NDLI: Artificial neural network and wavelet analysis application in speed forecast of Beijing urban freeway

Content Provider	IEEE Xplore Digital Library
Author	Xu Ting Xiaoduan Sun Wu Yan He Yulong Changrong Xie
Copyright Year	2009
Description	Author affiliation: Yangzhou Transportation Management Bureau, 225002, China (Changrong Xie) \|\| Key Lab of Transportation Engineering Beijing, University of Technology, 100124, China (Xu Ting; Xiaoduan Sun; He Yulong) \|\| School of Economy and Management, Chang'an University, Xi'an, 710064, China (Wu Yan)
Abstract	Speed is an important factor for traffic safety evolution.The use of ITS technology in speed management with real-time information of urban freeway is one of strategies to enhance road safety. This paper presents methods for prediction short-term traffic flow speed on Beijing urban freeway with real time information from inductive loops. The source data sets including traffic volumes, speed and occupancy which are collected 24h/day over several years on Beijing ring road. Traffic flow is divided into three statuses, including free flow, transition status and congestion according to occupancy. To achieve objective prediction results, wavelet technology is applied to de-noising process. The artificial neural network, which does not require any pre-defined underlying relationship between dependent and independent variables, is a powerful tool in dealing with prediction problems. In this paper, RBF network is designed for predicting speed for future five minutes. Results show that the proposed RBF network model produces reliable estimates of vehicle speed for three various traffic conditions, especially congestion condition.
Starting Page	1004
Ending Page	1008
File Size	974447
Page Count	5
File Format	PDF
ISBN	9781424435036
ISSN	19310587
DOI	10.1109/IVS.2009.5164418
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Wavelet analysis Traffic control Telecommunication traffic Road safety Radial basis function networks Information management Technology management Noise reduction Vehicles RBF network Traffic prediction Wavelet denoise Speed Prediction DWT
Content Type	Text
Resource Type	Article

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