Statistical metamodeling of dynamic network loading

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Title: Statistical metamodeling of dynamic network loading
Author(s): Song, W
Han, K
Wang, Y
Friesz, TL
Del Castillo, E
Item Type: Journal Article
Abstract: Dynamic traffic assignment models rely on a network performance module known as dynamic network loading (DNL), which expresses flow propagation, flow conservation, and travel delay at a network level. The DNL defines the so-called network delay operator, which maps a set of path departure rates to a set of path travel times (or costs). It is widely known that the delay operator is not available in closed form, and has undesirable properties that severely complicate DTA analysis and computation, such as discontinuity, non-differentiability, non-monotonicity, and computational inefficiency. This paper proposes a fresh take on this important and difficult issue, by providing a class of surrogate DNL models based on a statistical learning method known as Kriging. We present a metamodeling framework that systematically approximates DNL models and is flexible in the sense of allowing the modeler to make trade-offs among model granularity, complexity, and accuracy. It is shown that such surrogate DNL models yield highly accurate approximations (with errors below 8%) and superior computational efficiency (9 to 455 times faster than conventional DNL procedures such as those based on the link transmission model). Moreover, these approximate DNL models admit closed-form and analytical delay operators, which are Lipschitz continuous and infinitely differentiable, with closed-form Jacobians. We provide in-depth discussions on the implications of these properties to DTA research and model applications.
Publication Date: 24-Aug-2017
Date of Acceptance: 16-Aug-2017
URI: http://hdl.handle.net/10044/1/50378
DOI: https://dx.doi.org/10.1016/j.trb.2017.08.018
ISSN: 0191-2615
Publisher: Elsevier
Journal / Book Title: Transportation Research Part B: Methodological
Copyright Statement: © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords: dynamic traffic assignment
dynamic network loading
delay operator
metamodeling
Kriging
dynamic games
Gaussian processes
dynamic traffic assignment
dynamic network loading
delay operator
metamodeling
Kriging
dynamic games
1507 Transportation And Freight Services
0102 Applied Mathematics
Logistics & Transportation
Publication Status: Published
Embargo Date: 2019-02-24
Appears in Collections:Faculty of Engineering
Civil and Environmental Engineering



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