Flow count data-driven static traffic assignment models through network modularity partitioning

Abstract 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 produce data-driven from loop detector data on large systems. The use partitioning allows estimation key model input Origin–Destination demand matrices flow counts alone. Previous tomography-based techniques have been limited by size. amount changes optimisation problems different levels computational difficulty. Different approaches utilising were tested, one which degenerated scale partitions and others left intact. Applied subnetwork England’s Strategic Road Network other test networks, our results degenerate case showed travel time errors reasonable with small degeneration. non-degenerate cases that similar in prediction lower computation requirements can be obtained when using compared non-partitioned case. This work could used improve effectiveness national systems planning infrastructure models.