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《Computer Engineering》 2019-06
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Short Term Traffic Flow Forecasting Based on Dimension Weighted Residual LSTM

LI Yuelong;TANG Dehua;JIANG Guiyuan;XIAO Zhitao;GENG Lei;ZHANG Fang;WU Jun;School of Computer Science and Technology,Tianjin Polytechnic University;Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems;School of Computer Science and Engineering,Nanyang Technological University;School of Electronics and Information Engineering,Tianjin Polytechnic University;  
Due to the fact that the current neural network-based traffic flow forecasting method embeds part of the manually designed features,the feature extracted by the network has single function,bad adaptability,poor robustness and inaccurate characterization of the local features.Therefore,this paper proposes a traffic flow forecasting method based on residual Long Short Term Memory(LSTM).The model uses the idea of ensemble learning to train the spatially distributed data into a residual LSTM network.In addition,dimension weighted units are introduced after each LSTM unit to present the interdependencies between different dimensions of modeling features.Experimental results show that the method can realize adaptive modeling analysis of short term traffic flow data.
【Fund】: 国家自然科学基金(61771340 61302127 61601325);; 天津市自然科学基金(18JCYBJC15300)
【CateGory Index】: TP183;U491.1
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