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Incorporating automated vehicle identification data into origin-destination estimation Antoniou, Constantinos ; Ben-Akiva, Moshe ; Koutsopoulos, Haris N

By: Contributor(s): Publication details: Transportation Research Record, 2004Description: nr 1882, s. 37-44Subject(s): Bibl.nr: VTI P8167:1882; VTI P8169:2004Location: Abstract: A methodology for the incorporation of automated vehicle identification (AVI) data into origin-destination (O-D) estimation and prediction is presented. AVI technologies facilitate the collection of useful data, such as point-to-point travel times and subpath flows. A framework for the incorporation of AVI data into the well-established O-D estimation and prediction process is presented. Improvements are proposed for both the formulation and the inputs to the O-D estimation and prediction model. Furthermore, as the O-D estimation and prediction process is often used in the traffic estimation and prediction context, approaches to the incorporation of AVI data into other areas of the dynamic traffic assignment framework are outlined. Performance and computational issues are also considered, and the results of a case study are presented to demonstrate the approach.
Item type: Reports, conferences, monographs
Holdings
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Statens väg- och transportforskningsinstitut Available
Statens väg- och transportforskningsinstitut Available

A methodology for the incorporation of automated vehicle identification (AVI) data into origin-destination (O-D) estimation and prediction is presented. AVI technologies facilitate the collection of useful data, such as point-to-point travel times and subpath flows. A framework for the incorporation of AVI data into the well-established O-D estimation and prediction process is presented. Improvements are proposed for both the formulation and the inputs to the O-D estimation and prediction model. Furthermore, as the O-D estimation and prediction process is often used in the traffic estimation and prediction context, approaches to the incorporation of AVI data into other areas of the dynamic traffic assignment framework are outlined. Performance and computational issues are also considered, and the results of a case study are presented to demonstrate the approach.

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