## 6 Conclusions  
In this paper, we propose a traffic flow prediction model based on a local-global features fusion temporal convolutional network. First, we used a forward and backward graph convolutional network to obtain spatial local features, and we used a probabilistic sparse self-attention mechanism to efficiently extract spatial global features. Then, we used a multichannel temporal convolutional network to extract the temporal feature, and fine-grained extraction could be achieved by making each channel correspond to a node. Finally, the prediction result was obtained through the prediction module. Extensive experiments demonstrate the overall better performance of our model, and ablation experiments demonstrate that each module plays a role in prediction.  
Figure 7: Comparison of ablation experiment results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**FENS03**} & \multicolumn{2}{c}{**FENS04**} & \multicolumn{2}{c}{**FENS07**} & \multicolumn{2}{c}{**FENS08**} \\  & **MAE** & **MAPE(\%)** & **RMSE** & **MAE** & **MAPE(\%)** & **RMSE** & **MAE** & **MAPE(\%)** & **RMSE** \\ \hline LGTCN & **15.14** & **14.73** & **24.00** & **19.77** & **15.36** & **31.75** & **22.88** & **10.56** & **35.47** & **16.27** & **10.31** & **25.41** \\ NO-PSA & 17.14 & 15.67 & 26.90 & 20.47 & 15.72 & 32.44 & 25.26 & 11.97 & 38.52 & 16.90 & 11.20 & 26.25 \\ NO-MCTCN & 17.31 & 17.05 & 27.54 & 23.61 & 17.17 & 36.66 & 25.98 & 11.89 & 39.77 & 18.89 & 11.58 & 29.08 \\ NO-PM & 18.15 & 17.50 & 30.69 & 24.28 & 17.87 & 38.18 & 29.00 & 12.84 & 45.00 & 19.66 & 12.38 & 30.63 \\ NO-RES & 18.45 & 18.73 & 31.53 & 21.60 & 19.51 & 34.80 & 28.36 & 12.80 & 47.35 & 20.20 & 12.73 & 31.77 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment.  
In the future, we will study the simultaneous extraction of spatially local and global features. At the same time, due to the cross-correlation operation of the multichannel convolution output, the model could not realize the independent extraction of complete temporal features, which inspires us to construct a series of independent time extraction sub-modules in the future. In addition, we need to embed real-time data from multiple sources (e.g., weather) into the model to further improve the prediction accuracy of the model. We also hope to implement the model in more urban spatio-temporal data mining areas, such as parking demand analysis. Downstream applications, including congestion information dissemination system, dynamic navigation, etc., are also yet to be developed.  
Conceptualization, W.Y. and D.Z.; methodology, W.Y. and D.Z.; software, W.Y., H.K. and D.Z.; validation, W.Y., D.Z., H.K. and K.D.; formal analysis, W.Y. and J.L.; investigation, W.Y. and D.Z.; resources, D.Z. and J.L.; data curation, W.Y., H.K. and K.D.; writing--original draft preparation, W.Y.; writing--review and editing, W.Y., H.K., K.D., D.Z. and J.L.; visualization, W.Y., H.K. and K.D.; supervision, D.Z.; project administration, D.Z.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangdong Key Areas R&D Program. Grant number No. 2019B090913001.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the China Mobile Internet Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.