{
  "annotation_id": "2635b33d-668d-484d-a8e0-4ac7825d704d",
  "date_created": "2026-03-02T18:01:03.420000Z",
  "date_modified": "2026-03-02T18:01:03.420000Z",
  "file_hash": "0b3939979b3f9535f4f479039319fe8cbbcc6f829400fc841eb1d770dc42b252",
  "private": false,
  "record": {
    "abstract": "The data presented here show that human drivers apply a discrete noisy\ncontrol mechanism to drive their vehicle. A car-following model built on these\nobservations, together with some physical limitations (crash-freeness,\nacceleration), led to non-Gaussian probability distributions in the speed\ndifference and distance which are in good agreement with empirical data. All\nmodel parameters have a clear physical meaning and can be measured. Despite its\napparent complexity, this model is simple to understand and might serve as a\nstarting point to develop even quantitatively correct models.",
    "arxiv_id": "physics/0601058",
    "authors": [
      "Peter Wagner"
    ],
    "categories": [
      "physics.soc-ph",
      "physics.data-an"
    ],
    "doi": "10.1140/epjb/e2006-00300-1",
    "title": "How human drivers control their vehicle",
    "url": "https://arxiv.org/abs/physics/0601058"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a6ab59b3-7c81-45d2-9aa2-66f6d0c0fb15",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}