Design of a highway low-temperature icing monitoring and early warning platform

Abstract

Highway icing monitoring is challenged by sparse sensing, heterogeneous data, and the separation between hazard identification and operational response. This paper presents an architecture and deployment design for a highway low-temperature icing monitoring and early warning platform based on multi-source data fusion. The proposed architecture organizes meteorological, pavement, visual, traffic, geographic-exposure, and historical-event information into a closed-loop workflow covering perception, data governance, risk estimation, warning release, operational response, and event feedback. An algorithm-independent, confidence-aware fusion logic is introduced to distinguish dynamic hazard evidence, static road exposure, traffic corroboration, historical priors, and data-quality uncertainty. Preliminary engineering targets, logical service interfaces, and staged deployment principles are also specified. The Suizhong–Panjin section of the Beijing–Harbin Expressway is used as a deployment-oriented case to illustrate sensing priorities and warning strategies for a coastal winter corridor characterized by bridges, fog-prone areas, and heavy truck traffic. The resulting framework provides a structured basis for subsequent prototype implementation, parameter calibration, and real-road validation.