A spatio-temporal network for human activity prediction based on deep learning

• Main Authors: LI Jing, LIU Haiyan, GUO Wenyue, CHEN Xin
• Format: Article
• Language: zho
• Published: Surveying and Mapping Press 2021-04-01
• Series: Acta Geodaetica et Cartographica Sinica
• Subjects: spatial multiscale; spatio-temporal network; spatio-temporal prediction; parallel convolution; gate recurrent unit
• Online Access: http://xb.sinomaps.com/article/2021/1001-1595/2021-4-522.htm
• ISSN: 1001-1595; 1001-1595

The traditional spatio-temporal prediction methods could hardly model the complex nonlinear relationship of spatio-temporal phenomenons, thus they lack the ability to consider the influence of spatial multi-scale characteristics into the prediction results. In order to overcome this deficiency, a novel model of space-time network (MST-Net) is proposed in this paper, which transforms the regression problem of volume prediction into a discriminant model with time-space characteristics. The spatial and temporal characteristics of spatio-temporal data are extracted by multi-scale parallel convolution and gate recurrent unit respectively. Thus the extracted features are fused with the attention mechanism introduced to capture the long-term features. Finally, the prediction results can be obtained by using the full connection layers. In order to prove the reliability and validity of the model, the model is tested on two challenging social media sign-in datasets. The results indicate that the proposed model outperformed other algorithms in two prediction results evaluation indexes, namely the root mean square errors (RMSE) and mean absolute percentage errors (MAPE), which illustrate that the proposed method could achieve higher prediction accuracy and could better fit the nonlinear relationship of the space-time problem. The proposed model is suitable to predict the flow of human activities.