Next Article in Journal
Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array
Previous Article in Journal
A Separated Receptor/Transducer Scheme as Strategy to Enhance the Gas Sensing Performance Using Hematite–Carbon Nanotube Composite
Previous Article in Special Issue
Exploiting Vehicular Social Networks and Dynamic Clustering to Enhance Urban Mobility Management
Open AccessArticle

Towards a Fog-Enabled Intelligent Transportation System to Reduce Traffic Jam

1
Federal Rural University of Pernambuco (UFRPE), UAST, Gregorio Ferraz Nogueira Av., Serra Talhada, Pernambuco 56909–535, Brazil
2
Institute of Computing, University of Campinas (UNICAMP), 1251 Albert Einstein Av., Campinas SP 13083, Brazil
3
Department of Computer Science, University of Brasília, Distrito Federal 70000-000, Brazil
4
Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
5
Department of Computer Science, Pontifical Catholic University of Minas Gerais (PUC Minas), Belo Horizonte 30535–901, Brazil
6
Department of Computer Science, Federal University of São João del-Rei, São João del-Rei 36301-360, Brazil
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(18), 3916; https://doi.org/10.3390/s19183916
Received: 5 August 2019 / Revised: 31 August 2019 / Accepted: 2 September 2019 / Published: 11 September 2019
(This article belongs to the Special Issue Vehicular Network Communications)
Frustrations, monetary losses, lost time, high fuel consumption and CO 2 emissions are some of the problems caused by traffic jams in urban centers. In an attempt to solve this problem, this article proposes a traffic service to control congestion, named FOXS–Fast Offset Xpath Service. FOXS aims to reduce the problems generated by a traffic jam in a distributed way through roads classification and the suggestion of new routes to vehicles. Unlike the related works, FOXS is modeled using the Fog computing paradigm. Therefore, it is possible to take advantage of the inherent aspects of this paradigm, such as low latency, processing load balancing, scalability, geographical correlation and the reduction of bandwidth usage. In order to validate FOXS, our performance evaluation considers two realistic urban scenarios with different characteristics. When compared with related works, FOXS shows a reduction in stop time by up to 70%, the CO 2 emissions by up to 29% and, the planning time index by up to 49%. When considering communication evaluation metrics, FOXS reaches a better result than other solutions on the packet collisions metric (up to 11.5%) and on the application delay metric (up to 30%). View Full-Text
Keywords: vehicular networks; fog computing; intelligent transport system; mobile edge computing vehicular networks; fog computing; intelligent transport system; mobile edge computing
Show Figures

Graphical abstract

MDPI and ACS Style

Brennand, C.A.R.L.; Filho, G.P.R.; Maia, G.; Cunha, F.; Guidoni, D.L.; Villas, L.A. Towards a Fog-Enabled Intelligent Transportation System to Reduce Traffic Jam. Sensors 2019, 19, 3916.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop