{
  "id": 19301767,
  "title": "Reconstructing hourly traffic on every street of a capital from a sparse counting network: a graph neural network applied to Paris",
  "url": "https://urgent.news/2026/11/09/reconstructing-hourly-traffic-on-every-street-of-a-capital-from-a",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-09T13:20:25.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.12379v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Micro-scale air quality models require hourly traffic data on every road segment, whereas permanent counters cover only a small part of the network. A graph neural network (GNN) was developed to reconstruct hourly traffic flows on the 32,163 road segments of Paris intra-muros from the 1,573 segments equipped with a counter (4.9%). The model adapts the Graph Neural Network for Urban Interpolation…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}