Imperial College London

Dr Ke Han

Faculty of EngineeringDepartment of Civil and Environmental Engineering

Senior Lecturer



+44 (0)20 7594 5682k.han Website CV




Mrs Maya Mistry +44 (0)20 7594 6100




605Skempton BuildingSouth Kensington Campus






BibTex format

author = {Song, W and Han, K and Wang, Y and Friesz, TL and del, Castillo E},
doi = {10.1016/j.trb.2017.08.018},
journal = {Transportation Research Part B: Methodological},
title = {Statistical metamodeling of dynamic network loading},
url = {},
year = {2017}

RIS format (EndNote, RefMan)

AB - 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.
AU - Song,W
AU - Han,K
AU - Wang,Y
AU - Friesz,TL
AU - del,Castillo E
DO - 10.1016/j.trb.2017.08.018
PY - 2017///
SN - 0191-2615
TI - Statistical metamodeling of dynamic network loading
T2 - Transportation Research Part B: Methodological
UR -
UR -
ER -