# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 5 Conclusions  
This study proposes a traffic information detection method at a single intersection based on Wi-Fi data. 1. The hardware architecture of Wi-Fi data acquisition equipment is determined to realize the collection of Wi-Fi data at a single intersection. 2. This study combined MAC address matching, k-means clustering, sliding time window, and other algorithms to process and analyze the Wi-Fi data of a single intersection and realize the detection of the spatial average speed of the entrance and exit sections of a single intersection and the vehicle turning proportion information of the entrance section. 3. The function of a single-point intersection traffic information detection scheme based on Wi-Fi data acquisition equipment is expanded. The traffic data prediction model of the intersection inlet section based on the LSTM neural network is constructed, and the training of the prediction model is completed in the Keras framework so as to predict the spatial average speed and vehicle turning ratio of the intersection inlet section. This study shows the error evaluation of the spatial average speed and the left-turn ratio prediction model of the inlet road section in each direction under the case verification. For different data sets, the prediction error evaluation is low. The error evaluation of the prediction results of the model trained by using the reconstructed samples only considers the data of the inlet road section, and the reconstructed samples in this paper are compared. The MAPE and RMSE of the model prediction results of the reconstructed samples in this paper are generally smaller, indicating that the prediction effect is better and that the proposed method is effective. The data collected in this study were obtained on sunny working days, which has certain limitations. Further research will be conducted to expand to the following aspects: \begin{table}
\begin{tabular}{c c c c c} \hline
**Error** & **North** & **South** & **East** & **West** \\ \hline RMSE (Textual reconstruction method) & 0.01713 & 0.01642 & 0.02594 & 0.02041 \\ MAPE (Textual reconstruction method) & 12.8634 & 10.07899 & 10.1695 & 11.9669 \\ RMSE & 0.017605 & 0.021753 & 0.02827 & 0.02743 \\ MAPE & 13.69658 & 13.15832 & 10.5006 & 12.2864 \\ \hline \end{tabular}
\end{table} 
Table 4: Statistical table of error evaluation of vehicle left steering ratio prediction model. \begin{table}
\begin{tabular}{c c c c c} \hline
**Error** & **North** & **South** & **East** & **West** \\ \hline RMSE (Textual reconstruction method) & 0.64729 & 0.93593 & 0.99953 & 0.958572 \\ MAPE (Textual reconstruction method) & 2.79431 & 3.26728 & 3.41611 & 3.6238 \\ RMSE & 0.66443 & 1.067952 & 1.25264 & 0.72434 \\ MAPE & 3.10929 & 3.50150 & 4.48097 & 2.93503 \\ \hline \end{tabular}
\end{table} 
Table 5: Statistical table of error evaluation of the space mean speed prediction model of the road entrance section. 1. Taking into account the influence of the surrounding environment, in our subsequent research, we will adopt complementary technologies to expand the research scope to multiple intersections and also consider the impact of complex environments (different days, seasons, weather conditions, etc.) on the collection of Wi-Fi signals. 2. Multi-step prediction and sliding-window forecasting approaches are the research directions for us in the future. Conceptualization, Q.W. and S.L. ; methodology, Q.W. and J.F. ; software, J.F. ; formal analysis, Q.W., J.F. and S.L. ; data curation, Q.W., J.F. and S.L. ; writing--original draft preparation, Q.W. ; writing--review and editing, Q.W. and S.L. ; supervision, S.L. ; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China grant funded by the Ministry of Science and Technology of the People's Republic of China (MOST) (No. 2021 YFA1000303), the Shaanxi Provincial Natural Science Foundation of China grant funded by the Shaanxi Provincial Department of Science and Technology (No.