基于维度加权的残差LSTM 短期交通流量预测
Short Term Traffic Flow Forecasting Based on Dimension Weighted Residual LSTM
查看参考文献15篇
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文摘
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基于神经网络的交通流量预测由于嵌入了部分手工设计的特征,使得提取的网络特征功能单一,存在适应性及鲁棒性差、数据局部特征刻画不准确等问题。为此,提出基于残差长短期记忆网络(LSTM)的交通流量预测方法,利用集成学习思想将空间分布的数据端到端训练到残差LSTM 网络中,同时在每个LSTM 单元后引入维度加权单元,显式建模特征维度之间的相互依赖关系。实验结果表明,该方法能实现短期交通流量数据的自适应建模分析。 |
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其他语种文摘
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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. |
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来源
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计算机工程
,2019,45(6):1-5 【扩展库】
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DOI
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10.19678/j.issn.1000-3428.0052120
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关键词
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智能交通
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短期交通流量预测
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残差连接
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长短期记忆网络
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维度加权
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地址
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1.
天津工业大学计算机科学与技术学院, 天津, 300387
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天津市光电检测技术与系统重点实验室, 天津市光电检测技术与系统重点实验室, 天津, 300387
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南洋理工大学计算机科学与工程学院, 新加坡, 新加坡, 639798
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天津工业大学电子与信息工程学院, 天津, 300387
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-3428 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金
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天津市自然科学基金
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文献收藏号
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CSCD:6513194
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