Multi-Step Traffic Flow Prediction Using Recurrent Neural Network

Abstract

Multi-step traffic flow prediction extends short-term single-step prediction to long-term prediction, which is more significant in many basic application in intelligent transportation systems, such as traffic planning. A main problem of multi-step prediction is that the error accumulation as steps increase, resulting in prediction performance degradation. In this work, combining recursive and multi-output strategies, we proposed a deep learning model, named MARNN, for multi-step traffic flow prediction. Specifically, we jointly consider recurrent neural network as dynamic neural network for simulating the dynamic characteristics in traffic time series as recursive strategy does and multi-output strategy for decreasing the accumulated error as step increases. In addition, we introduce attention mechanism for adaptively seeking correlated important information among traffic time series to improve prediction performance. The experiments on real traffic data show the advantages of MARNN model over other four baseline models, demonstrating the potential and promising capability of the proposed model on multi-step traffic flow prediction.