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

Samer Madanat*, James Krogmeier, Shou-Ren Hu

*Corresponding author for this work

Research output: Contribution to conferencePaper

3 Scopus citations

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
StatePublished - 1 Jan 1996
EventProceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering - Capri, Italy
Duration: 27 Jun 199530 Jun 1995

Conference

ConferenceProceedings of the 1995 4th International Conference on Applications of Advanced Technologies in Transportation Engineering
CityCapri, Italy
Period27/06/9530/06/95

Fingerprint Dive into the research topics of 'Enhanced Kalman Filtering algorithm for dynamic freeway OD matrix estimation and prediction'. Together they form a unique fingerprint.

Cite this