Investigation of relationships between skid resistant deficient pavement and aggregate by GIS and statistical analysis

Clague, Stephen John (2005) Investigation of relationships between skid resistant deficient pavement and aggregate by GIS and statistical analysis. [USQ Project]

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Abstract

The provision of a safe road network for the public is one of the areas of strategic
focus of the Queensland Department of Main Roads. One aspect that facilitates
the delivery of this objective is the maintenance of road pavement surfacings to a
standard that reduces the risk of road related accidents. Efficient and effective
management of skid resistance is one aspect that contributes to this accident risk
minimisation goal.
Skid resistance is affected by a multitude of variables that relate to factors such
as the vehicle, climate, and road surfacing to name a few. This dissertation will
focus solely on those factors that relate to the road surface. The majority of this
data is already stored in electronic form albeit in a variety of databases and
formats so a computer based approach was logical. To enable effective analysis
to occur it was necessary to integrate all data into a single environment. One way
to achieve this is through the use of a Geographical Information System (GIS).
GIS brings a powerful data integration mechanism through which analysis,
presentation and enhanced communication may occur.

Statistics for this ePrint Item

Item Type:	USQ Project
Refereed:	No
Item Status:	Live Archive
Faculty/School / Institute/Centre:	Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information (Up to 30 Jun 2013)
Date Deposited:	11 Oct 2007 00:23
Last Modified:	02 Jul 2013 22:32
Uncontrolled Keywords:	skid resistance, road pavement, geographical information system (GIS)
Fields of Research (2008):	09 Engineering > 0909 Geomatic Engineering > 090903 Geospatial Information Systems
Fields of Research (2020):	40 ENGINEERING > 4013 Geomatic engineering > 401302 Geospatial information systems and geospatial data modelling
URI:	https://sear.unisq.edu.au/id/eprint/506

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