In this paper we focus on the multimodal network design problem that consists in designing jointly road and transit systems, assuming elastic demand at least at the mode choice level. We refer to regional contexts where a planner may have financial resources to be invested for improving the mobility of a wide area and have to decide how these resources should be allocated between transit and road systems. We propose an optimisation model for solving the problem, whereby we introduce an objective function that takes into account different objectives of the problem (reduction in user costs, reduction in external costs, etc.) and all constraints to be considered (budget constraints, capacity constraints, assignment constraints, etc.). We then propose a meta-heuristic solution algorithm for solving the problem and test it on a trial and a real-scale network.
An optimisation model and algorithms for solving the multimodal network design problem in regional contexts / Gallo, M.; D'Acierno, Luca; Montella, Bruno. - ELETTRONICO. - (2010), pp. 01439.1-01439.20. (Intervento presentato al convegno 12th World Conference on Transport Research tenutosi a Lisbon, Portugal nel July 2010).
An optimisation model and algorithms for solving the multimodal network design problem in regional contexts
D'ACIERNO, LUCA;MONTELLA, BRUNO
2010
Abstract
In this paper we focus on the multimodal network design problem that consists in designing jointly road and transit systems, assuming elastic demand at least at the mode choice level. We refer to regional contexts where a planner may have financial resources to be invested for improving the mobility of a wide area and have to decide how these resources should be allocated between transit and road systems. We propose an optimisation model for solving the problem, whereby we introduce an objective function that takes into account different objectives of the problem (reduction in user costs, reduction in external costs, etc.) and all constraints to be considered (budget constraints, capacity constraints, assignment constraints, etc.). We then propose a meta-heuristic solution algorithm for solving the problem and test it on a trial and a real-scale network.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.