Abstract: During holidays, the highway traffic pressure increases, and the traffic data collected directly is prone to the defects of data loss and error, which further affects the accuracy of prediction. Therefore, the prediction algorithm of holiday highway traffic congestion under the Internet of Things technology is proposed. The algorithm collects holiday road traffic data in real time through the Internet of Things technology, and carries out correlation analysis and sets up relevant intersection sets. Combining the traffic-occupancy model and radial basis function neural network model, the traffic flow is repaired, and each road section is divided by K-means clustering analysis. By constructing the compression matrix of road network data, the mapping relationship between the original data matrices of road network is realized, and the traffic prediction results of all sections of the whole road network during holidays are obtained. The experimental results show that the CE(Coefficient of Efficiency) of the proposed algorithm is significantly close to 1, which is closer to the actual observation value. It proves that the proposed algorithm has good performance in highway traffic congestion prediction and can effectively realize highway traffic congestion prediction on holidays. 

Key words: internet of things technology, holidays, highways, traffic congestion situation, forecast