Freeway traffic state estimation: A Lagrangian-space Kalman filter approach

Recent researches have shown the potential benefits of using Lagrangian coordinates in modeling mobile sensor data such as GPS, Bluetooth, Wi-Fi, and cellphone probe data. Research shows the numerical accuracy and convenience of Lagrangian traffic flow models in traffic state estimation. In this paper, a new traffic state estimation model by using Lagrangian-space Kalman filter is proposed based on the travel time transition model (TTM). The proposed methodology reformulates the TTM model into a state-space form to fit the Kalman filter framework. The corresponding state-updating matrices for various traffic conditions are also provided. A numerical experiment is conducted based on a simulation model calibrated with the field loop detector data on IH-894 in Milwaukee, Wisconsin for model evaluation. The proposed TTM-based method is compared with a CTM-based Kalman filter estimator on Eulerian coordinate under different penetration rates of the input Bluetooth, Wi-Fi, or Cellular probe vehicle data in which vehicles are re-identified between two consecutive physical or virtual readers. The evaluation results indicate that TTM-based estimation model performs well especially during congestion and can track traffic breakdowns and recovery effectively. The TTM-based estimator outperforms CTM-based methods at all penetration rates levels. Furthermore, the 4% penetration rate is found to be a threshold beyond which TTM-based estimation results improve significantly. With increased penetration rates, the TTM-based model can achieve a mean absolute percentage error around 10%; while CTM-based model remains higher than 13%.

• Record URL:
  https://doi.org/10.1080/15472450.2018.1476147
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/15472450
• Supplemental Notes:
  • © 2018 Taylor & Francis Group, LLC. Abstract republished with permission of Taylor & Francis.
• Authors:
  • Yang, Han
  • Jin, Peter J.
  • Ran, Bin
  • Yang, Dongyuan
  • Duan, Zhengyu
  • He, Linghui
• Publication Date: 2019-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 525-540
• Serial:
  • Journal of Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 6
  • Publisher: Taylor & Francis
  • ISSN: 1547-2450
  • EISSN: 1547-2442
  • Serial URL: http://www.tandfonline.com/loi/gits20

Subject/Index Terms

• TRT Terms: Freeways; Kalman filtering; Lagrangian functions; Traffic estimation; Traffic flow theory; Traffic models
• Geographic Terms: Milwaukee (Wisconsin)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01723161
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 20 2019 9:49AM