Vehicle Traffic Density Estimation with Deep Learning for Predicting Communication Traffic Volume by Vehicle Communication Services

Vehicle communication services via mobile networks would further increase communication traffic volume. To prevent network congestion caused by the increase in communication traffic volume, network operators expand the number of base stations by predicting the maximum communication traffic volume. Although conventional methods for predicting communication traffic volume use previous mobile traffic data, vehicle communication services lack sufficient data due to limited usage. In this paper, we attempt to predict the maximum communication traffic volume without using mobile traffic data by relying on the maximum vehicle traffic density, which represents the number of vehicles per kilometer of road, although this is challenging since the vehicle traffic density is measured on only some roads. Meanwhile, road characteristics (e.g., the number of lanes) can be obtained for all roads. Therefore, we propose a method to estimate the maximum vehicle traffic density from road characteristics using a deep learning (DL) model to predict the maximum communication traffic for all roads. The proposed method uses the maximum vehicle traffic density or the parameters of the probability density function (PDF) of vehicle traffic density as labels. Training to estimate the parameters of the PDF allows us to calculate the maximum vehicle traffic density with a limited number of measurement days of data. Experimental results showed that the proposed method is more accurate than the baseline method in estimating the maximum vehicle traffic density.