Use of local lane distribution patterns to estimate missing data values from traffic monitoring systems Smith, Brian L ; Conklin, James H
Publication details: Transportation Research Record, 2002Description: nr 1811, s. 50-6Subject(s): Bibl.nr: VTI P8167:1811Location: Abstract: Lane distribution for a given link (where a link is a short, directional segment of freeway) is defined as the proportion of total link volume served by each lane. The state of the knowledge in terms of lane distribution, as reported in the Highway Capacity Manual, is that "there are no typical lane distributions." However, this research effort found that, in three independent freeway links in the Hampton Roads region of Virginia, consistent lane distribution patterns were found by time of day and location. This finding was used to develop a methodology to estimate missing detector data for use in intelligent transportation system (ITS) data. The methodology uses time-of-day lane distribution patterns at a particular location to estimate missing detector data. Evaluation of this methodology found that the error associated with this approach ranged from 6% to 8%. This low error indicates that this methodology, which can easily be implemented in ITSs with archived data, holds high potential.| Cover image | Item type | Current library | Home library | Collection | Shelving location | Call number | Materials specified | Vol info | URL | Copy number | Status | Notes | Date due | Barcode | Item holds | Item hold queue priority | Course reserves | |
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| Statens väg- och transportforskningsinstitut | Available |
Lane distribution for a given link (where a link is a short, directional segment of freeway) is defined as the proportion of total link volume served by each lane. The state of the knowledge in terms of lane distribution, as reported in the Highway Capacity Manual, is that "there are no typical lane distributions." However, this research effort found that, in three independent freeway links in the Hampton Roads region of Virginia, consistent lane distribution patterns were found by time of day and location. This finding was used to develop a methodology to estimate missing detector data for use in intelligent transportation system (ITS) data. The methodology uses time-of-day lane distribution patterns at a particular location to estimate missing detector data. Evaluation of this methodology found that the error associated with this approach ranged from 6% to 8%. This low error indicates that this methodology, which can easily be implemented in ITSs with archived data, holds high potential.