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Open AccessArticle

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

1
Convergence Laboratory, KT R&D Center, Seoul 06763, Korea
2
Department of Computer and Telecommunication Engineering, Yonsei University, Wonju-si 26493, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5327; https://doi.org/10.3390/s19235327
Received: 4 October 2019 / Revised: 21 November 2019 / Accepted: 25 November 2019 / Published: 3 December 2019
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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. View Full-Text
Keywords: road traffic prediction; LTE access data; cellular phones; long short-term memory (LSTM); deep learning road traffic prediction; LTE access data; cellular phones; long short-term memory (LSTM); deep learning
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Ji, B.; Hong, E.J. Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data. Sensors 2019, 19, 5327.

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