NDLI: Short Term Traffic Flow Prediction Using Hybrid ARIMA and ANN Models

Content Provider	IEEE Xplore Digital Library
Author	Dehuai Zeng Dehuai Zeng Jianmin Xu Jianwei Gu Liyan Liu Gang Xu
Copyright Year	2008
Description	Author affiliation: Sch. of Civil Eng. & Transp., South China Univ. of China, Guangzhou (Dehuai Zeng; Jianmin Xu) \|\| Inst. of Intell. Technol., Shenzhen Univ., Shenzhen (Dehuai Zeng; Gang Xu) \|\| Guangzhou Post & Telecom Equip. Co. Ltd., Guangzhou (Jianwei Gu; Liyan Liu)
Abstract	According to the complexity of the traffic historical data and the randomness of a lot of uncertain factors influence, a hybrid predicting model that combines both autoregressive integrated moving average (ARIMA) and multilayer artificial neural network (MLANN) is proposed in this paper. ARIMA is suitable for linear prediction and MLFNN is suitable for nonlinear prediction. This paper also investigates the issue on how to effectively model short term traffic flow time series with a new algorithm, which estimates the weights of the MLFNN and the parameters of ARMA model. Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.
Starting Page	621
Ending Page	625
File Size	303424
Page Count	5
File Format	PDF
ISBN	9780769533421
DOI	10.1109/PEITS.2008.135
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Time series analysis Artificial neural networks hybrid mode MLFNN Predictive models Traffic flow prediction time series Data models ARIMA model Forecasting Load modeling
Content Type	Text
Resource Type	Article

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