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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

An adaptive and Distributed Traffic Management System using Vehicular Ad-hoc Networks

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Gomides, Thiago S. [1, 2] ; De Grande, Robson E. [2] ; de Souza, Allan M. [3, 1] ; Souza, Fernanda S. H. [1, 4] ; Villas, Leandro A. [3] ; Guidoni, Daniel L. [1, 4]
Total Authors: 6
[1] Univ Fed Sao Joao del Rei, Dept Comp Sci, Sao Joao Del Rei - Brazil
[2] Brock Univ, Dept Comp Sci, St Catharines, ON - Canada
[3] Univ Estadual Campinas, Inst Comp, Campinas - Brazil
[4] Villas, Leandro A., Univ Estadual Campinas, Inst Comp, Campinas, Brazil.Gomides, Thiago S., Univ Fed Sao Joao del Rei, Dept Comp Sci, Sao Joao Del Rei - Brazil
Total Affiliations: 4
Document type: Journal article
Source: COMPUTER COMMUNICATIONS; v. 159, p. 317-330, JUN 1 2020.
Web of Science Citations: 0

Traffic Management Systems become an important challenge for large cities due to the constant growth of vehicles. As the road mesh does not increase as well as the number of vehicles in the streets, technological solutions for the traffic congestion rise as alternative and easy-to-use applications. This work presents the ON-DEMAND: An adaptive and Distributed Traffic Management System using VANETS. The proposed solution is based on V2V communication and the local view of traffic congestion. During its displacement in a road, the vehicle monitors its traveled distance and the expected one considering a free-flow traffic condition. The difference between these measurements is used to classify a contention factor, i.e., the vehicle perception on the road traffic condition. Each vehicle uses the contention factor to classify the overall congestion level and this information is proactively disseminated to its vicinity considering an adaptive approach. In the case a vehicle does not have the necessary traffic information to estimate alternative routes, it executes a reactive traffic information knowledge discovery. The proposed solution is compared with three literature solutions, named DIVERT, PANDORA and s-NRR. Our results showed that ON-DEMAND presents better results regarding network and traffic congestion metrics. (AU)

FAPESP's process: 18/19639-5 - Solutions for intelligent and cooperative transportation systems based on urban computing
Grantee:Leandro Aparecido Villas
Support type: Regular Research Grants