TRAFFIC TIME SERIES FORECASTING BY FEEDFORWARD NEURAL NETWORK: A CASE STUDY BASED ON TRAFFIC DATA OF MONROE

被引：11

作者：

Raeesi, M. ^{[1
]}

Mesgari, M. S.

Mahmoudi, P.

机构：

[1] KN Toosi Univ Technology, Fac Geodesy & Geomat Engn, GIS Div, Tehran, Iran

来源：

1ST ISPRS INTERNATIONAL CONFERENCE ON GEOSPATIAL INFORMATION RESEARCH | 2014年 / 40卷 / 2/W3期

关键词：

Traffic; Neural networks; Time series forecasting; Intelligence Transportation System; PREDICTION; MODELS;

D O I：

10.5194/isprsarchives-XL-2-W3-219-2014

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

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.

引用

页码：219 / 223

页数：5

共 12 条

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