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Open AccessArticle

Exploiting Vehicular Social Networks and Dynamic Clustering to Enhance Urban Mobility Management

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Institute of Computing (IC), University of Campinas (UNICAMP), 1251 Albert Einstein Av., Campinas, SP 13083, Brazil
2
Faculty of Business and Information Technology (FBIT), Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1H 7K4, Canada
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Authors to whom correspondence should be addressed.
Sensors 2019, 19(16), 3558; https://doi.org/10.3390/s19163558
Received: 4 July 2019 / Revised: 10 August 2019 / Accepted: 12 August 2019 / Published: 15 August 2019
(This article belongs to the Special Issue Vehicular Network Communications)
Transport authorities are employing advanced traffic management system (ATMS) to improve vehicular traffic management efficiency. ATMS currently uses intelligent traffic lights and sensors distributed along the roads to achieve its goals. Furthermore, there are other promising technologies that can be applied more efficiently in place of the abovementioned ones, such as vehicular networks and 5G. In ATMS, the centralized approach to detect congestion and calculate alternative routes is one of the most adopted because of the difficulty of selecting the most appropriate vehicles in highly dynamic networks. The advantage of this approach is that it takes into consideration the scenario to its full extent at every execution. On the other hand, the distributed solution needs to previously segment the entire scenario to select the vehicles. Additionally, such solutions suggest alternative routes in a selfish fashion, which can lead to secondary congestions. These open issues have inspired the proposal of a distributed system of urban mobility management based on a collaborative approach in vehicular social networks (VSNs), named SOPHIA. The VSN paradigm has emerged from the integration of mobile communication devices and their social relationships in the vehicular environment. Therefore, social network analysis (SNA) and social network concepts (SNC) are two approaches that can be explored in VSNs. Our proposed solution adopts both SNA and SNC approaches for alternative route-planning in a collaborative way. Additionally, we used dynamic clustering to select the most appropriate vehicles in a distributed manner. Simulation results confirmed that the combined use of SNA, SNC, and dynamic clustering, in the vehicular environment, have great potential in increasing system scalability as well as improving urban mobility management efficiency. View Full-Text
Keywords: vehicular social networks; dynamic clustering; urban mobility management; social network analysis; social network concepts; advanced traffic management system vehicular social networks; dynamic clustering; urban mobility management; social network analysis; social network concepts; advanced traffic management system
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Akabane, A.T.; Immich, R.; Pazzi, R.W.; Madeira, E.R.M.; Villas, L.A. Exploiting Vehicular Social Networks and Dynamic Clustering to Enhance Urban Mobility Management. Sensors 2019, 19, 3558.

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