DocumentCode :
2383236
Title :
A review of some main models for traffic flow forecasting
Author :
Han, Chao ; Song, Su
Author_Institution :
Electron. Inf. & Control Eng. Coll., Beijing Polytech. Univ., China
Volume :
1
fYear :
2003
fDate :
12-15 Oct. 2003
Firstpage :
216
Abstract :
As intelligent transportation systems (ITS) are implemented widely throughout the world, managers of transportation systems have access to large amounts of "real-time" status data. Real-time forecasting is becoming an important tool in ITS. During the past few years, various traffic-flow forecasting models have been developed and forecasting accuracy has been improved substantially. In this paper some main models for traffic flow forecasting are introduced. These models include ARIMA model, various neural network (NN) models, nonparametric model, and so on. Furthermore, in this paper some techniques for improving the forecasting power are also discussed which include judgmental adjustment technique, adaptive estimation for time-varying parameters and adopting feed-back loop structure when estimating parameters. Finally, a summary of these models and techniques is to be given.
Keywords :
adaptive estimation; air traffic; autoregressive moving average processes; chaos; forecasting theory; neural nets; parameter estimation; real-time systems; system theory; transportation; ARIMA model; ITS; NN; adaptive estimation; autoregressive integrated moving average model; chaotic system theory; feedback loop structure; intelligent transportation systems; neural network models; nonparametric model; parameters estimation; real time data; real time forecasting; time varying parameters; traffic flow forecasting model; Chaos; Communication system traffic control; Control engineering; Intelligent transportation systems; Parameter estimation; Predictive models; Real time systems; Telecommunication traffic; Time series analysis; Traffic control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE
Print_ISBN :
0-7803-8125-4
Type :
conf
DOI :
10.1109/ITSC.2003.1251951
Filename :
1251951
Link To Document :
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