Search

Most of the existing traffic flow prediction algorithms only consider the prediction under normal conditions, but not the influence of weather attributes and surrounding geographical attributes on the prediction results, This paper proposed a combined prediction model（A-STIGCN） integrating external attributes. First, the external attributes are taken as the attributes of the sections in the road network, and the attributes and traffic characteristics of the sections are also put under modeling to obtain the enhanced feature vectors. Secondly, the method used graph wavelet transform and adaptive matrix to extract the local and global spatial feature information of the traffic flow respectively, with the help of the gating cycle unit（GRU） to extract the temporal information. Finally, the temporal dynamic variability of the attention mechanism is captured to predict the traffic flow. Shenzhen taxi trajectory data, corresponding weather data and POI data for prediction, the research results show that A-STIGCN combination model is better than the traditional linear model and variant model, compared with the ASTGCN model without introducing attention mechanism, MAE reduced about 0.131, accuracy improved 0.068, and the TGCN model without introducing external factors, MAPE. The reduction by about 0.637% and the improved accuracy by 0.079 provides better guidance for traffic management. [ABSTRACT FROM AUTHOR]