Title :
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
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;
Conference_Titel :
Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE
Print_ISBN :
0-7803-8125-4
DOI :
10.1109/ITSC.2003.1252737
