Toward a Resilient Prediction System for Non-Uniform Traffic Data

The authors developed a traffic prediction system which enhances a traffic information service. The prediction method is based on time series analysis and is applicable to short to long term prediction. Traffic information system are real-time and real-world system therefore it suffers various kind of disturbance from environment. To preserve traffic prediction quality, we need fundamental treatment on overall system so that the prediction engine be tolerant toward incomplete traffic data feed or non-stationary traffic data. A solution for incomplete data feed is a combination of data for multiple links. A solution for non-stationary traffic is a traffic simulation dedicated to traffic accidents. With these enhancements toward cyber disturbance and physical disturbance, the system resiliency can be higher.

  • Availability:
  • Supplemental Notes:
    • Abstract used with permission of ITS Japan. Paper No. 3293.
  • Corporate Authors:

    ITS Japan

    Tokyo,   Japan 
  • Authors:
    • Masutani, Osamu
    • Liu, Zhen
    • Miwa, Tomio
    • Morikawa, Takayuki
  • Conference:
  • Publication Date: 2013


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 8p
  • Monograph Title: 20th ITS World Congress, Tokyo 2013. Proceedings

Subject/Index Terms

Filing Info

  • Accession Number: 01538651
  • Record Type: Publication
  • ISBN: 9784990493981
  • Files: TRIS
  • Created Date: Sep 2 2014 10:37AM