Reference : Improving the efficiency of repeated dynamic network loading through marginal simulation
Scientific journals : Article
Engineering, computing & technology : Civil engineering
http://hdl.handle.net/10993/16116
Improving the efficiency of repeated dynamic network loading through marginal simulation
English
Corthout, Ruben mailto [Transport and Mobility Leuven]
Himpe, Willem mailto [KU Leuven > Department of Mechanical Engineering, CIB/Traffic & Infrastructure]
Viti, Francesco mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Frederix, Rodric mailto [Transport and Mobility Leuven]
Tampere, Chris MJ mailto []
Jan-2014
Transportation Research. Part C : Emerging Technologies
Pergamon Press
41
1-20
Yes
International
0968-090X
[en] Marginal Simulation ; Dynamic Network Loading ; Marginal Computation Algorithm ; Computational efficiency
[en] Currently, the applicability of macroscopic Dynamic Network Loading (DNL) models for
large-scale problems such as network-wide traffic management, reliability and vulnerability
studies, network design, traffic flow optimization and dynamic origin–destination (OD)
estimation is computationally problematic. The main reason is that these applications
require a large number of DNL runs to be performed. Marginal DNL simulation, introduced
in this paper, exploits the fact that the successive simulations often exhibit a large overlap.
Through marginal simulation, repeated DNL simulations can be performed much faster by
approximating each simulation as a variation to a base scenario. Thus, repetition of identical
calculations is largely avoided. The marginal DNL algorithm that is presented, the
Marginal Computation (MaC) algorithm, is based on first order kinematic wave theory.
Hence, it realistically captures congestion dynamics. MaC can simulate both demand and
supply variations, making it useful for a wide range of DNL applications. Case studies on
different types of networks are presented to illustrate its performance.
Researchers ; Professionals
http://hdl.handle.net/10993/16116

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