Prediction of Highway Performance Monitoring System’s Present Serviceability Rating for Local Agencies Using San Francisco Bay Area Pavement Management System

Abstract

Several local agencies in the San Francisco Bay Area use the Metropolitan Transportation Commission (MTC) pavement management system (PMS) that requires a pavement condition index (PCI) as the primary condition measure. This PCI is based on distress types, severities, and quantities. However, several of these local agencies must also submit present serviceability rating (PSR) data on a sample of their network for use in the Highway Performance Monitoring System (HPMS). Currently, these agencies use a trained rater to determine a subjective PSR value for each HPMS section to report to FHWA and another set of trained raters to inspect the pavement for surface observable distress from which the PCI is calculated. A study was performed to develop mathematical models to relate the PCI used in the MTC PMS to the subjective PSR submitted by local agencies for FHWA’s HPMS reports. Regression equations were developed to predict the PSR values, as defined for HPMS, from Bay Area PCI values and subcomponents of the PCI. These equations have R2 values that show moderate to strong relationships between the HPMS PSR and the MTC PCI. They provide reasonable values at or near the boundaries of the PSR scale. The local agencies using the Bay Area PMS can use these equations to estimate a PSR value from the inspection required for the PMS without inspecting pavement sections a second time.