APPLICATION OF ITS, AI AND DIGITAL TWINS IN THE MANAGEMENT AND MAINTENANCE OF THE ROAD NETWORK

Abstract

The transport system faces growing challenges in maintaining road infrastructure due to network ageing, increased traffic loads, limited financial resources, and climate impacts. Traditional maintenance approaches, based on periodic inspections and reactive interventions, are increasingly becoming insufficient to meet the requirements of efficiency, safety, and sustainability. Digitalisation through ITS, IoT, AI, and digital twins offers solutions to modernise road management by enabling data-driven operations, supporting real-time decision-making, and improving predictive maintenance planning. Artificial intelligence (AI) is increasingly transforming the planning, construction, operation, and maintenance of road infrastructure. This paper analyses the role of these technologies in road network monitoring, planning, and maintenance, with a focus on integrating ITS data with Road Asset Management Systems (RAMS), applying edge computing, and using AI models for distress detection and degradation forecasting. In particular, for countries with less developed road infrastructure, the paper discusses key implementation challenges, including data availability and quality, institutional readiness, costs, and cybersecurity considerations. Finally, the paper synthesises the main benefits and limitations and proposes future research directions as well as a phased implementation approach towards resilient and sustainable road infrastructure.