Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous rese...Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).展开更多
针对SDN流量工程中流量预测基于静态时空依赖的问题,提出了一种基于注意力机制的图卷积神经网络(GCN)与门控递归单元(GRU)集成的动态网络流量预测方法——AGCNGRU(attention mechanism for GCNGRU model)。借助GCN捕获网络中节点之间的...针对SDN流量工程中流量预测基于静态时空依赖的问题,提出了一种基于注意力机制的图卷积神经网络(GCN)与门控递归单元(GRU)集成的动态网络流量预测方法——AGCNGRU(attention mechanism for GCNGRU model)。借助GCN捕获网络中节点之间的流量空间依赖性和GRU捕获流量经过网络中各节点的时间依赖性,通过时间注意力机制设计每个隐藏状态的权重,以调整时间点流量信息的重要性,同时通过数据驱动空间注意力机制动态自适应调整Laplace矩阵,实现动态提取网络信息数据时空相关性,最终完成动态流量精准预测。在GEANT的数据集上的实验表明,所提出的方法在均方误差方面比GCNGRU减少24.8%,比GRU减少66.4%,并通过与传统路由算法OSPF、DDPG算法比较,在90%的流量负载强度下,网络性能比OSPF提升了24%,比DDPG提升了8.1%,进一步说明了AGCNGRU算法网络流量准确预测带来的时效性和有效性。展开更多
基金Supported by the Support Program of the National 12th Five Year-Plan of China(2015BAK25B03)
文摘Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).
文摘针对SDN流量工程中流量预测基于静态时空依赖的问题,提出了一种基于注意力机制的图卷积神经网络(GCN)与门控递归单元(GRU)集成的动态网络流量预测方法——AGCNGRU(attention mechanism for GCNGRU model)。借助GCN捕获网络中节点之间的流量空间依赖性和GRU捕获流量经过网络中各节点的时间依赖性,通过时间注意力机制设计每个隐藏状态的权重,以调整时间点流量信息的重要性,同时通过数据驱动空间注意力机制动态自适应调整Laplace矩阵,实现动态提取网络信息数据时空相关性,最终完成动态流量精准预测。在GEANT的数据集上的实验表明,所提出的方法在均方误差方面比GCNGRU减少24.8%,比GRU减少66.4%,并通过与传统路由算法OSPF、DDPG算法比较,在90%的流量负载强度下,网络性能比OSPF提升了24%,比DDPG提升了8.1%,进一步说明了AGCNGRU算法网络流量准确预测带来的时效性和有效性。
