Redundancy-Reducing and Holiday Speed Prediction Based on Highway Traffic Speed Data

Abstract

The accurate prediction of the highway traffic speed plays an important role in improving the production efficiency and the convenience of lives, and it is also one of the important issues in vehicle social big data analyses. Aiming at the problem of highway traffic speed data preprocessing, this paper proposes the algorithm of Redundant Data Reducing (RDR) that can greatly reduce the amount of data in model training of long short-term memory (LSTM) and improve the training speed under the condition that the influence on prediction accuracy is small and controllable. Aiming at the problem of low prediction accuracy for holiday traffic speeds due to small data volume, this paper proposes the Segment Prediction Algorithm (SPA) based on the speed features, which can effectively improve the prediction accuracy. The experimental results show that RDR can reduce the training data by up to 60% without notably affecting the prediction accuracy, while it improves the training speed by 60%. Compared with the current LSTM algorithm, the prediction accuracy of SPA improves significantly.