Capacity-Aware Deep Learning for Generalizable Traffic Volume Estimation Across Links and Cities
Network-wide traffic volume estimation typically relies on propagating measurements from fixed sensors, making performance highly dependent on sensor density and limiting deployment in sparsely instrumented networks. We propose a link-level learning framework that estimates hourly traffic volumes from widely available territorial data only, including probe speed profiles, road and topological descriptors, along with weather observations. A supervised local mapping is learned from sparse sensor measurements and evaluated under two generalization settings: intra-network (unseen links within the training network) and inter-network (unseen city). This formulation frames traffic volume estimat...
| Authors | Léo Hein, Giovanni De Nunzio, Aurélie Pirayre et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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