Research of Traffic Flow Forecasting Based on Neural Network

Author

Jun, Ma ; Ying, Meng

Author_Institution

Dept. of Comput. Sci., Xi´´an Univ. of the Finance & Econ., Xi´´an

Volume

2

fYear

2008

fDate

20-22 Dec. 2008

Firstpage

104

Lastpage

108

Abstract

Intelligent transportation system (ITS) is an effective measure to solve the problem of traffic jam. Accurate real-time predication of traffic flow is the key technology of ITS. In this paper a dynamic traffic flow forecasting model based on neural network is proposed. BP and RBF neural network are used to build the forecasting models. The data pre-handle method and the judgment criterion of the forecasting model are given. Simulation shows the traffic flow forecasting method is effective, and the RBF can be more fast and effective in forecasting the traffic flow by simulation analysis.

Keywords

automated highways; backpropagation; radial basis function networks; road traffic; BP neural network; RBF neural network; dynamic traffic flow forecasting model; intelligent transportation system; traffic jam; Communication system traffic control; Economic forecasting; Intelligent networks; Intelligent transportation systems; Neural networks; Predictive models; Roads; Telecommunication traffic; Traffic control; Vehicles; neural network; traffic flow forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3497-8

Type

conf

DOI

10.1109/IITA.2008.207

Filename

4739736