A Data-Driven Road Management Application for Automated Pavement Condition and Safety Assessment

Abstract

Road infrastructure management in many developing countries is hindered by the absence of integrated systems for evaluating pavement conditions, road safety, and maintenance planning. This challenge is particularly evident in Iraq, where existing road management practices lack standardized assessment mechanisms and centralized databases. This study presents a Road Management Application (RMA), a data-driven, semi-automated system that integrates structural pavement evaluation and road safety assessment within a unified platform. Developed using Python 3.12.7, the application stores comprehensive road information, including road classification, pavement age, material properties, pavement layer configuration, and geometric characteristics, enabling efficient data management and periodic updates. The RMA applies standardized evaluation criteria to identify and rank pavement defects and safety issues, providing decision support for maintenance planning. The system was validated through case studies on Airport Street and Hilla–Najaf Street in Najaf Governorate, Iraq. The results demonstrate that the application effectively identifies critical pavement and safety deficiencies, prioritizes road sections according to their condition, and establishes a reliable database for long-term infrastructure management. Compared with conventional descriptive surveys, the proposed approach provides a data-driven, reproducible, and integrated framework for road condition assessment and maintenance decision-making. The developed application can be readily adapted for use by road agencies in Iraq and other regions facing similar infrastructure management challenges.