Creating a Predictive Model for Pavement Deterioration using Geographic Weighted Regression

Abstract

This paper represents an effort by the New York City Department of Transportation (NYCDOT) to develop, by means of geographically weighted regression (GWR), an empirical pavement deterioration model for the five boroughs of New York City. Evaluation of probabilistic and deterministic models from existing literature, as well as statistical modeling techniques and applications, were examined to inform model selection. A model using GWR was selected as the preferred methodology. Validation tests demonstrated an adjusted R² of .808 and a standard error (σ) representing 4.8% of the mean. Emphasis is placed on the novelty and practicality of GWR as a means for pavement deterioration prediction. Future applications of this model will aid decision makers in strategically allocating limited funds to priority projects.