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This paper presents a spatial-temporal deep learning network, termed ST-TrafficNet, for traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined graph structure for the complex spatial dependencies of traffic flow, and ineffectively harvest high dimensional temporal features of the traffic flow. In this paper, a novel multi-diffusion convolution block constructed by an attentive diffusion convolution and bidirectional diffusion convolution is proposed, which is capable to extract precise potential spatial dependencies. Moreover, a stacked Long Short-Term Memory (LSTM) block is adopted to capture high-dimensional temporal features. By integrating the two blocks, the ST-TrafficNet can learn the spatial-temporal dependencies of intricate traffic data accurately. The performance of the ST-TrafficNet has been evaluated on two real-world benchmark datasets by comparing it with three commonly-used methods and seven state-of-the-art ones. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) of the proposed method outperform not only the commonly-used methods, but also the state-of-the-art ones in 15 min, 30 min, and 60 min time-steps.

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In recent years, more and more researchers have focused on emotion recognition methods based on electroencephalogram (EEG) signals. However, most studies only consider the spatio-temporal characteristics of EEG and the modelling based on this feature, without considering personality factors, let alone studying the potential correlation between different subjects. Considering the particularity of emotions, different individuals may have different subjective responses to the same physical stimulus. Therefore, emotion recognition methods based on EEG signals should tend to be personalized. This paper models the personalized EEG emotion recognition from the macro and micro levels. At the macro level, we use personality characteristics to classify the individuals’ personalities from the perspective of ‘birds of a feather flock together’. At the micro level, we employ deep learning models to extract the spatio-temporal feature information of EEG. To evaluate the effectiveness of our method, we conduct an EEG emotion recognition experiment on the ASCERTAIN dataset. Our experimental results demonstrate that the recognition accuracy of our proposed method is 72.4% and 75.9% on valence and arousal, respectively, which is 10.2% and 9.1% higher than that of no consideration of personalization.

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