Layerwise Recurrent Autoencoder for Real-World Traffic Flow Forecasting

Abstract

Accurate spatio-temporal traffic forecasting is a fundamental task for wide applications in city management, transportation area and financial domain. There are many factors that make this significant task also challenging, like: (1) maze-like road network makes the spatial dependency complex; (2) the relationship between traffic flow and time brings non-linear temporal problem; (3) with the larger road network, the difficulty of flow forecasting grows. The prevalent and state-of-the-art methods have mainly been discussed on datasets covering relatively small districts and short time span. To forecast the traffic flow across a wider area and overcome the mentioned challenges, Layerwise Recurrent Autoencoder (LRA) is designed and proposed, in which a three-layer stacked autoencoder (SAE) architecture is used to obtain temporal traffic correlations in three different time scales and for each output of different time scales, a dedicate neural network is used for prediction. The convolutional neural networks (CNN) model is also employed to extract spatial traffic information within the road map for more accurate prediction. To the best of our knowledge, there is no effective method for traffic flow prediction which concerns traffic of city group and LRA is the first one. The experiment is completed on a large real-world traffic dataset to show the performance of the proposed. In the end, evaluations show that our model outperforms the state-of-the-art baselines by 6%–15%.