Kalman filtering based dynamic OD matrix estimation and prediction for traffic systems

Author

Yong, LIN ; Yuanli, CAI ; Yongxuan, Huang

Author_Institution

Sch. of Electron. & Inf. Eng., Xi´´an Jiaotong Univ., China

Volume

2

fYear

2003

fDate

12-15 Oct. 2003

Firstpage

1515

Abstract

In this paper, a state space model is proposed so that the dynamic OD matrix can be estimated though the surveillance of flows and traveling time on links in a traffic network. To eliminate the influence of slow time-variant parameters, a recursive least square (RLS) algorithm is introduced to identify the system matrix online. Moreover, an analytical formula to calculate the key assignment matrix is presented. With the sequential Kalman filtering method, the fast and real-time OD estimation and prediction algorithm is established. The algorithm is proven to be very effective and efficient with simulation tests.

Keywords

Kalman filters; filtering theory; least squares approximations; matrix algebra; real-time systems; recursive estimation; road traffic; state-space methods; transportation; dynamic origin-destination matrix estimation; key assignment matrix; real-time origin-destination prediction algorithm; recursive least square algorithm; sequential Kalman filtering; slow time-variant parameters; state space model; traffic systems; Filtering; Kalman filters; Least squares methods; Prediction algorithms; Resonance light scattering; State estimation; State-space methods; Surveillance; Telecommunication traffic; 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.2003.1252737

Filename

1252737