Accurately obtaining information of road traffic conditions is of great significance to people’s travel planning and arrangement of social shared resources, and has become a major research focus in the field of smart cities. Accurately predicting road conditions poses a huge challenge due to the complex spatial correlations and nonlinear temporal dependencies of real-time traffic networks. In this paper we propose a Multi-Range Attention-Based Dynamic Graph Convolutional Network (MRA-DGCN) to model complex traffic networks. The MRA-DGCN model uses a bicomponent modules to separate different periodicity to extract refined traffic signal. In the MRA-DGCN model, we use the adaptive spatial-temporal network block (ASTnet block), which includes dynamic graph convolution and temporal attention, to mine complex spatial correlations and nonlinear temporal dependencies, respectively. In the adaptive spatial-temporal network block, we use dynamically generated adjacency matrices instead of existing distance-based adjacency matrices to perform graph convolution operations to aggregate information between nodes during model training. Instead of hierarchically extracting spatial-temporal signal, we adopt temporal attention to capture the spatial-temporal information synchronously to improve the prediction performance. Furthermore, we propose a residual gated network to control the flow of information passed to the next hidden layer to enhance the predictive accuracy. Extensive experiments on two real-world traffic datasets, METR-LA and PeMS-BAY, show that the MRA-DGCN achieves the state-of-the-art results.

Accurate long-term traffic forecasting is of great significance to intelligent transportation systems, but long-term traffic forecasting is very challenging due to the complexity of the spatial and temporal relationship of traffic data. This paper proposes a model for prediction of traffic data. The model adopts an encoder-decoder architecture, in which the encoder and decoder are composed of a Graph Generator module, a Multi-Head Convolutional Self-Attention module, and a Dynamic Convolution Recurrent module to simulate the impact of spatial and temporal factors on traffic conditions. The encoder encodes the input features and the decoder predicts the output sequence. Between the encoder and the decoder, we use a Transformation mechanism to re-encode the traffic features to generate new sequence representations as the input of the decoder, which helps alleviate the problem of error propagation during prediction. We conduct the traffic prediction task on two real-world traffic datasets and the experimental results show that our model performs better than other baseline models.

In recent years, graph neural network has been widely used. Attention mechanism is introduced into the graph neural network to make it more applicable. Both GAT and AGNN prove that attention mechanism plays an important role in graph neural network. Attention mechanism algorithms such as gat and AGNN directly use a self-learning variable to do the point product after calculating the connection (or similarity calculation) of node and neighbor features (without further processing of the calculation results). Finally, we get an aggregation of neighbor information. A cosine similarity distance pruning algorithm based on graph attention mechanism (CDP-GA) is proposed to optimize the attention matrix of nodes and their adjacent nodes. By calculating the cosine similarity between node features and neighbor features (the feature here is obtained by linear transformation), the similarity of nodes is regarded as the distance between nodes (or the weight of edges). And we think that the aggregation degree of node information is inversely proportional to the distance between nodes (similar to the heat conduction formula). In the method, we prune the neighborhood of the node according to the cosine similarity to get the final attention coefficient matrix. In this way, the attention mechanism in the graph neural network is further refined, and the loss of aggregation neighbor information is reduced. In the experiments of three datasets, our model is compared with the experimental classification of GAT and AGNN and the experiment of correlation graph neural network algorithm. Finally, it is proved that the algorithm is better than three known datasets.

The problem of session-based recommendation model aims to predict user actions based on anonymous sessions. Although, previous models achieved promising results, there are still some problems, for example, we are unable to take into account the effects of session sequences of different lengths. Generally speaking, the effect of long sequence is not as good as that of short sequence in the same model. The reason of above is that the characteristics of different length session will vary greatly. Generally, the shorter the session, the tighter the relationship between items, and the longer the session, the more likely there are items that have no relationship with each other. So, we propose a model named session-based recommendation model based on multiple neural networks hybrid extraction feature. This model uses different feature extractor to deal with the features of long sessions and short sessions respectively. In SR-MNN, we use Graph Convolutional Network to extract the features of long session and use Recurrent Neural Network to extract the features of short session. Each session is then represented as the composition of the global preference, the initial interest of that session, and the current interest of that session using an attention network. Experiments on two real datasets show that SR-MNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.