Flow count data-driven static traffic assignment models through network modularity partitioning
Bibliographic Data
| ID | 19338877 |
|---|---|
| Authors | Alexander Roocroft (0000-0002-6551-1800, Agency for Science, Technology and Research, corresponding author), Giuliano Punzo (0000-0003-4246-9045, University of Sheffield), Muhamad Azfar Ramli (0000-0002-6321-0828, Agency for Science, Technology and Research) |
| Year | 2025 |
| Volume | 52 |
| Issue | 1 |
| Pages | 185-214 |
| Publication date | 2025-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Transportation (JOURNAL) |
| Journal identifiers | ISSN: 0049-4488 • E-ISSN: 1572-9435 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11116-023-10416-x |
| OpenAlex | W4387097767 |
| Language | EN |
| Citations received | 2 |
| References cited | 41 |
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
Algorithm · Computation · Data mining · Flow network · Mathematical optimization · Modularity (biology) · Network model · Partition (number theory) · Real-time computing · Subnetwork · Traffic flow (computer networking) · Traffic generation model · Computer Science · Mathematics · Traffic control and management · Traffic Prediction and Management Techniques · Transportation Planning and Optimization
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| Citations per year | 2 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |