Estimation of dynamic assignment matrices and OD demands using adaptive Kalman filtering

Shou Ren Hu; Samer M. Madanat; James V. Krogmeier; Srinivas Peeta

Estimation of dynamic assignment matrices and OD demands using adaptive Kalman filtering

Shou Ren Hu, Samer M. Madanat, James V. Krogmeier, Srinivas Peeta

Civil Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

The purpose of this research was to develop a dynamic model for the on-line estimation and prediction of freeway users' origin-destination (OD) matrices. In this paper, we present a Kalman Filtering algorithm that uses time-varying assignment matrices generated by using a mesoscopic traffic simulator. The use of a traffic simulator to predict time-varying travel time model parameters was shown to be promising for the determination of dynamic OD matrices for a freeway system. Moreover, the issues of using time-varying model parameters, effects of incorporating different sources of measurements and the use of adaptive estimation are addressed and investigated in this research.

Original language	English (US)
Pages (from-to)	281-300
Number of pages	20
Journal	ITS Journal
Volume	6
Issue number	3
State	Published - 2001

Keywords

Adaptive filters
Kalman Filtering
Optimal estimation
Origin-destination demands
Traffic simulator

ASJC Scopus subject areas

Automotive Engineering
Mechanical Engineering
Strategy and Management
Management Science and Operations Research

Cite this

@article{dd85f20a40164156a383f7fc7310e7ae,

title = "Estimation of dynamic assignment matrices and OD demands using adaptive Kalman filtering",

abstract = "The purpose of this research was to develop a dynamic model for the on-line estimation and prediction of freeway users' origin-destination (OD) matrices. In this paper, we present a Kalman Filtering algorithm that uses time-varying assignment matrices generated by using a mesoscopic traffic simulator. The use of a traffic simulator to predict time-varying travel time model parameters was shown to be promising for the determination of dynamic OD matrices for a freeway system. Moreover, the issues of using time-varying model parameters, effects of incorporating different sources of measurements and the use of adaptive estimation are addressed and investigated in this research.",

keywords = "Adaptive filters, Kalman Filtering, Optimal estimation, Origin-destination demands, Traffic simulator",

author = "Hu, {Shou Ren} and Madanat, {Samer M.} and Krogmeier, {James V.} and Srinivas Peeta",

year = "2001",

language = "English (US)",

volume = "6",

pages = "281--300",

journal = "ITS Journal",

issn = "1024-8072",

publisher = "Taylor and Francis Ltd.",

number = "3",

}

TY - JOUR

T1 - Estimation of dynamic assignment matrices and OD demands using adaptive Kalman filtering

AU - Hu, Shou Ren

AU - Madanat, Samer M.

AU - Krogmeier, James V.

AU - Peeta, Srinivas

PY - 2001

Y1 - 2001

N2 - The purpose of this research was to develop a dynamic model for the on-line estimation and prediction of freeway users' origin-destination (OD) matrices. In this paper, we present a Kalman Filtering algorithm that uses time-varying assignment matrices generated by using a mesoscopic traffic simulator. The use of a traffic simulator to predict time-varying travel time model parameters was shown to be promising for the determination of dynamic OD matrices for a freeway system. Moreover, the issues of using time-varying model parameters, effects of incorporating different sources of measurements and the use of adaptive estimation are addressed and investigated in this research.

AB - The purpose of this research was to develop a dynamic model for the on-line estimation and prediction of freeway users' origin-destination (OD) matrices. In this paper, we present a Kalman Filtering algorithm that uses time-varying assignment matrices generated by using a mesoscopic traffic simulator. The use of a traffic simulator to predict time-varying travel time model parameters was shown to be promising for the determination of dynamic OD matrices for a freeway system. Moreover, the issues of using time-varying model parameters, effects of incorporating different sources of measurements and the use of adaptive estimation are addressed and investigated in this research.

KW - Adaptive filters

KW - Kalman Filtering

KW - Optimal estimation

KW - Origin-destination demands

KW - Traffic simulator

UR - http://www.scopus.com/inward/record.url?scp=2942673411&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=2942673411&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:2942673411

SN - 1024-8072

VL - 6

SP - 281

EP - 300

JO - ITS Journal

JF - ITS Journal

IS - 3

ER -

Estimation of dynamic assignment matrices and OD demands using adaptive Kalman filtering

Abstract

Keywords

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this