# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
## 2 Review of Related Studies
### Traffic Volume Correction and Methods Based on Historical Data  
In Texas and Georgia, USA, the missing rates of traffic volume collection equipment have been reported at approximately 15-90% and 5-14%, respectively. In Alberta, Canada, an analysis over seven years revealed average missing rates ranging from 10% to 40% [1; 2]. Although several studies have attempted to address these discrepancies, the methods predominantly rely on historical data from the same locations. These statistical techniques and algorithms, although useful, cannot fully eliminate the uncertainty associated with traffic volume. Relying on historical data for traffic volume correction is time-consuming and often ineffective when traffic patterns undergo significant changes. Moreover, if the reliability of the historical traffic volume data is questionable, corrections based on subjective analyst judgment or experience can lead to distorted interpretations of the time series data.  
[PERSON] et al. [3] enhanced short-term traffic volume prediction by integrating historical data with transition matrices and proposing an absorbing Markov chain (AMC) model. In terms of prediction accuracy, their findings suggest that the AMC model outperforms both the seasonal autoregressive integrated moving average (ARIMA) and neural network models.  
[PERSON] et al. [4] developed an algorithm designed to eliminate outliers and correct missing values in the toll collection system data. They determined the algorithm's applicability to various traffic flow scenarios across different sections of highways during weekdays, holidays, and special event periods.  
[PERSON] et al. [5] implemented traffic volume corrections using profile-based mean correction, regression correction with time-specific data from similar detectors, and autoregression with complete daily traffic data from similar detectors through significance tests of monthly and daily traffic volume change patterns, comparisons of similar traffic volume change patterns, and cluster analysis of daily average traffic volume change patterns.  
[PERSON] et al. [6] reported that historical-data-based correction methods were more accurate than the adjacent point reference method. Among three techniques using historical data, applying the same period's historical data, regression analysis, and lane usage rates, the replacement of historical data from the same period and regression analysis of front and rear points presented the most reliable estimates.  
[PERSON] and [PERSON] [7] suggested correcting the missing traffic volumes by calculating the axle correction coefficients derived from the cumulative number of axles detected by piezo sensors. This method exhibited a lower error rate than the spatial trend correction method for specific points.  
[PERSON] et al. [8] developed an autoregressive analysis method using traffic volume data from the same time as the target for correction and a seasonal time series analysis correction method using data from the same point as the target. Their findings indicated that the autoregressive method, which considers the current time points, presented fewer errors when compared to the time series method, indicating that present conditions influence the traffic volumes more significantly than historical data.