Development of pavement maintenance management system for an urban areas based upon multiple linear regression analysis

Abstract

The Road Network, a crucial national asset, requires high maintenance investments to ensure longevity and optimal functioning. In the context of Amman city in Jordan, most roads were constructed before the year 2000, which makes most of them at the end of their service life. To address this challenge, we developed a Pavement Maintenance Management System (PMMS) utilising GIS, Micro PAVER, and SPSS software, providing intuitive assessment tools for decision-makers. Focusing on the urban Nasir district in Amman city as a representative case study, our study used Performance Scoring Rating (PSR), Pavement Surface Index (PSI), and Pavement Condition Index (PCI) to evaluate the street network. Benefiting from the GPS technology, conduct a detailed assessment of five streets to assess the distress types, severity levels, and section lengths. Multiple linear regressions were employed to create nine distinct prediction models containing various PSR, PSI, and PCI variables. These models enable estimating and measuring pavement conditions at any street section, even when limited data is available. After testing the developed models, minimal estimation errors and high acceptance levels were found, reinforcing decision-maker reliability.