{
  "annotation_id": "d0d3a112-a445-45f5-9364-f3e3d12ae258",
  "date_created": "2026-03-02T18:01:07.088000Z",
  "date_modified": "2026-03-02T18:01:07.088000Z",
  "file_hash": "3f5ae691283c2c714eb5224fc5049da86a799f697282cac2714635c722137555",
  "private": false,
  "record": {
    "abstract": "Traffic flows are studied in terms of their noise of sound, which is an\neasily accessible experimental quantity. The sound noise data is studied making\nuse of scaling properties of wavelet transforms and Hurst exponents are\nextracted. The scaling behavior is used to characterize the traffic flows in\nterms of scaling properties of the memory function in Mori-Lee stochastic\ndifferential equations. The results obtained provides for a new theoretical as\nwell as experimental framework to characterize the large-time behavior of\ntraffic flows. The present paper outlines the procedure by making use of\none-lane computer simulations as well as sound-data measurements from a real\ntwo-lane traffic flow. We find the presence of conventional diffusion as well\nas 1/f-noise in real traffic flows at large time scales.",
    "arxiv_id": "physics/0603252",
    "authors": [
      "B. -S. Skagerstam",
      "A. Hansen"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1209/epl/i2005-10291-y",
    "journal_ref": "Europhysics Letters, Vol. 72, No. 4, pp. 513-519 (2005)",
    "title": "Memory Effects and Scaling Properties of Traffic Flows",
    "url": "https://arxiv.org/abs/physics/0603252"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "51b64c17-1c5b-4123-af93-7899506135a7",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}