Intelligent early warning of highway tunnel safety monitoring data based on Markov model
Yang Wang · 2024
Traditional highway tunnel management systems often face problems such as independent and decentralized management, inflexible monitoring, and low visualization of disease details. Especially in the face of traffic accidents, lane foreign objects, vehicle congestion, and vehicle and pedestrian congestion in tunnels, traditional warning systems often fail to provide timely and accurate warning information, posing great risks to the safe operation of tunnels. In response to the above issues, this article proposes an intelligent early warning system for highway tunnel safety monitoring data based on Markov model (MM). This system fully utilizes the advantages of MM in describing random processes, and achieves dynamic monitoring and early warning of tunnel safety status through real-time analysis of tunnel monitoring data, combined with Internet of Things (IoT) technology. The experimental results show that the intelligent early warning system for highway tunnel safety monitoring data based on MM has high accuracy and reliability. It can not only achieve real-time monitoring and early warning of tunnel safety status, but also provide rich visual information to help managers better understand the operation status of tunnels and improve the level of tunnel safety management.