Citation

Thomas S, Sharma SC, Liu GX. ITE J. 1997; 67(4): 5-.

Affiliation

University of Regina, Regina, Sask, Canada

Copyright

(Copyright © 1997, Institute of Transportation Engineers)

DOI

unavailable

PMID

unavailable

Abstract

Accurate truck volume and classification data was used in asphalt concrete (AC) pavement overlay designs. The AC overlay designs are sensitive to changes in the truck volume and average load equivalency factor. The design life is also sensitive to the truck annual average daily traffic (TAADT) estimates. The spatial, temporal and distributional patterns of truck traffic obtained from permanent automatic vehicle classifier (PAVC) patterns can be used to classify statewide truck routes. The estimates of the truck type distribution from a single 48-hour traffic count can be subjected to a large margin of error. Increasing frequency of counts to two or three in a year can be expected to reduce the error in the estimates of truck type distribution.

In the past, collection of vehicle classification (VC) data was difficult and costly Such data often had to be collected manually. Recently, considerable advances have been made concerning automatic vehicle-classifier (AVC) technology. AVCs are devices capable of providing a record of vehicle volume with respect to vehicle type. Most highway agencies in North America now employ permanent automatic vehicle classifiers (PAVC) at some locations on their networks. In addition, there is a considerable interest on the part of these agencies to expand their existing PAVC programs and use available portable AVC technology to undertake seasonal and short-period vehicle classification counts.

The Impact of the Structure of Debt on Target Gains While AVC technology has advanced considerably in the past few years, the practices of collection, analysis and application of AVC data have not progressed similarly. The main objectives of this study are 1) to demonstrate the need for an accurate truck volume and classification data in overlay designs of pavement; 2) to study temporal and spatial variations in vehicle composition and truck volume at a number of PAVC sites located in the Canadian provinces of Alberta and Saskatchewan; and 3) to study errors associated with sample estimates of truck annual average daily traffic (TAADT) and vehicle classification.

Language: en