GIS-T; Sub-project on methods for quality control of data and model results

  • Nielsen, Otto Anker (Project Manager)
  • Leleur, Steen (Project Participant)
  • Brems, Camilla Riff (Project Participant)
  • Thorlacius, Per (Project Participant)
  • Grevy, Bo (Project Participant)
  • Nielsen, Erik Rude (Project Participant)
  • Israelsen, Thomas (Project Participant)
  • Hansen, Christian Overgaard (Project Participant)
  • Bloch, Karsten Sand (Project Participant)
  • Nielsen, Jan (Project Participant)
  • Nielsen, Mogens (Project Participant)
  • Petersen, Jens Møller (Project Participant)

Project Details


The use of digital maps and adjacent databases have the potential to ease the work process when setting up traffic models. However, such methods do not necessarily secure against data errors. The amount of data in traffic models is rapidly increasing (especially due to new technologies such as GIS), which results in extremely time consuming quality controls in practice. Due to the amount and complexity of the work, many errors and deficiencies are overlooked. Such disregards in the data foundation of traffic models results often in doubtful traffic models. In addition it becomes difficult to determine whether problems stems from the data foundation, simplified assumptions or the structure of the traffic models. Thus quality control of data and models must be seen as a whole.
It is the sub-projects goal to develop GIS-based methods and guidelines for quality control of data, as well as to develop validation methods for traffic models. This will provide a significant contribution to the quality of traffic forecasts. Several good experiences with the methodologies have already been achieved in different applied projects (especially the Harbour Tunnel project) and several Danish/Nordic and International papers on the subject are on their way. See the GIS-T programme for organizational details on the project.
Effective start/end date02/01/199601/06/1997


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.