Data Fusion Model of Road Sensors based IoT Feature Clustering

Abstract

The collection of traffic data can play a role in analyzing and predicting highway design, planning, and real-time traffic management. The accuracy requirements for road dynamic data collection are low, and the accuracy is usually 3%-5%. However, it is required that vehicles can pass at high speed and obtain traffic information such as vehicle classification and vehicle speed. The prerequisite for the application of Internet of Things (IoT) technology to road information monitoring lies in the research and development of sensor technology in the perception layer and communication technology in the network layer, so that can obtain a large amount of perception data to serve the development and application of algorithms. To achieve the goal of low-cost and long-term monitoring of comprehensive traffic information and road service status information, this paper constructs a road vibration monitoring system, carries out road vibration monitoring under complex road environments, and proposes a traffic information monitoring method driven by road vibration data. By deploying the pavement vibration monitoring system in the actual road, the original signal of pavement vibration under the action of vehicle moving load is obtained. Through smooth processing and eigenvalue extraction, the monitoring of vehicle speed, wheelbase driving direction, vehicle load position and traffic flow is realized. The experimental results prove that the analysis of the road dynamic response under working conditions, as well as smoothing processing and eigenvalue extraction, the numerical modeling method in this paper realizes the monitoring of the position of the vehicle load and the traffic flow. The calculation error of vehicle speed and wheelbase is within ±4%, which is helpful to find the characteristic index of road vibration signal for evaluating road service status, and provides a reference for the application of road vibration response in road damage early warning and scientific maintenance.