Use of density-based cluster analysis and classification techniques for traffic congestion prediction and visualisation

The field of Intelligent Transportation Systems has lately raised increasing interest due to its high socio-economic impact. This work aims on developing efficient techniques for traffic congestion prediction and visualisation. We have designed a simple, yet effective and scalable model to handle sparse data from GPS observations and reduce the problem of congestion prediction to a binary classification problem (jam, non-jam). An attempt to generalise the problem is performed by exploring the impact of discriminative versus generative classifiers when employed to produce results in a 30-minute interval ahead of present time. In addition, we present a novel congestion prediction algorithm based on using correlation metrics to improve feature selection. Concerning the visualisation of traffic jams, we present a traffic jam visualisation approach based on cluster analysis that identifies dense congestion areas.

• 
• Corporate Authors:

  Institut Francais des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)

  14-20 bd Newton, Cité Descartes, Champs su Marne
  77447 Marne la Vallée,   France  Cedex 2
• Authors:
  • Diamantopoulos, T
  • Kehagias, D
  • König, F G
  • Tzovaras, D
• Conference:
  • Transport Research Arena (TRA) 5th Conference: Transport Solutions from Research to Deployment
  • Location: Paris , France
  • Date: 2014-4-14 to 2014-4-17
• Publication Date: 2014-4

Language

• English

Media Info

• Pagination: 10p
• Monograph Title: Transport Research Arena (TRA) 2014 Proceedings

Subject/Index Terms

• TRT Terms: Data analysis; Forecasting; Highway traffic control; Intelligent transportation systems; Traffic congestion; Traffic density
• ATRI Terms: Data analysis; Forecast; Intelligent transport systems (ITS); Traffic concentration; Traffic congestion; Traffic management
• ITRD Terms: 8735: Intelligent transport system
• Subject Areas: Operations and Traffic Management;

Filing Info

• Accession Number: 01530706
• Record Type: Publication
• Source Agency: ARRB
• Files: VTI, TRIS, ATRI
• Created Date: Jul 23 2014 12:54PM