Application of a digital twin for highway tunnels based on multi-sensor and information fusion

Abstract

Due to the harsh environment of highway tunnels and frequent breakdowns of various detection sensors and surveillance devices, the operational management of highway tunnels lacks effective data support. This paper analyzes the characteristics of operational surveillance data in highway tunnels. It proposes a multimodal information fusion method based on CNN–LSTM–attention and designs and develops a digital twin for highway tunnel operations. The system addresses issues such as insufficient development and coordination of the technical architecture of operation control systems, weak information service capabilities, and insufficient data application capabilities. The system also lacks intelligent decision-making and control capabilities. The developed system achieves closed-loop management of “accurate perception–risk assessment–decision warning–emergency management” for highway tunnel operations based on data-driven approaches. The engineering demonstration application underscores the system’s capacity to enhance tunnel traffic safety, diminish tunnel management costs, and elevate tunnel driving comfort.