Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data

Sensors (Basel). 2019 Dec 3;19(23):5327. doi: 10.3390/s19235327.


In this paper, we propose a method for deep-learning-based real-time road traffic predictions using long-term evolution (LTE) access data. The proposed system generates a road traffic speed learning model based on road speed data and historical LTE data collected from a plurality of base stations located within a predetermined radius from the road. Real-time LTE data were the input for the generated learning model in order to predict the real-time speed of traffic. Since the system was developed using a time-series-based road traffic speed learning model based on LTE data from the past, it is possible for it to be used for a road where the environment has changed. Moreover, even on roads where the collection of traffic data is invalid, such as a radio shadow area, it is possible to directly enter real-time wireless communications data into the traffic speed learning model to predict the traffic speed on the road in real time, and in turn, raise the accuracy of real-time road traffic predictions.

Keywords: LTE access data; cellular phones; deep learning; long short-term memory (LSTM); road traffic prediction.