Link Count Data-driven Static Traffic Assignment Models Through Network Modularity Partitioning

TL;DR

This paper introduces a network partitioning method based on modularity to improve data-driven static traffic assignment models, enabling efficient OD demand estimation from flow data on large road networks.

Contribution

It presents a novel network partitioning approach that enhances demand estimation and reduces computational complexity in static traffic assignment models.

Findings

Partitioning yields reasonable flow and travel time errors.

Large partitions maintain accuracy with lower computation.

Degenerate networks show acceptable errors with minimal degeneration.

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 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.