Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter

Reliable road traffic state identification systems should be designed to provide accurate traffic state information anywhere and anytime. In this paper we propose a road traffic classification system, based on traffic variables estimated using the second order Divided Difference Kalman Filter (DDKF2). This filter is compared with the Extended Kalman Filter (EKF) using both simulated and real-world dataset of highway traffic. Monte-Carlo simulations indicate that the DDKF2 outperforms the EKF filter in terms of parameters estimation error. The real-word evaluation of the DDKF2 filter in terms of classification rate confirms that this filter is promising for real-world traffic state identification systems.

• Record URL:
  • https://doi.org/10.2478/ttj-2019-0010
• Record URL:
  • https://content.sciendo.com/view/journals/ttj/20/2/article-p114.xml
• Supplemental Notes:
  • © Transport and Telecommunication Institute.
• Authors:
  • Ouessai, Asmâa
  • Keche, Mokhtar
• Publication Date: 2019-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 114-122
• Serial:
  • Transport and Telecommunication Journal
  • Volume: 20
  • Issue Number: 2
  • Publisher: De Gruyter Open
  • EISSN: 1407-6179
  • Serial URL: https://sciendo.com/journal/TTJ
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Classification; Freeways; Highway traffic; Kalman filtering; Real time information
• Uncontrolled Terms: Support vector machines; Traffic state estimation
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01711725
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 22 2019 7:59AM