Short-Term Traffic Flow Forecasting Method With M-B-LSTM Hybrid Network

Deep learning has achieved good performance in short-term traffic forecasting recently. However, the stochasticity and distribution imbalance are main characteristics to traffic flow, and these will bring the uncertainty and induce the network overfitting problem during deep learning. To deal with the problems, a new end-to-end hybrid deep learning network model, named M-B-LSTM, is proposed for short-term traffic flow forecasting in this paper. In the M-B-LSTM model, an online self-learning network is constructed as a data mapping layer to learn and equalize the traffic flow statistic distribution for reducing the effect of distribution imbalance and overfitting problem during network learning. Besides, the deep bidirectional long short-term memory network (DBLSTM) is introduced to reduce the uncertainty problem by forward and reverse contexts approximation process in the stochasticity reducing layer, and then the long short-term memory network (LSTM) is used to forecast the next traffic flow state in the forecasting layer. Furthermore, sufficient comparative experiments have been conducted and the results show the proposed model has better ability on solving uncertainty and overfitting problems than the state-of-art methods.

I. Introduction

Traffic flow forecasting is an essential component of intelligent transportation system. Real-time accurate short-time traffic flow forecasting can give traffic guidance for traffic participants to select an appropriate travel route, and help the traffic managers with a reasonable control strategy to relieve traffic congestion. Traffic flow is a time series data with strong regularity and cyclicity which are the basis for accurate forecasting, but the randomness and uncertainty of it increase forecast difficulty [1]. Thus, short-time traffic flow forecasting is essential also challenging in transportation management and research fields.