## 5 Conclusions  
In response to the development of the concept of \"transportation + tourism\" vigorously promoted by the state in recent years, traffic volume is a crucial factor for tourist service areas. Traffic forecasting is a direct quantitative indicator that directly affects the benefits provided by tourism service areas. Therefore, this paper proposes to apply a theoretical model based on the combination of a convolutional neural network and a gated circulation unit to predict the traffic flow of toll station entrances before and after the spatial location of a tourist service area. A one-dimensional convolutional neural network can extract the spatial dimensional features of traffic volume at the entrance of a toll station. The gated cycle unit learns the traffic volume data in the time axis. By combining the two, the spatial and temporal characteristics of traffic flow at the entrance of a toll station can be fully explored to finally obtain the traffic volume prediction results for a targeted time. As the complexity of the model increases, the prediction error decreases. When the computing time increases, the model has better prediction results. Considering the time and error of the algorithm, the convolutional neural network model works well when the total volume  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **Algorithm Time/s** \\ \hline LSTM & 4.445 & 5.930 & 43.21 \\ GRU & 4.484 & 5.985 & 43.91 \\
1 DCNN & 4.426 & 5.677 & 22.61 \\
1 DCNN + GRU & 3.820 & 5.172 & 58.55 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Toll station 3 model comparison results.  
Figure 6: Predictive model comparison results. (**a**) Toll station 17 forecast results. (**b**) Toll station 3 forecast results.  
of traffic is low. When the total volume of traffic is high, the algorithm error is considered to be significantly lower, and the use of the combined convolutional neural network and gated recurrent cell model has good prediction results.  
As there are few cases of \"traffic + tourism\"-type service areas, it is difficult to offer full credit to the role of neural networks in predicting the traffic volume of tourist-type service areas on highways. The May and June 2017 data from 17 of the toll stations of the S62 Yu'an Expressway in Guizhou Province are proposed as a hypothetical case study of the tourist-oriented service area of the Sky Bridge. The subsequent study will obtain information on the traffic volume of the toll stations before and after the spatial location of tourist service areas, and further demonstrate the impacts of setting up tourist-oriented service areas on the additional traffic volume. At the same time, the subsequent study will pay attention to human factors to make the model more applicable.  
Conceptualization, Y.-Q.Y. Investigation, Y.-B.Z.; Methodology, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Restrictions apply to the availability of these data. Data was obtained from Guizhou Expressway Group and are available from the authors with the permission of Guizhou Expressway Group.  
The authors declare no conflict of interest.