Road Traffic Flow Prediction with a Time-Oriented ARIMA Model

Author

Dong, Honghui ; Jia, Limin ; Sun, Xiaoliang ; Li, Chenxi ; Qin, Yong

Author_Institution

State Key Lab. of Rail Traffic Control & Safety, Beijing Jiaotong Univ., Beijing, China

fYear

2009

fDate

25-27 Aug. 2009

Firstpage

1649

Lastpage

1652

Abstract

The prediction of the traffic flow can give the people important traveling information. In this paper, the traffic flow prediction problem is studied. An ARIMA model is proposed for the traffic flow prediction. The ARIMA model is trained according to the different period traffic data. Based on the different period data training, the ARIMA model is refined more accuracy. The experiments show that the ARIMA model trained by the time-oriented data can reach a better result than the non time-oriented data trained model.

Keywords

autoregressive moving average processes; forecasting theory; road traffic; ARIMA model; non time-oriented data trained model; road traffic flow prediction; time-oriented data model; traffic data; traveling information; Autocorrelation; Parameter estimation; Predictive models; Rails; Road safety; Sun; Testing; Traffic control; Training data; Yttrium; ARIMA; Traffic flow; level of service;

fLanguage

English

Publisher

ieee

Conference_Titel

INC, IMS and IDC, 2009. NCM '09. Fifth International Joint Conference on

Conference_Location

Seoul

Print_ISBN

978-1-4244-5209-5

Electronic_ISBN

978-0-7695-3769-6

Type

conf

DOI

10.1109/NCM.2009.224

Filename

5331589

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2363093