The effects of traffic on a performance prediction model

Abstract

This thesis analyzes the use of different age and traffic combinations in determining the predicted condition of pavements. The purpose of this study is to develop improved performance prediction models using traffic data from cities and counties in the San Francisco Bay Area. The truck factor and percentage trucks were missing from the data and were derived through standard values from the AASHTO design guide and CALTRANS flexible pavement design guide. PC SAS (Statistical Analysis Software) was used to analyze the data. The model did not always converge on a value when using SAS. To focus on a range of values for the coefficients in the MTC model, linear regression was applied using a spreadsheet. The bounds of the coefficients were then placed in PC SAS. This helped SAS to converge on a value. The models were compared using the mean square and coefficient of determination. The use of traffic shows no improvement over the current models developed using only age. The reasons for this are explained in detail in the thesis.