As urban traffic pollution continues to increase, there is an urgent need to build traffic emission monitoring and forecasting system for the urban traffic construction. The traffic emission monitoring and forecasting system's core is the prediction of traffic emission's evolution. And the traffic flow prediction on the urban road network contributes greatly to the prediction of traffic emission's evolution. Due to the complex non-Euclidean topological structure of traffic networks and dynamic heterogeneous spatial-temporal correlations of traffic conditions, it is difficult to obtain satisfactory prediction results with less computation cost. To figure these issues out, a novel deep learning traffic flow forecasting framework is proposed in this paper, termed as Ensemble Attention based Graph Time Convolutional Networks (EAGTCN). More specifically, each component of our model contains two major blocks: (1) the global spatial patterns are captured by the spatial blocks which are fused by the Graph Convolution Network (GCN) and spatial ensemble attention layer; (2) the temporal patterns are captured by the temporal blocks which are composed by the Time Convolution Net (TCN) and temporal ensemble attention layers. Experiments on two real-world datasets demonstrate that our model obtains more accurate prediction results than the state-of-the-art baselines at less computation expense especially in the long-term prediction situation.
How to accurately predict Short-term traffic travel time is an important problem in Intelligent Transportation Systems. However, the traffic data usually exhibit high nonlinearities and complex patterns. Predicting traffic travel time is a challenge. Most previous studies use the topological adjacency of road networks to explore the spatial correlations. However, as a real network, the road network contains higher-order connectivity patterns, which have different statistical significance. The topology adjacency cannot reflect these higher-order connectivity patterns. To obtain topological adjacency and higher-order connection pattern information, a novel deep learning framework was proposed: Multiple Motifs Graph Convolutional Recurrent Neural Networks, for traffic travel time prediction in this paper. The accuracy of travel time prediction can be improved by the proposed model. To be more specific, there are two meaning blocks in each unit of the model: (1) The spatial blocks captured spatial patterns information by the Multi-Motif graph convolution network and Motif Graph embedding; (2) The temporal blocks captured temporal patterns information by the combination of LSTM and the FC layer. To prove the effectiveness and accuracy of the prediction model, experiments were conducted on real world traffic travel time datasets.
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