# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
## 6 Conclusions  
The challenge of uncertainty in traffic volume data persists as an unavoidable issue for next-generation traffic systems. In particular, sensor-based time series traffic volume data, which accumulate vast amounts of real-time information, consistently exhibit anomalies and missing values despite the sophistication of the collection equipment. Therefore, it is crucial to accurately assess the uncertainty of the collected data and execute appropriate correction tasks. This study applied statistical learning, machine learning, and deep learning models--all primarily utilized for time series algorithms--to develop and evaluate methods that accurately reflect the characteristics of the traffic volume data and enhance the accuracy of correcting and predicting the missing traffic volume data. The key findings of this study are summarized as follows. First, a statistical learning model, SARIMA (4,1,3)(4,0,3) 12 [9], effectively captured the periodicity, seasonality, trend, and residual characteristics of traffic volume data. Predictions were also obtained using the Prophet model, a machine-learning-based additive algorithm that performs iterative computations. The SARIMA model predicted traffic volume with an average accuracy of 85%; a comparison of actual values to predicted values exhibited a variation ranging from a minimum of 3.28% to a maximum of 14.59%. For the Prophet \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model** & **MAE** & **Comparison** \\ \hline Statistical learning & SARIMA & 0.158 & 84.18\% \\ \hline Machine learning & Prophet & 0.117 & 78.63\% \\ \hline \multirow{2}{*}{Deep learning} & LSTM & 0.128 & 80.47\% \\ \cline{2-4}  & Power-function-based LSTM model & 0.025 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative analysis of error rates among models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Actual Values** & **Existing Methods** & **Developed LSTM** \\ \hline AADT (vehicles/day) & 14,623 & 14,647 & 14,619 \\ \hline Traffic volume difference (vehicles/day) & - & 24 & 4 \\ \hline Total annual traffic volume (vehicles) & 5,337,395 & 5,346,155 & 5,335,935 \\ \hline Traffic volume difference (vehicles) & - & 8760 & 1460 \\ \hline Error rate (%) & - & 0.164 & 0.025 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy verification results of the power-function-based LSTM model. model, the performance was evaluated by deleting 10% of the data arbitrarily, revealing an MAE of 0.117, an RMSE of 0.147, and an R\({}^{2}\) of 0.914 for point 10001 and an MAE of 111.687, an RMSE of 158.050, and an R\({}^{2}\) of 0.847 for point 10004. Second, to address the long-term trends and data uncertainties evident in SARIMA and Prophet models, the performance of the LSTM model--a deep learning approach--was assessed for traffic volume prediction. The results indicated that the LSTM model outperformed the SARIMA model but was less accurate than the Prophet model. To enhance the LSTM model's performance, this study introduced an improved LSTM model using a power-function-based forget gate memory cell. This adaptation aims to reduce information loss over time and more accurately reflect long-term patterns in time series data with high dependencies. Third, the traffic volume prediction performance of the proposed power-function-based LSTM model was compared with those of the statistical learning model SARIMA, the machine learning model Prophet, and the deep learning model LSTM. The results confirmed that the proposed model achieved the lowest MAE for correcting and predicting the missing traffic volume. Therefore, the proposed power-function-based LSTM model is expected to significantly contribute to enhancing the efficiency of traffic surveys and statistics. By improving traffic volume corrections and predictions, it can help prepare accurate and reliable road traffic statistics, which are imperative for national planning and decision making.