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    Estimating annual average daily traffic (AADT) from short-duration counts in towns

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    Klassen-Townsend_Karalee.pdf (9.448Mb)
    Date
    2021
    Author
    Klassen-Townsend, Karalee
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    Abstract
    Traffic volume data, commonly summarized as annual average daily traffic (AADT), is a fundamental input for transportation engineering decisions. Current traffic monitoring guidance provides insufficient detail on the development of AADT estimates from short-duration counts conducted within towns. This is due to limited knowledge of the attributes that characterize a town count and uncertainty about the temporal factors required to estimate AADT from short-duration town count data. This research addressed these gaps by using a decision algorithm and GIS analysis to identify which short-duration counts should be considered town counts and by developing and validating a methodology to estimate AADT from short-duration town count data. The analysis demonstrated that temporal factors generated from continuous counts conducted near towns could be reliably applied to short-duration town count data. This finding enables traffic monitoring authorities to leverage existing data and methods to improve the representativeness of traffic volume estimates in towns.
    URI
    http://hdl.handle.net/1993/35846
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    • FGS - Electronic Theses and Practica [25529]
    • Manitoba Heritage Theses [6064]

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