Working paper Open Access

Hybrid clustering/HMM constrained-based learning for Aircraft Trajectory Prediction

Harris Georgiou; Nikos Pelekis; David Scarlatti; Stylianos Sideridis; Yannis Theodoridis


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/627028e2-7fcc-460c-9ccb-b1c08573d551/Hybrid-clustering-HMM-final_20170216.pdf"
      }, 
      "checksum": "md5:e9c63b34fe080c1ff950528a00c1f806", 
      "bucket": "627028e2-7fcc-460c-9ccb-b1c08573d551", 
      "key": "Hybrid-clustering-HMM-final_20170216.pdf", 
      "type": "pdf", 
      "size": 1667556
    }
  ], 
  "owners": [
    23465
  ], 
  "doi": "10.5281/zenodo.1174083", 
  "stats": {
    "version_unique_downloads": 27.0, 
    "unique_views": 41.0, 
    "views": 45.0, 
    "downloads": 29.0, 
    "unique_downloads": 27.0, 
    "version_unique_views": 41.0, 
    "volume": 48359124.0, 
    "version_downloads": 29.0, 
    "version_views": 45.0, 
    "version_volume": 48359124.0
  }, 
  "links": {
    "doi": "https://doi.org/10.5281/zenodo.1174083", 
    "conceptdoi": "https://doi.org/10.5281/zenodo.1174082", 
    "bucket": "https://zenodo.org/api/files/627028e2-7fcc-460c-9ccb-b1c08573d551", 
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.1174082.svg", 
    "html": "https://zenodo.org/record/1174083", 
    "latest_html": "https://zenodo.org/record/1174083", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.1174083.svg", 
    "latest": "https://zenodo.org/api/records/1174083"
  }, 
  "conceptdoi": "10.5281/zenodo.1174082", 
  "created": "2018-02-16T11:39:01.289055+00:00", 
  "updated": "2019-04-09T13:33:58.835372+00:00", 
  "conceptrecid": "1174082", 
  "revision": 4, 
  "id": 1174083, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.1174083", 
    "version": "(preprint)", 
    "license": {
      "id": "CC-BY-NC-ND-4.0"
    }, 
    "title": "Hybrid clustering/HMM constrained-based learning for Aircraft Trajectory Prediction", 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "relation": "isVersionOf", 
        "identifier": "10.5281/zenodo.1174082"
      }
    ], 
    "notes": "Intermediate technical report for work-in-progress (Oct/2017).", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "1174082"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "1174083"
          }
        }
      ]
    }, 
    "language": "eng", 
    "grants": [
      {
        "code": "687591", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/501100000780::687591"
        }, 
        "title": "Big Data Analytics for Time Critical Mobility Forecasting", 
        "acronym": "datACRON", 
        "program": "H2020", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [
            "EC"
          ], 
          "name": "European Commission", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/501100000780"
          }
        }
      }, 
      {
        "code": "699299", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/501100000780::699299"
        }, 
        "title": "Data-driven AiRcraft Trajectory prediction research", 
        "acronym": "DART", 
        "program": "H2020", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [
            "EC"
          ], 
          "name": "European Commission", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/501100000780"
          }
        }
      }
    ], 
    "keywords": [
      "trajectory prediction", 
      "Big data analytics", 
      "mobility patterns", 
      "semantic clustering"
    ], 
    "publication_date": "2018-02-16", 
    "creators": [
      {
        "affiliation": "Data Science Lab, Univ. of Piraeus (UniPi), Greece", 
        "name": "Harris Georgiou"
      }, 
      {
        "affiliation": "Data Science Lab, Univ. of Piraeus (UniPi), Greece", 
        "name": "Nikos Pelekis"
      }, 
      {
        "affiliation": "Boeing Research & Technology Europe, Spain", 
        "name": "David Scarlatti"
      }, 
      {
        "affiliation": "Data Science Lab, Univ. of Piraeus (UniPi), Greece", 
        "name": "Stylianos Sideridis"
      }, 
      {
        "affiliation": "Data Science Lab, Univ. of Piraeus (UniPi), Greece", 
        "name": "Yannis Theodoridis"
      }
    ], 
    "access_right": "open", 
    "resource_type": {
      "subtype": "workingpaper", 
      "type": "publication", 
      "title": "Working paper"
    }, 
    "description": "<p>Abstract:</p>\n\n<p>Aircraft trajectory prediction (TP) is a challenging and inherently data-driven time-series modeling problem. Adding annotation parameters further increases the complexity of the search space, especially when &lsquo;blind&rsquo; optimization algorithms are employed. In this paper, flight plans, localized weather and aircraft properties are introduced as trajectory annotations (or semantics), which enable modeling in a space higher than the typical 4-D spatio-temporal domain. A two-phase hybrid approach is employed for the core TP task: (a) clustering using properly designed semantic-aware similarity functions as distance metrics; and (b) a hidden Markov model (HMM) for each cluster, using non-uniform graph-based spatial grid and exploiting flight plans as constraints for a parametric probabilistic model for the emissions. The proposed method is applied in real radar tracks and weather data for a one-month dataset of flights in Spanish airspace. Using parametric Gaussians as the base for the emissions model and confidence interval estimations for the associated errors, the proposed method exhibits exceptionally low HMM complexity and per-waypoint prediction accuracy of a few hundred meters compared with submitted flight plans.</p>"
  }
}
45
29
views
downloads
All versions This version
Views 4545
Downloads 2929
Data volume 48.4 MB48.4 MB
Unique views 4141
Unique downloads 2727

Share

Cite as