Improving the efficiency of repeated dynamic network loading through marginal simulation

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.