Abstract #301699

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JSM 2003 Abstract #301699
Activity Number: 477
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #301699
Title: Adjusting Pavement Performance Prediction Using a Bayesian Approach
Author(s): Eun Sug Park*+
Companies: Texas A&M University
Address: , College Station, TX, 77843-3135,
Keywords: Pavement Management Information System ; Bayesian adjustment ; state space models ; Kalman filtering ; Markov chain Monte Carlo
Abstract:

We present a method for predicting future pavement condition scores. These scores are used to plan road repairs. Our method assumes that the theoretical pavement condition score is modeled with a sigmoidal equation with coefficients based on prior engineering knowledge. A state space model is employed to accommodate the dynamic nature of pavement conditions and to provide a tool for short-term forecasting. Markov chain Monte Carlo methods are used for computation. The method is demonstrated on data collected by the Texas Transportation Institute at several sites in Texas. The predictions behave in, what the authors believe to be, a reasonable and statistically valid manner.

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