Traffic Flow Prediction Based on Stream Tensor Analysis

Abstract

In intelligent transportation system (ITS), traffic flow prediction can provide data support for route planning, traffic management and public safety. Prediction algorithms based on machine learning or deep learning usually need a large number of unabridged historical data to conduct parameter training. However, data will be missing and abnormal in practice, which will affect the accuracy of prediction. In this paper, we propose the stream tensor analysis (STA) algorithm for traffic flow prediction. First, dynamic tensor stream of four dimensions of space, time, day and week is constructed to better mine the multi-mode correlation between traffic flow data. Second, the first few columns with the largest norm are selected to update the projection matrix by the tracking projection matrix algorithm. The experimental results show that the STA algorithm has low complexity, and also achieve good prediction performance in random missing and extreme missing patterns.