# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
###### Abstract  
Accurate traffic volume data are crucial for effective traffic management, infrastructure development, and demand forecasting. This study addresses the challenges associated with traffic volume data collection, including, notably, equipment malfunctions that often result in missing data and inadequate anomaly detection. We have developed a deep-learning-based model to improve the reliability of predictions for annual average daily traffic volume. Utilizing a decade of traffic survey data (2010-2020) from the Korea Institute of Civil Engineering and Building Technology, we constructed a univariate time series prediction model across three consecutive sections. This model incorporates both raw and adjusted traffic volume data from 2017 to 2019, employing long short-term memory (LSTM) techniques to manage data discontinuities. A power function was integrated to simulate various error correction scenarios, thus enhancing the model's resilience to prediction inaccuracies. The performance of the model was evaluated using certain metrics, such as the mean absolute error, the root mean squared error, and the coefficient of determination, thus validating the effectiveness of the deep learning approach in refining traffic volume estimations. annual average daily traffic volume (AADT); traffic volume; long short-term memory (LSTM); power function; deep learning +
Footnote †: journal: Medical  
Continuous surveys deploy automatic vehicle classification (AVC) systems equipped with sensors that continuously record traffic data around the clock at designated points. These systems gather detailed information on the vehicle type, direction, and timing. Technologies used in continuous surveys include loop and piezoelectric sensors embedded in the roadway, artificial-intelligence-driven video analysis from CCTV footage, and vehicle license plate recognition systems that integrate CCTV with a vehicle specifications database. Among the continuous traffic volume survey methods, those utilizing buried sensors encounter several challenges. Frequent sensor damage caused by road wear from heavy vehicles often results in gaps in the traffic volume data. Similarly, video survey methods face data losses arising from equipment malfunctions and adverse weather conditions. Furthermore, the traffic volume survey equipment designated for 365-day measurement suffers from inconsistent maintenance times due to communication disruptions, controller malfunctions, and sensor damage. These factors result in prolonged periods where there are missing or abnormal data, rendering it challenging to provide accurate observed traffic volume data. Consequently, the traffic volume information collected from such continuous survey equipment may not accurately reflect the actual traffic volumes owing to a combination of technical limitations, controller defects, sensor errors, external environmental factors, and communication issues. This study aims to develop and verify a deep-learning-based traffic volume correction and prediction algorithm to enhance the reliability of high-quality traffic volume information and raw data for use in national infrastructure planning, as defined in Article 102 of the Road Act and under Article 88 of the National Transport System Efficiency Act. The spatial scope of this study encompasses 551 points where general national road traffic volume survey equipment (AVC) is installed to calculate the AADT. The temporal scope includes hourly and daily continuous traffic volume data from the past decade (2010 to 2020) collected in real-time from the AVC equipment installed on general national roads. The main objective is to correct and predict traffic volume by applying a cyclic model-based algorithm to short-cycle complex seasonal time series information while specifically addressing the issue of frequent missing data during the traffic volume data collection process.