A Combination Forecasting Model of Urban Ring Road Traffic Flow

Author

Guan, Wei ; Hua, Xie

Author_Institution

Traffic & Transp. Sch., Beijing Jiaotong Univ.

fYear

2006

fDate

17-20 Sept. 2006

Firstpage

671

Lastpage

676

Abstract

A combination forecasting model of urban ring road traffic flow based on neural network, Kalman filter and ARIMA model is proposed in this paper, and the traffic flow data of Beijing third-ring-road (BTRR) are explored to test the validity of the model. The experimental results show that combination forecasting cannot improve the forecasting precision in contrast to which ARIMA model and neural network models are more accurate in the traffic congestion periods. However, combination forecasting model can improve forecasting precision comparing with single forecasting models in traffic non-congestion periods

Keywords

Kalman filters; autoregressive moving average processes; forecasting theory; neural nets; road traffic; traffic engineering computing; ARIMA model; Beijing third-ring-road; Kalman filter; combination forecasting model; neural network; urban ring road traffic flow; Communication system traffic control; Data flow computing; Mathematical model; Neural networks; Predictive models; Real time systems; Roads; Telecommunication traffic; Testing; Traffic control;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE

Conference_Location

Toronto, Ont.

Print_ISBN

1-4244-0093-7

Electronic_ISBN

1-4244-0094-5

Type

conf

DOI

10.1109/ITSC.2006.1706819

Filename

1706819