  Accurate static traffic assignment models are important tools for the
assessment of strategic transportation policies. In this article we present a
novel approach to partition road networks through network modularity to produce
data-driven static traffic assignment models from loop detector data on large
road systems. The use of partitioning allows the estimation of the key model
input of Origin-Destination demand matrices from flow counts alone. Previous
network tomography-based demand estimation techniques have been limited by the
network size. The amount of partitioning changes the Origin-Destination
estimation optimisation problems to different levels of computational
difficulty. Different approaches to utilising the partitioning were tested, one
which degenerated the road network to the scale of the partitions and others
which left the network intact. Applied to a subnetwork of England's Strategic
Road Network and other test networks, our results for the degenerate case
showed flow and travel time errors are reasonable with a small amount of
degeneration. The results for the non-degenerate cases showed that similar
errors in model prediction with lower computation requirements can be obtained
when using large partitions compared with the non-partitioned case. This work
could be used to improve the effectiveness of national road systems planning
and infrastructure models.
