INFERENCES ON TRIP MATRICES FROM OBSERVATIONS ON LINK VOLUMES: A BAYESIAN STATISTICAL APPROACH

Previous methods for estimating a trip matrix from traffic volume counts have used the principles of maximum entropy and minimum information. These techniques implicitly give as little weight to prior information on the trip matrix as possible. The new method proposed here is based on bayesian statistical inference and has several advantages over these earlier approaches. It allows complete flexibility in the degree of belief placed on the prior estimate of the trip matrix and also allows for different degrees of belief in different parts of the prior estimate. Furthermore under certain assumptions the method reduces to a simple updating scheme in which observations on the link flows successively modify the trip matrix. At the end of the scheme confidence intervals are available for the estimates of the trip matrix elements. (Author/TRRL)

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Corporate Authors:

  Pergamon Press, Incorporated

  Headington Hill Hall
  Oxford OX30BW,    
• Authors:
  • MAHER, M J
• Publication Date: 1983-12

Media Info

• Features: Figures; References; Tables;
• Pagination: p. 435-447
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 17B
  • Issue Number: 6
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Forecasting; Link volume; Mathematical models; Origin and destination; Statistical analysis; Statistics; Traffic counts; Traffic flow; Traffic simulation; Traffic volume; Travel; Trip matrices
• ITRD Terms: 132: Forecast; 698: Journey; 6473: Mathematical model; 687: Origin destination traffic; 6555: Statistics; 689: Traffic count; 671: Traffic flow
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I71: Traffic Theory;

Filing Info

• Accession Number: 00382152
• Record Type: Publication
• Source Agency: Transport Research Laboratory
• Report/Paper Numbers: HS-036 482
• Files: ITRD, TRIS
• Created Date: Apr 30 1984 12:00AM