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

Lin Yong, / Cai YuanLi, / Huang YongXuan,

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.

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

Lin Yong, / Cai YuanLi, / Huang YongXuan,

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.

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

Lin Yong, / Cai YuanLi, / Huang YongXuan,

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.

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Document information

Title:

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

Contributors:

Lin Yong, (author) / Cai YuanLi, (author) / Huang YongXuan, (author)

Published in:

Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems ; 2 ; 1515-1520 vol.2

Publication date:

2003-01-01

Size:

355294 byte

ISBN:

DOI:

https://doi.org/10.1109/ITSC.2003.1252737

Type of media:

Conference paper

Type of material:

Electronic Resource

Language:

English

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