  Metropolitan scale vehicular traffic modeling is used by a variety of private
and public sector urban mobility stakeholders to inform the design and
operations of road networks. High-resolution stochastic traffic simulators are
increasingly used to describe detailed demand-supply interactions. The design
of efficient calibration techniques remains a major challenge. This paper
considers a class of high-dimensional calibration problems known as
origin-destination (OD) calibration. We formulate the problem as a continuous
simulation-based optimization problem. Our proposed algorithm builds upon
recent metamodel methods that tackle the simulation-based problem by solving a
sequence of approximate analytical optimization problems, which rely on the use
of analytical network models. In this paper, we formulate a network model
defined as a system of linear equations, the dimension of which scales linearly
with the number of roads with field data and independently of the dimension of
the route choice set. This makes the approach suitable for large-scale
metropolitan networks. The approach has enhanced efficiency compared with past
metamodel formulations that are based on systems of nonlinear, rather than
linear, equations. It also has enhanced efficiency compared to traditional
calibration methods that resort to simulation-based estimates of traffic
assignment matrices, while the proposed approach uses analytical approximations
of these matrices. We benchmark the approach considering a peak period Salt
Lake City case study and calibrate based on field vehicular count data. The new
formulation yields solutions with good performance and is suitable for
large-scale road networks.
