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

Samer Madanat, James Krogmeier, Shou Ren Hu

Research output: Contribution to conferencePaper

2 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 languageEnglish
Pages423-428
Number of pages6
Publication statusPublished - 1996 Jan 1
EventProceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering - Capri, Italy
Duration: 1995 Jun 271995 Jun 30

Other

OtherProceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering
CityCapri, Italy
Period95-06-2795-06-30

Fingerprint

Highway systems
Traffic congestion

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Madanat, S., Krogmeier, J., & Hu, S. R. (1996). Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction. 423-428. Paper presented at Proceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering, Capri, Italy, .
Madanat, Samer ; Krogmeier, James ; Hu, Shou Ren. / Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction. Paper presented at Proceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering, Capri, Italy, .6 p.
@conference{7a07218cabe642cb86872e6e8ce33bfd,
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 = "1",
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",

}

Madanat, S, Krogmeier, J & Hu, SR 1996, 'Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction', Paper presented at Proceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering, Capri, Italy, 95-06-27 - 95-06-30 pp. 423-428.

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 conferencePaper

TY - CONF

T1 - Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction

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.

UR - http://www.scopus.com/inward/record.url?scp=0029780581&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029780581&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0029780581

SP - 423

EP - 428

ER -

Madanat S, Krogmeier J, Hu SR. Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction. 1996. Paper presented at Proceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering, Capri, Italy, .