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

过度拟合 计算机科学 深度学习 人工智能 期限(时间) 流量(计算机网络) 机器学习 人工神经网络 交通生成模型 统计的 实时计算 数学 计算机网络 统计 量子力学 物理
作者
Zhaowei Qu,Haitao Li,Zhihui Li,Zhong Tao
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:23 (1): 225-235 被引量:64
标识
DOI:10.1109/tits.2020.3009725
摘要

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.
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