APPLICATIONS OF STATE ESTIMATION ALGORITHMS TO HOURLY TRAFFIC VOLUME SYSTEM

This paper applies three state estimation algorithms to an hourly traffic volume system. The observation values of traffic volume by detectors are contaminated by errors depending on type, setting, sensitivity, and so on. From the statistical analysis of hourly traffic volume data, it is assumed that hourly traffic volumes vary depending on the day of the week and time. The hourly traffic volume system is described by a linear time-varying discrete dynamic system. State and observation equations are described in such linear equations that the state variables are additively affected by the state and observation noises respectively. In order to remove errors caused by type, setting, and sensitivity of a detector, the authors apply such state estimation algorithms as the Kalman filter, the fixed-interval smoother, and the MIPA Kalman filter to the hourly traffic volume system. The Kalman filter and the fixed-interval smoother are optimal for the linear dynamic system with Gaussian noises. The MIPA Kalman filter, based on the M-interval polynomial approximation method, is a robust Kalman filter for the linear dynamic system with non-Gaussian noise. Three state estimation algorithms are applied to the hourly traffic volume system using the observation data obtained from 7 a.m. to 7 p.m. in Fukuyama, Japan. Estimates of the Kalman filter are fairly improved compared with the observation values, and estimates of the fixed-interval smoother are more accurate than the Kalman filter estimates. Estimates of the MIPA Kalman filter are the most accurate and robust as compared with other estimates.

• Supplemental Notes:
  • Five volumes of papers and one volume of abstracts comprise the published set of conference materials.
• Corporate Authors:

  VERTIS

  TORANOMOM 34 MORI BUILDING 1-25-5
  TORANOMON, MINATOKU, TOKYO 105  Japan 
• Authors:
  • SHIMIZU, H
  • Yamagami, K
  • WATANABE, E
• Conference:
  • Steps Forward. Intelligent Transport Systems World Congress
  • Location: Yokohama, Japan
  • Date: 1995-11-9 to 1995-11-11
• Publication Date: 1995-11

Language

• English

Media Info

• Pagination: p. 72

Subject/Index Terms

• TRT Terms: Algorithms; Applications; Dynamics; Estimating; Estimation theory; Kalman filtering; Linear systems; Traffic volume
• Old TRIS Terms: Dynamic conditions
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Research; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 00716543
• Record Type: Publication
• Report/Paper Numbers: Volume 1
• Files: TRIS
• Created Date: Feb 28 1996 12:00AM