Vt −1 ) // Update variance V end if ~ Δ∇t = RecαΔt (Min (∇( I t ), ∇(Δ t ) )) // Common-edges hybrid reconst.

1990) Finding Structure in Time; Cognitive Science, 14, 179-221. Haykin, S. (1994) Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company, New York, USA. haykin p 300-305 Jose Maria P. Junior & Guilherme A. Barreto (2008) Narx reference: Long-Term Time Series Prediction with the NARX Network: An Empirical Evaluation In: Neurocomputing Volume 71, Issue 16-18 Pages: 3335-3343 ISSN:0925-2312 General Directorate Of Highways. Traffic and Transportation Survey 2000-2004. The MathWorks, Inc.

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