Alicante-Murcia Freeway Scenario: A High-Accuracy and Large-Scale Traffic Simulation Scenario Generated Using a Novel Traffic Demand Calibration Method in SUMO

The design, testing and optimization of Vehicle to Everything (V2X), connected and automated driving and Intelligent Transportation Systems (ITS) and technologies requires mobility traces and traffic simulation scenarios that can faithfully characterize the vehicular mobility at the macroscopic and...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Juan Jesus Gonzalez-Delicado, Javier Gozalvez, Jesus Mena-Oreja, Miguel Sepulcre, Baldomero Coll-Perales
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Traffic simulation freeway large-scale SUMO V2X CAVs Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/8ffa382dcf22473e894114674b6c6076
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8ffa382dcf22473e894114674b6c6076
record_format	dspace
spelling	oai:doaj.org-article:8ffa382dcf22473e894114674b6c60762021-11-25T00:00:58ZAlicante-Murcia Freeway Scenario: A High-Accuracy and Large-Scale Traffic Simulation Scenario Generated Using a Novel Traffic Demand Calibration Method in SUMO2169-353610.1109/ACCESS.2021.3126269https://doaj.org/article/8ffa382dcf22473e894114674b6c60762021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606704/https://doaj.org/toc/2169-3536The design, testing and optimization of Vehicle to Everything (V2X), connected and automated driving and Intelligent Transportation Systems (ITS) and technologies requires mobility traces and traffic simulation scenarios that can faithfully characterize the vehicular mobility at the macroscopic and microscopic levels under large-scale and complex scenarios. The generation of accurate scenarios and synthetic traces requires a precise modelling approach, and the possibility to validate them against real-world measurements that are generally not available for large-scale scenarios. This limits the open availability of realistic and large-scale traffic simulation scenarios. The purpose of this paper is to present a large-scale and high-accuracy traffic simulation scenario. The scenario has been implemented over the open-source SUMO traffic simulator and is openly released to the community. The scenario accurately models the traffic flow, the traffic speed and the road’s occupancy for 9 full days of traffic over a 97 km freeway section. The scenario models mixed traffic with light and heavy vehicles. The simulation scenario has been calibrated using a unique dataset provided by the Spanish road authority and a novel learning-based and iterative traffic demand calibration technique for SUMO. This technique, referred to as Clone Feedback, is proposed for the first time in this paper and does not require a pre-calibration to generate realistic traffic demand. Clone Feedback can generate calibrated mixed traffic (light and heavy vehicles) using as input only traffic flow measurements. The results obtained show that Clone Feedback outperforms two reference techniques for calibrating the traffic demand in SUMO.Juan Jesus Gonzalez-DelicadoJavier GozalvezJesus Mena-OrejaMiguel SepulcreBaldomero Coll-PeralesIEEEarticleTraffic simulationfreewaylarge-scaleSUMOV2XCAVsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154423-154434 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Traffic simulation freeway large-scale SUMO V2X CAVs Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Traffic simulation freeway large-scale SUMO V2X CAVs Electrical engineering. Electronics. Nuclear engineering TK1-9971 Juan Jesus Gonzalez-Delicado Javier Gozalvez Jesus Mena-Oreja Miguel Sepulcre Baldomero Coll-Perales Alicante-Murcia Freeway Scenario: A High-Accuracy and Large-Scale Traffic Simulation Scenario Generated Using a Novel Traffic Demand Calibration Method in SUMO
description	The design, testing and optimization of Vehicle to Everything (V2X), connected and automated driving and Intelligent Transportation Systems (ITS) and technologies requires mobility traces and traffic simulation scenarios that can faithfully characterize the vehicular mobility at the macroscopic and microscopic levels under large-scale and complex scenarios. The generation of accurate scenarios and synthetic traces requires a precise modelling approach, and the possibility to validate them against real-world measurements that are generally not available for large-scale scenarios. This limits the open availability of realistic and large-scale traffic simulation scenarios. The purpose of this paper is to present a large-scale and high-accuracy traffic simulation scenario. The scenario has been implemented over the open-source SUMO traffic simulator and is openly released to the community. The scenario accurately models the traffic flow, the traffic speed and the road’s occupancy for 9 full days of traffic over a 97 km freeway section. The scenario models mixed traffic with light and heavy vehicles. The simulation scenario has been calibrated using a unique dataset provided by the Spanish road authority and a novel learning-based and iterative traffic demand calibration technique for SUMO. This technique, referred to as Clone Feedback, is proposed for the first time in this paper and does not require a pre-calibration to generate realistic traffic demand. Clone Feedback can generate calibrated mixed traffic (light and heavy vehicles) using as input only traffic flow measurements. The results obtained show that Clone Feedback outperforms two reference techniques for calibrating the traffic demand in SUMO.
format	article
author	Juan Jesus Gonzalez-Delicado Javier Gozalvez Jesus Mena-Oreja Miguel Sepulcre Baldomero Coll-Perales
author_facet	Juan Jesus Gonzalez-Delicado Javier Gozalvez Jesus Mena-Oreja Miguel Sepulcre Baldomero Coll-Perales
author_sort	Juan Jesus Gonzalez-Delicado
title	Alicante-Murcia Freeway Scenario: A High-Accuracy and Large-Scale Traffic Simulation Scenario Generated Using a Novel Traffic Demand Calibration Method in SUMO
title_short	Alicante-Murcia Freeway Scenario: A High-Accuracy and Large-Scale Traffic Simulation Scenario Generated Using a Novel Traffic Demand Calibration Method in SUMO
title_full	Alicante-Murcia Freeway Scenario: A High-Accuracy and Large-Scale Traffic Simulation Scenario Generated Using a Novel Traffic Demand Calibration Method in SUMO
title_fullStr	Alicante-Murcia Freeway Scenario: A High-Accuracy and Large-Scale Traffic Simulation Scenario Generated Using a Novel Traffic Demand Calibration Method in SUMO
title_full_unstemmed	Alicante-Murcia Freeway Scenario: A High-Accuracy and Large-Scale Traffic Simulation Scenario Generated Using a Novel Traffic Demand Calibration Method in SUMO
title_sort	alicante-murcia freeway scenario: a high-accuracy and large-scale traffic simulation scenario generated using a novel traffic demand calibration method in sumo
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8ffa382dcf22473e894114674b6c6076
work_keys_str_mv	AT juanjesusgonzalezdelicado alicantemurciafreewayscenarioahighaccuracyandlargescaletrafficsimulationscenariogeneratedusinganoveltrafficdemandcalibrationmethodinsumo AT javiergozalvez alicantemurciafreewayscenarioahighaccuracyandlargescaletrafficsimulationscenariogeneratedusinganoveltrafficdemandcalibrationmethodinsumo AT jesusmenaoreja alicantemurciafreewayscenarioahighaccuracyandlargescaletrafficsimulationscenariogeneratedusinganoveltrafficdemandcalibrationmethodinsumo AT miguelsepulcre alicantemurciafreewayscenarioahighaccuracyandlargescaletrafficsimulationscenariogeneratedusinganoveltrafficdemandcalibrationmethodinsumo AT baldomerocollperales alicantemurciafreewayscenarioahighaccuracyandlargescaletrafficsimulationscenariogeneratedusinganoveltrafficdemandcalibrationmethodinsumo
_version_	1718414706923798528

Alicante-Murcia Freeway Scenario: A High-Accuracy and Large-Scale Traffic Simulation Scenario Generated Using a Novel Traffic Demand Calibration Method in SUMO

Ejemplares similares