Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mob...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 23; H. 1; S. 499
Hauptverfasser: Peyman, Mohammad, Fluechter, Tristan, Panadero, Javier, Serrat, Carles, Xhafa, Fatos, Juan, Angel A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.01.2023
MDPI
Schlagworte:
Algorithms
Car pools
Case studies
Cities
Communication
Computer Simulation
Efficiency
Heuristics
Infrastructure
Intelligence
Internet of Things
Machine learning
Mathematical optimization
Numerical analysis
open data
optimization
Optimization techniques
Quality of service
Smart cities
Social networks
Traffic congestion
Vehicles
vehicular networks
Spain
smart cities
vehicular networks
heuristics
open data
optimization
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23010499