Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction

Samer Madanat; James Krogmeier; Shou Ren Hu

Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction

Samer Madanat, James Krogmeier, Shou Ren Hu

Department of Transportation and Communication Management Science

Research output: Contribution to conference › Paper › peer-review

3 Citations (Scopus)

Abstract

This paper presents an enhanced Kalman Filtering algorithm for the dynamic estimation and prediction of freeway OD matrices. The effects of traffic congestion and traffic diversion information on the OD distribution patterns are explicitly captured through a behavioral model of route switching. In view of time-varying nature of traffic movements, the proposed algorithm updates the model parameters by using on-line traffic measurements. Preliminary simulation results demonstrate the importance of using time-dependent model parameters and accounting for the effect of traffic information in the estimation of dynamic OD demands.

Original language	English
Pages	423-428
Number of pages	6
Publication status	Published - 1996 Jan 1
Event	Proceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering - Capri, Italy Duration: 1995 Jun 27 → 1995 Jun 30

Other

Other	Proceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering
City	Capri, Italy
Period	95-06-27 → 95-06-30

All Science Journal Classification (ASJC) codes

General Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction",

abstract = "This paper presents an enhanced Kalman Filtering algorithm for the dynamic estimation and prediction of freeway OD matrices. The effects of traffic congestion and traffic diversion information on the OD distribution patterns are explicitly captured through a behavioral model of route switching. In view of time-varying nature of traffic movements, the proposed algorithm updates the model parameters by using on-line traffic measurements. Preliminary simulation results demonstrate the importance of using time-dependent model parameters and accounting for the effect of traffic information in the estimation of dynamic OD demands.",

author = "Samer Madanat and James Krogmeier and Hu, {Shou Ren}",

year = "1996",

month = jan,

day = "1",

language = "English",

pages = "423--428",

note = "Proceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering ; Conference date: 27-06-1995 Through 30-06-1995",

}

Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction. / Madanat, Samer; Krogmeier, James; Hu, Shou Ren.
1996. 423-428 Paper presented at Proceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering, Capri, Italy.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

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AU - Madanat, Samer

AU - Krogmeier, James

AU - Hu, Shou Ren

PY - 1996/1/1

Y1 - 1996/1/1

N2 - This paper presents an enhanced Kalman Filtering algorithm for the dynamic estimation and prediction of freeway OD matrices. The effects of traffic congestion and traffic diversion information on the OD distribution patterns are explicitly captured through a behavioral model of route switching. In view of time-varying nature of traffic movements, the proposed algorithm updates the model parameters by using on-line traffic measurements. Preliminary simulation results demonstrate the importance of using time-dependent model parameters and accounting for the effect of traffic information in the estimation of dynamic OD demands.

AB - This paper presents an enhanced Kalman Filtering algorithm for the dynamic estimation and prediction of freeway OD matrices. The effects of traffic congestion and traffic diversion information on the OD distribution patterns are explicitly captured through a behavioral model of route switching. In view of time-varying nature of traffic movements, the proposed algorithm updates the model parameters by using on-line traffic measurements. Preliminary simulation results demonstrate the importance of using time-dependent model parameters and accounting for the effect of traffic information in the estimation of dynamic OD demands.

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M3 - Paper

AN - SCOPUS:0029780581

SP - 423

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T2 - Proceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering

Y2 - 27 June 1995 through 30 June 1995

ER -

Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction

Abstract

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All Science Journal Classification (ASJC) codes

UN SDGs

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