Traffic flow forecasting has become a key technology to alleviate urban congestion. The core challenge is to accurately model the spatial-temporal coupling correlation in data. At present, many methods have made great progress, but most methods still face two important challenges: (i) The internal periodic pattern of traffic flow has not been accurately modeled. (ii) Failure to adapt effectively to the dynamic nature of traffic flow data. Accurate modeling of periodic patterns helps uncover the evolution of traffic flow, while improved adaptability to dynamic topologies enhances the capture of changing spatial dependencies. Therefore, this paper proposes a Periodic-Aware Time-Varying Topological Network (PATVTN) to enhance the precision of traffic flow prediction. In particular, PATVTN employs a Period-Aware Time Encoder to capture periodic traffic patterns, and introduces a Time-Varying Topological Space Encoder to adapt to the time-varying topological structure. Experiments conducted on four real-world traffic datasets show that our model consistently outperforms existing state-of-the-art methods.

In order to cope with the increasingly severe global energy conservation and emission reduction problems, research on urban carbon emission prediction is of great significance. The existing methods mainly use time series analysis to predict urban carbon emission, but there is a strong spatial correlation between the carbon emission data of several cities. Therefore, this paper designs a multi-scale spatial-temporal feature screening attention network to predict target cities' future carbon emission data. Firstly, this paper combines the daily carbon emission data of the near-neighbouring cities and the daily homologous emission data of the target city to analyze the urban carbon emission data from a spatio-temporal perspective. Then, this paper designs a multi-scale spatial interactive convolution module and a multi-scale temporal convolution module to extract multi-scale spatio-temporal features effectively. In addition, the feature screening module is designed to reduce the adverse effects of redundant features. Finally, the multi-scale features are used to predict the future carbon emissions of the target city through a predictor. The experimental results show that our prediction model is superior to the existing methods in six datasets.