Abstract
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.
Highlights
There has been a rapid diversification of traffic information services, and vehicle navigation is one of the most representative services
Considering the nature of a field of traffic where the accurate prediction of low-speed traffic, such as during congestion, is critical, mean absolute percentage error (MAPE) is used as the error metric for the performance evaluation since it is highly sensitive to the prediction of relatively small values
Models based on historical speed statistics were mainly used, but they have issues when there is a change in the road environment, as well as providing low-reliability predictions
Summary
There has been a rapid diversification of traffic information services, and vehicle navigation is one of the most representative services. A navigation system receives information on the current location of a vehicle and provides information on the route and arrival time from a global positioning system (GPS). Since the user requires a high reliability for the arrival time, it is critical to increase the reliability of traffic information. In order to do so, it is important to include as much traffic information as possible for the prediction of traffic conditions ahead. Because there are roads where probe vehicles do not pass by and where speed detectors are not installed, shadow roads are somewhat inevitable. Historical statistics-based pattern data are used for these shadow roads
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