The objective of this paper is to assess whether existing pavement temperature prediction algorithms can be used to reliably determine Performance Grade (PG) design temperatures for Australian asphalt pavements. The results show good agreement between internationally and locally developed pavement temperature algorithms for the prediction of high pavement design temperature. There is more variability between models in the prediction of low pavement design temperature. The paper also provides a set of PG grading results for a range of Australian asphalt binders. The findings indicate that for a given design situation harder bitumen, ormore highly modified binder would be specified in Australia than in jurisdictions using the PG system. [ABSTRACT FROM AUTHOR]
The conditional autoregressive approach is popular to analyse data with geocoded boundary. However, spatial prediction is often challenging when observed data are sparse. It becomes more challenging in predicting areal units with different areal boundaries. Hence, this paper develops a spatial generalised linear model for spatial predictions using data from spatially misaligned sparse locations. A spatial basis function associated with the conditional autoregressive models and the kriging method is considered. The proposed model demonstrates its better predictive performance through a simulation study and then is applied to understand the spatial pattern of undecided voting preferences in Australia. [ABSTRACT FROM AUTHOR]