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.

Language

  • English

Media Info

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

Subject/Index Terms

Filing Info

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