TailorAlert: Large Language Model-Based Personalized Alert Generation System for Road Infrastructure Management with Digital Twins

Abstract

Digital twins have emerged as a promising tool in road infrastructure management, enabling real-time monitoring, proactive maintenance, and data-driven decision-making. However, existing digital twin systems primarily rely on generic, non-personalized alerts to proactively deliver information to users, failing to differentiate between the diverse information needs of different stakeholders. This results in information overload for some users and insufficient detail for others, leading to delayed response times and inefficiencies in operations and maintenance workflows. This paper presents a proof-of-concept study on a large language model-based personalized alert generation system, TailorAlert, that integrates with digital twins to provide role-specific alerts. The system utilizes a Large Language Model (LLM) to transform general digital twin alerts into role-specific messages, ensuring that each stakeholder receives the right level of detail tailored to their responsibilities and skill levels. The system is evaluated across seven road maintenance scenarios, assessing alert accuracy, relevance, information overload, prioritization effectiveness, and consistency. Results demonstrate that the system achieves an overall accuracy of 100%, role-content match rate of 82% and information overload rate of 25%, indicating that alerts are effectively tailored to ensure that each user receives comprehensive information necessary to perform their role, while minimizing information overload. Additionally, the system demonstrates a high level of consistency and prioritization accuracy, with an overall low-priority distraction rate of 7% and high-priority miss rate of 4%, reducing unnecessary distractions and ensuring that high-priority alerts are promptly delivered. The findings highlight the potential of LLM-driven digital twin-initiated interactions to optimize information flow, improve response efficiency, and enhance decision-making processes.