Dynamic estimation of freeway origin-destination demand and travel time using extended Kalman filtering algorithm

Shou-Ren Hu, Chi Bang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In the present research, a nonlinear Kalman filtering approach, i.e., Extended Kalman Filter (EKF) was proposed to solve dynamic OD flows and travel times on a freeway segment. The non-linearity results from the facts that the coefficient matrices in the measurement equation of the Kalman filtering framework are unknown in advance and needed to be obtained/updated in light of the most recent observations. The numerical results demonstrated the capability of the proposed EKF model in the dynamic estimation of freeway OD demands and travel times. More significantly, one can design beneficial traffic control and management strategies in accordance with the estimation results.

Original languageEnglish
Title of host publicationConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control
Pages1329-1334
Number of pages6
Publication statusPublished - 2004 Jun 28
EventConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control - Taipei, Taiwan
Duration: 2004 Mar 212004 Mar 23

Publication series

NameConference Proceeding - IEEE International Conference on Networking, Sensing and Control
Volume2

Other

OtherConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control
CountryTaiwan
CityTaipei
Period04-03-2104-03-23

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Hu, S-R., & Chen, C. B. (2004). Dynamic estimation of freeway origin-destination demand and travel time using extended Kalman filtering algorithm. In Conference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control (pp. 1329-1334). (Conference Proceeding - IEEE International Conference on Networking, Sensing and Control; Vol. 2).