## 5 Conclusions  
In this study, the KNN-BILSTM combined model is used to predict the traffic flow of expressway sections. Considering the spatio-temporal characteristics of traffic flow data, the KNN model is selected to check the spatial correlation of the detection points of the road section. By selecting different K values, the correlation between the detection points of the target road section is sorted, and the two-way propagation characteristics of the BILSTM model are used to fully consider the change law of forward and backward traffic flow. When analyzing the time series of road section detection points, the effect is better than the LSTM model that only considers forward propagation. Sequence training and obtained prediction results are compared with SVR, LSTM, GRU, KNN-LSTM, and CNN-LSTM models. The experimental results prove that the model proposed in this study  
Figure 11: Comparison of errors in prediction results of the different models. (**a**) Used a day’s data sample for prediction. (**b**) Used a total of 8 samples of 40–47 from Figure (**a**) for amplification.  
has good prediction accuracy. In this study, by predicting the traffic flow of expressways, managers can obtain effective information in a short time and control and guide traffic conditions promptly, thereby effectively alleviating traffic congestion. As a time series prediction model, the model is also applicable to other time series traffic forecasts, such as urban taxi traffic forecasts and subway crowd traffic forecasts.  
Although the prediction effect of the model proposed in this paper is good, it also has some limitations. First of all, the prediction target of this paper is only single-feature prediction, and the impact analysis of weather factors and traffic accidents is lacking. Second, the model in this paper still predicts using Euclidean spatial data, and in the actual traffic scene, non-European spatial data prediction may be closer to life. Therefore, in future research work, we will add other influencing factors of traffic flow to predict the first problem to make the model better. Weather factors, traffic speed, road blockage, and other factors will make the prediction problem more complex and accurate. For the second problem, we will consider using a new model to predict the traffic flow of non-European spatial data, such as a graph convolution neural network. Therefore, we plan to propose a model that can accurately deal with data lost due to influencing factors.  
[PERSON] designed the ideas, frameworks, and approach and edited the draft of the manuscript. [PERSON] edited the draft of the manuscript, and [PERSON] contributed to its modification. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Social Science Foundation of China (22 BGL007), Fujian Zhi-lian-yun Supply Chain Technology and Economy Integration Service Platform from the Fujian Association for Science and Technology, the Fujian-Kenya Silk Road Cloud Joint R&D Center (2021D021) from the Fujian Provincial Department of Science and Technology, the Fujian Social Sciences Federation Planning Project (FJ2021Z006), the General Program of the Fujian Natural Science Foundation (2022)01941), and the 2022 Scientific and Technological Innovation Think Tank Project of the Fujian Association for Science and Technology (FJKX-2022 XKB023).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The traffic flow data used to support the findings of this study are available from the corresponding author upon request.  
The authors would like to thank the reviewers and editors for improving this manuscript.  
The authors declare no conflict of interest.