Predictive management of consumer properties of highways: continuous monitoring technologies in the processes of transport infrastructure operation

Abstract

This paper discusses the transition from traditional periodic road inspection methods to a system of predictive management of roadway serviceability based on continuous monitoring. Serviceability is defined here as measurable characteristics perceived by the user during travel (travel speed and flow stability, safety, comfort, reliability/availability, and information support). The paper proposes managing these characteristics through an indicator → metric → data source → management action framework. The study relies on an analysis of modern data collection tools—such as the Internet of Things (IoT), Vehicle-to-Everything (V2X) and Machine-to-Machine (M2M) systems, and smart sensors installed on road user vehicles and maintenance fleets (including mobile diagnostic units)—combined with telematics and maintenance protocols integrated into a single digital ecosystem. The paper introduces a concept of service-oriented management, where road infrastructure is viewed not as a static object but as a dynamic service, the quality of which is maintained through the real-time interaction of the equipment fleet and predictive analytics systems. A target architecture is proposed, comprising a digital twin of the road asset (including pavement deterioration modeling), sensor networks (IoT) and distributed measurements, a cloud-based GIS platform, and machine learning modules for defect detection and degradation forecasting under uncertainty. It is shown that implementing the proposed approach can increase the operational reliability of roads and ensure the achievement of network reliability/availability targets and fleet efficiency, provided that data quality and fleet readiness conditions are met. The practical significance of this work lies in the potential for road agencies to apply the proposed algorithms to improve fleet utilization efficiency and minimize accident risks caused by roadway conditions. While the benefits (reduction in accidents and costs) are considered expected and substantiated by global experience and literature, the study emphasizes the necessity of pilot implementations and strict data quality validation for application in specific regional conditions.