We model the formation of multilayer transportation networks as amultiobjective optimization process, where service providers compete forpassengers, and the creation of routes is determined by a multiobjectivecost function encoding a trade-off between efficiency and competition.The resulting model reproduces well real-world systems as diverse asairplane, train, and bus networks, thus suggesting that such systems areindeed compatible with the proposed local optimization mechanisms. Inthe specific case of airline transportation systems, we show that thenetworks of routes operated by each company are placed very close to thetheoretical Pareto front in the efficiency-competition plane, and thatmost of the largest carriers of a continent belong to the correspondingPareto front. Our results shed light on the fundamental role played bymultiobjective optimization principles in shaping the structure oflarge-scale multilayer transportation systems, and provide novelinsights to service providers on the strategies for the smart selectionof novel routes.

Pareto Optimality in Multilayer Network Growth

Andrea Santoro;Vito Latora;Giuseppe Nicosia;Vincenzo Nicosia
2018-01-01

Abstract

We model the formation of multilayer transportation networks as amultiobjective optimization process, where service providers compete forpassengers, and the creation of routes is determined by a multiobjectivecost function encoding a trade-off between efficiency and competition.The resulting model reproduces well real-world systems as diverse asairplane, train, and bus networks, thus suggesting that such systems areindeed compatible with the proposed local optimization mechanisms. Inthe specific case of airline transportation systems, we show that thenetworks of routes operated by each company are placed very close to thetheoretical Pareto front in the efficiency-competition plane, and thatmost of the largest carriers of a continent belong to the correspondingPareto front. Our results shed light on the fundamental role played bymultiobjective optimization principles in shaping the structure oflarge-scale multilayer transportation systems, and provide novelinsights to service providers on the strategies for the smart selectionof novel routes.
2018
TRANSPORTATION NETWORK, COMPLEX NETWORKS, AIR TRANSPORT, TRADE-OFFS, TRANSITION, OPTIMIZATION, PREDICTION, EMERGENCE, GEOMETRY, SCIENCE.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/357664
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