Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K -Nearest Neighbor Pattern Matching Method

Remote transportation microwave sensor (RTMS) technology is being promoted for China's highways. The distance is about 2 to 5 km between RTMSs, which leads to missing data and data sparseness problems. These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-missing problem, based on traffic multimode characteristics, a tensor completion method is proposed to recover the lost RTMS speed and volume data. Aiming at the data sparseness problem, virtual sensor nodes are set up between real RTMS nodes, and the two-dimensional linear interpolation and piecewise method are applied to estimate the average travel time between two nodes. Next, compared with the traditional K-nearest neighbor method, an optimal KNN method is proposed for travel time prediction. optimization is made in three aspects. Firstly, the three original state vectors, that is, speed, volume, and time of the day, are subdivided into seven periods. Secondly, the traffic congestion level is added as a new state vector. Thirdly, the cross-validation method is used to calibrate the K value to improve the adaptability of the KNN algorithm. Based on the data collected from Jinggangao highway, all the algorithms are validated. The results show that the proposed method can improve data quality and prediction precision of travel time.