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

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, env...

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Bibliographic Details
Published in:Electronics (Basel) 2019-07, Vol.8 (7), p.722
Main Authors: Zambrano-Martinez, Jorge, Calafate, Carlos, Soler, David, Lemus-Zúñiga, Lenin-Guillermo, Cano, Juan-Carlos, Manzoni, Pietro, Gayraud, Thierry
Format: Article
Language:English
Subjects:
Algorithms
Autonomous vehicles
Control theory
Demand analysis
Driving conditions
Heuristic
Kalman filters
Network management systems
Neural networks
Optimization
Performance evaluation
Probability distribution
Servers
Software
Traffic congestion
Traffic control
Traffic flow
Traffic information
Traffic management
Travel
Travel time
Urban areas
Vehicles
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics8070722