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Open AccessFeature PaperArticle

A Centralized Route-Management Solution for Autonomous Vehicles in Urban Areas

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Department of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, Spain
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Institute of Multidisciplinary Mathematics (IMM), Universitat Politècnica de València, 46022 Valencia, Spain
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ITACA Institute. Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain
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LAAS/CNRS—Groupe OLC, Université Paul Sabatier-Toulouse III, 31031 Toulouse, France
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Author to whom correspondence should be addressed.
Electronics 2019, 8(7), 722; https://doi.org/10.3390/electronics8070722
Received: 23 May 2019 / Revised: 19 June 2019 / Accepted: 20 June 2019 / Published: 26 June 2019
(This article belongs to the Special Issue Smart, Connected and Efficient Transportation Systems)
Currently, one of the main challenges that large metropolitan areas must face is traffic congestion. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, environmental pollution. By properly analyzing traffic demand, it is possible to predict future traffic conditions, using this information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution, thereby improving the traffic flow in a city in a fully centralized manner. This paper represents a step forward towards this novel traffic management paradigm by proposing a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. We perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed traffic prediction equation, combined with frequent updating of traffic conditions on the route server, can achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity. View Full-Text
Keywords: autonomous vehicle; traffic prediction; SUMO; route server; DFROUTER; intelligent transportation system autonomous vehicle; traffic prediction; SUMO; route server; DFROUTER; intelligent transportation system
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Zambrano-Martinez, J.L.; Calafate, C.T.; Soler, D.; Lemus-Zúñiga, L.-G.; Cano, J.-C.; Manzoni, P.; Gayraud, T. A Centralized Route-Management Solution for Autonomous Vehicles in Urban Areas. Electronics 2019, 8, 722.

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