Constructing the intelligent expressway traffic monitoring system using the internet of things and inspection robot

With the continuous acceleration of urbanization, traffic congestion, traffic accidents, and urban environmental problems are becoming increasingly serious, which negatively impacts the lives of city residents. With today's urbanization trend, traffic management is a pressing issue, and the safety and smoothness of highways profoundly affect a city's economy and quality of life. As a result, the intelligent inspection robot has entered the public view. It has the advantages of stability and efficiency, can continue to work in a high-intensity state, and helps reduce a lot of human workloads. Firstly, an intelligent transport monitoring system based on the Internet of Things (IoT) is proposed. This system integrates deep learning and artificial intelligence technology, which can quickly query traffic parameters, environmental parameters, and violations that may cause traffic accidents. Secondly, an intelligent inspection robot is introduced to monitor road traffic flow and violation records in real-time, which provides technical support for further scientific management of road traffic. Finally, the intelligent monitoring system's sensitivity and improvement measures are analyzed using the Simultaneous Localization and Mapping (SLAM) algorithm results, making intelligent traffic monitoring more popular. A section of closed safety road is selected for the inspection robot test. The results reveal that (1) the urban transportation model based on the IoT can meet the architecture of intelligent urban transportation. (2) Considering the performance of the inspection robot, the SLAM algorithm is more suitable for road intelligent traffic monitoring. (3) When the number of particles in the improved SLAM algorithm is small, the accuracy and real-time performance of the algorithm can also be guaranteed. The calculation efficiency is improved to 80%, and the modeling accuracy is improved by 23.3%. Traditional traffic monitoring methods typically rely on static sensors and limited data sources. However, the proposed system leverages IoT technology's and inspection robots' real-time data collection capabilities, achieving a more comprehensive, accurate, and flexible acquisition of traffic data. Through this exploration, the overall ideas and objectives of the construction of intelligent highways are clarified, which will lay a solid foundation for the follow-up construction of intelligent highways and provide comprehensive design and practical ideas. The improved SLAM algorithm can more stably complete the positioning and mapping of the tunnel inspection robot in the road environment. In the SLAM algorithm, an Extended Kalman Filter is introduced to ensure the accuracy and real-time of the improved algorithm, which can be applied to the modeling and positioning of unknown environments. Consequently, using the SLAM algorithm in the road detection robot system can stably realize environment awareness and autonomous path planning.