ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprsarchives-XL-2-W3-219-2014 TRAFFIC TIME SERIES FORECASTING BY FEEDFORWARD NEURAL NETWORK: A CASE STUDY BASED ON TRAFFIC DATA OF MONROE Raeesi M. 1 Mesgari M. S. 1 Mahmoudi P. 1 GIS Division and Center of Excellence for Geoinformation Technology. Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology, Tehran, Iran 22 10 2014 XL-2/W3 219 223 Copyright: © 2014 M. Raeesi et al. 2014 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://isprs-archives.copernicus.org/articles/XL-2-W3/219/2014/isprs-archives-XL-2-W3-219-2014.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XL-2-W3/219/2014/isprs-archives-XL-2-W3-219-2014.pdf

Short time prediction is one of the most important factors in intelligence transportation system (ITS). In this research, the use of feed forward neural network for traffic time-series prediction is presented. In this paper, the traffic in one direction of the road segment is predicted. The input of the neural network is the time delay data exported from the road traffic data of Monroe city. The time delay data is used for training the network. For generating the time delay data, the traffic data related to the first 300 days of 2008 is used. The performance of the feed forward neural network model is validated using the real observation data of the 301st day.