Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells

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 prese...

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Main Authors: Harbil Arregui, Andoni Mujika, Estibaliz Loyo, Gorka Velez, Michael T. Barros, Oihana Otaegui
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8686069/
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spelling doaj-407fb57c200c4192a78fc9c77c42d7092021-04-05T17:23:38ZengIEEEIEEE Access2169-35362019-01-01714440814442410.1109/ACCESS.2019.29102258686069Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small CellsHarbil Arregui0https://orcid.org/0000-0002-7934-9250Andoni Mujika1Estibaliz Loyo2Gorka Velez3Michael T. Barros4https://orcid.org/0000-0002-9765-7660Oihana Otaegui5Intelligent Transport System and Engineering Department, Vicomtech, San Sebastián, SpainIntelligent Transport System and Engineering Department, Vicomtech, San Sebastián, SpainIntelligent Transport System and Engineering Department, Vicomtech, San Sebastián, SpainIntelligent Transport System and Engineering Department, Vicomtech, San Sebastián, SpainTelecommunication Software and Systems Group, Waterford Institute of Technology, Waterford, IrelandIntelligent Transport System and Engineering Department, Vicomtech, San Sebastián, SpainSignificant 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.https://ieeexplore.ieee.org/document/8686069/Wireless networksvehicular and wireless technologiesradio access networksintelligent transportation systemsantennas and propagation
collection DOAJ
language English
format Article
sources DOAJ
author Harbil Arregui
Andoni Mujika
Estibaliz Loyo
Gorka Velez
Michael T. Barros
Oihana Otaegui
spellingShingle Harbil Arregui
Andoni Mujika
Estibaliz Loyo
Gorka Velez
Michael T. Barros
Oihana Otaegui
Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells
IEEE Access
Wireless networks
vehicular and wireless technologies
radio access networks
intelligent transportation systems
antennas and propagation
author_facet Harbil Arregui
Andoni Mujika
Estibaliz Loyo
Gorka Velez
Michael T. Barros
Oihana Otaegui
author_sort Harbil Arregui
title Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells
title_short Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells
title_full Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells
title_fullStr Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells
title_full_unstemmed Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells
title_sort short-term vehicle traffic prediction for terahertz line-of-sight estimation and optimization in small cells
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description 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.
topic Wireless networks
vehicular and wireless technologies
radio access networks
intelligent transportation systems
antennas and propagation
url https://ieeexplore.ieee.org/document/8686069/
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AT estibalizloyo shorttermvehicletrafficpredictionforterahertzlineofsightestimationandoptimizationinsmallcells
AT gorkavelez shorttermvehicletrafficpredictionforterahertzlineofsightestimationandoptimizationinsmallcells
AT michaeltbarros shorttermvehicletrafficpredictionforterahertzlineofsightestimationandoptimizationinsmallcells
AT oihanaotaegui shorttermvehicletrafficpredictionforterahertzlineofsightestimationandoptimizationinsmallcells
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