Interactive multiple model ensemble Kalman filter for traffic estimation and incident detection

Ren Wang, Daniel B. Work

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

Abstract

This paper studies the problem of real-time traffic estimation and incident detection by posing it as a hybrid state estimation problem. An interactive multiple model ensemble Kalman filter is proposed to solve the sequential estimation problem, and to accommodate the switching dynamics and nonlinearity of the traffic incident model. The effectiveness of the proposed algorithm is evaluated through numerical experiments using a perturbed traffic model as the true model. The supporting source code is available for download at https://github.com/Lab-Work/IMM-EnKF-Traffic-Estimation-Incident-Detection.

Original languageEnglish (US)
Title of host publication2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages804-809
Number of pages6
ISBN (Electronic)9781479960781
DOIs
StatePublished - Nov 14 2014
Event2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 - Qingdao, China
Duration: Oct 8 2014Oct 11 2014

Other

Other2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
CountryChina
CityQingdao
Period10/8/1410/11/14

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Mechanical Engineering

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  • Cite this

    Wang, R., & Work, D. B. (2014). Interactive multiple model ensemble Kalman filter for traffic estimation and incident detection. In 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 (pp. 804-809). [6957788] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2014.6957788