文章基本信息

标题：TRAFFIC TIME SERIES FORECASTING BY FEEDFORWARD NEURAL NETWORK: A CASE STUDY BASED ON TRAFFIC DATA OF MONROE
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作者：M. Raeesi ; M. S. Mesgari ; P. Mahmoudi 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2014
卷号：XL-2/W3
页码：219-223
DOI：10.5194/isprsarchives-XL-2-W3-219-2014
出版社：Copernicus Publications
摘要：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.
关键词：Traffic; Neural networks; Time series forecasting; Intelligence Transportation System