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Among non-parametric approaches, neural networks and especially Multi-layer Feed-forward Perceptrons (MFLPs) have been shown to be most effective in forecasting traffic flow variables because of their propensity to account for a large range of traffic conditions and generate more accurate predictions than classical statistical forecasting algorithms (Smith & Oswald, 2003, Vlahogianni et al. , 2006). , 1989). They are usually trained under a pattern-based consideration; computations aim at distinguishing clusters that have different statistical properties (mean and variance).
Principe, The gamma model—A new neural-net model for temporal processing, Neural Networks, 5, 565-576, 1992. Y. Wang, M. Papageorgiou, and A. 14, no. 1, pp. 18-31, Jan. 2006. 38 Urban Transport and Hybrid Vehicles P. Webros, “Backpropagation Through time: What it does and How to do it”, IEEE Proceedings Vol 78, No. 10, 1550-1567 Oct. 1990. H. M. 33, no. 1, pp. 1-23. Feb. 1999. H. M. Zhang, “Recursive Prediction of Traffic Conditions With Neural Networks,” Journal of Transportation Engineering, 126(6), 472–481, 2000.
Three new images are required with respect to the basic sigma-delta algorithm: the frame counter image ( ItFC ), the detection counter image ( ItDC ) and the confidence image ( I tCON ). The variance image is intended to represent the variability of pixel intensities when no objects are over that pixel. In other words, the variance image will solely be determined by the background intensities, as a proper threshold should be chosen from that. A low variance should be interpreted as having a “stable background model” that has to be maintained.
