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Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Changzhou University (Natural Science Edition) / Changzhou Daxue Xuebao (Ziran Kexue Ban) is the property of Editorial Department of Journal of Changzhou University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)