Received March 5, 2019, accepted March 29, 2019, date of publication April 11, 2019, date of current version October 16, 2019. Digital Object Identifier 10.1109/ACCESS.2019.2910225 Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells HARBIL ARREGUI 1 , ANDONI MUJIKA 1 , ESTíBALIZ LOYO 1 , GORKA VELEZ 1 , MICHAEL T. BARROS 2 , AND OIHANA OTAEGUI 1 1 Intelligent Transport System and Engineering Department, Vicomtech, 20009 San Sebastián, Spain 2 Telecommunication Software and Systems Group, Waterford Institute of Technology, X91 K0EK Waterford, Ireland Corresponding author: Harbil Arregui (harregui@vicomtech.org) This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme through the Project CogNet under Grant 671625. ABSTRACT Significant efforts have been made and are still being made on short-term traffic prediction methods, especially for highway traffic based on punctual measurements. The literature on predicting the spatial distribution of the traffic in urban intersections is, however, very limited. This paper presents a novel data-driven prediction algorithm based on random forests regression over spatiotemporal aggregated data of vehicle counts inside a grid. The proposed approach aims to estimate the future distribution of vehicle to everything (V2X) traffic demand, providing valuable input for dynamic management of radio resources in small cells. Radio access networks (RANs) working in the terahertz band and deployed in small cells are expected to meet the high-demanding data rate requirements of connected vehicles. However, terahertz frequency propagation has important limitations in outdoor scenarios, including distance propagation, high- absorption coefficients values, and low-reflection properties. More concretely, in settings such as complex road intersections, dynamic signal blockage, and shadowing effects may cause significant power losses and compromise the quality of service for some vehicles. The forthcoming network demand estimated from the regression algorithm is used to compute the losses expected due to other vehicles potentially located between the transmitter and the receiver. We conclude that our approach, which is designed from a grid-like perspective, outperforms other traffic prediction methods and the combined result of these predictions with a dynamic reflector orientation algorithm, as a use case application, allows reducing the ratio of vehicles that do not receive minimum signal power. INDEX TERMS Wireless networks, vehicular and wireless technologies, radio access networks, intelligent transportation systems, antennas and propagation. I. INTRODUCTION Current trends in Intelligent Transport Systems (ITS) are making use of the forthcoming full vehicle to every- thing (V2X) capabilities. New technologies such as 5G are under intensive research, to offer ultra-high data rates (minimum peak data rates of 20 Gbit/s for downlink and 10 Gbit/s for uplink) and energy-efficient communication networks in a near future [1]. Higher throughputs per device are even expected in the future beyond the fifth genera- tion (B5G) mobile networks [2]. These data rates require The associate editor coordinating the review of this manuscript and approving it for publication was Ivan Wang-Hei Ho. to operate at carrier frequencies of several gigahertz or more [3]. The terahertz frequency range, which falls at 0.1-10 THz lying between the microwaves and the infrared, is expected to satisfy the data rate requirements of next-generation wireless communication networks [4], [5]. Nevertheless, electromag- netic waves operating at this high spectrum present challeng- ing implementation implications. First and foremost, Line of Sight (LoS) must exist between the transmitter and receiver to guarantee maximum data rate. The reason is that, unlike lower frequencies with higher wavelengths, terahertz signals cannot penetrate most obstacles, regardless of their material type. Secondly, any terahertz signal reflected on a non-smooth 144408 2169-3536  2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. VOLUME 7, 2019