Fusing Heterogeneous Traffic Data by Kalman Filters and Gaussian Mixture Models

Heterogeneous traffic data collected from different types of sensors are fused for estimating traffic states more accurately. Data quality and fusion method are two key issues required to be solved in the traffic state estimation. In this paper, the authors propose a fusion method of heterogeneous traffic data based on the Kalman filters (KF) and Gaussian mixture models (GMM). The noise in collected raw data is reduced by the KF in order to improve the quality of input data for fusion. The vectors of historical data from global positioning system (GPS) and remote traffic microwave sensors (RTMS) in different traffic states are modeled with multiple multi-variate GMM respectively. Finally, the estimated traffic state can be obtained by computing the posterior probabilities with the vector data and GMM. Performance of the authors work is examined by series of experiments, and the results show that the proposed method is effective for improving the precision of traffic state estimation.

• Record URL:
  https://doi.org/10.1109/ITSC.2014.6957704
• Availability:
  • Find a library where document is available. Order URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6948869
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Corporate Authors:

  Institute of Electrical and Electronics Engineers (IEEE)

  3 Park Avenue, 17th Floor
  New York, NY  United States  10016-5997
• Authors:
  • Wang, Chunhui
  • Zhu, Qianqian
  • Shan, Zhenyu
  • Xia, Yingjie
  • Liu, Yuncai
• Conference:
  • 17th International IEEE Conference on Intelligent Transportation Systems (ITSC14)
  • Location: Qingdao , China
  • Date: 2014-10-8 to 2014-10-11
• Publication Date: 2014-10

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 276-281
• Monograph Title: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC14)

Subject/Index Terms

• TRT Terms: Data quality; Global Positioning System; Heterogeneity; Kalman filtering; Microwaves; Remote sensing; Traffic data; Traffic estimation
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I73: Traffic Control;

Filing Info

• Accession Number: 01562643
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 30 2015 12:03PM