Marginal Dynamic Network Loading for Large-scale Simulation-Based Applications

Currently, the scope of using macroscopic Dynamic Network Loading (DNL) models for applications such as real-time traffic management, reliability and vulnerability studies, network design and dynamic origin-destination (OD) estimation is limited by the computational overhead. The main reason is that these applications require a large number of DNL runs to be performed. Since the successive simulations typically exhibit a large overlap, this problem can be overcome by introducing marginal simulation. Through marginal simulation, iterative or Monte-Carlo simulation can be performed much more efficiently by approximating each simulation as a variation to one single base simulation. Thus, repetition of countless identical calculations is avoided. The Marginal Computation (MaC) model presented here is a marginal DNL model consistent with first-order kinematic wave theory, thus realistically capturing congestion dynamics. It can model both demand and supply variations, which means it is suited for a wide range of possible applications. A case study on a medium to large-scale network (around Gent, Belgium) is added to illustrate its performance.

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