Title :
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
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;
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
DOI :
10.1109/NCM.2009.224
