Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles

Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the res...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Le Zhang, Mohamed Khalgui, Zhiwu Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	traffic congestion traffic signal control vehicle route guidance Internet of Vehicles Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/08ef779af5704ff0ae20dc54641faa1f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:08ef779af5704ff0ae20dc54641faa1f
record_format	dspace
spelling	oai:doaj.org-article:08ef779af5704ff0ae20dc54641faa1f2021-11-11T19:16:35ZPredictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles10.3390/s212173301424-8220https://doaj.org/article/08ef779af5704ff0ae20dc54641faa1f2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7330https://doaj.org/toc/1424-8220Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension’s decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality.Le ZhangMohamed KhalguiZhiwu LiMDPI AGarticletraffic congestiontraffic signal controlvehicle route guidanceInternet of VehiclesChemical technologyTP1-1185ENSensors, Vol 21, Iss 7330, p 7330 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	traffic congestion traffic signal control vehicle route guidance Internet of Vehicles Chemical technology TP1-1185
spellingShingle	traffic congestion traffic signal control vehicle route guidance Internet of Vehicles Chemical technology TP1-1185 Le Zhang Mohamed Khalgui Zhiwu Li Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles
description	Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension’s decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality.
format	article
author	Le Zhang Mohamed Khalgui Zhiwu Li
author_facet	Le Zhang Mohamed Khalgui Zhiwu Li
author_sort	Le Zhang
title	Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles
title_short	Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles
title_full	Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles
title_fullStr	Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles
title_full_unstemmed	Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles
title_sort	predictive intelligent transportation: alleviating traffic congestion in the internet of vehicles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/08ef779af5704ff0ae20dc54641faa1f
work_keys_str_mv	AT lezhang predictiveintelligenttransportationalleviatingtrafficcongestionintheinternetofvehicles AT mohamedkhalgui predictiveintelligenttransportationalleviatingtrafficcongestionintheinternetofvehicles AT zhiwuli predictiveintelligenttransportationalleviatingtrafficcongestionintheinternetofvehicles
_version_	1718431566548434944

Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles

Ejemplares similares