Evaluating the Effect of Time Series Segmentation on STARIMA-Based Traffic Prediction Model

As the interest for developing intelligent transportation systems increases, the necessity for effective traffic prediction techniques becomes profound. Urban short-term traffic prediction has proven to be an interesting yet challenging task. The goal is to forecast the values of appropriate traffic descriptors such as average travel time or speed, for one or more time intervals in the future. In this paper a novel and efficient short-term traffic prediction approach based on time series analysis is provided. The authors' idea is to split traffic time series into segments (that represent different traffic trends) and use different time series models on the different segments of the series. The proposed method was evaluated using historical Global Positioning System (GPS) traffic data from the city of Berlin, Germany covering a total period of two weeks. The results show smaller traffic prediction error, in terms of travel time, with respect to two basic time series analysis techniques in the relevant literature.

• Record URL:
  https://doi.org/10.1109/ITSC.2015.359
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9781467365956
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Corporate Authors:

  Institute of Electrical and Electronics Engineers (IEEE)

  3 Park Avenue, 17th Floor
  New York, NY  United States  10016-5997
• Authors:
  • Salamanis, Athanasios
  • Meladianos, Polykarpos
  • Kehagias, Dionysios
  • Tzovaras, Dimitrios
• Conference:
  • 18th International IEEE Conference on Intelligent Transportation Systems (ITSC)
  • Location: Canary Islands , Spain
  • Date: 2015-9-15 to 2015-9-18
• Publication Date: 2015

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 2225-2230
• Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

• TRT Terms: Intelligent transportation systems; Mathematical prediction; Time series analysis; Traffic data; Traffic forecasting; Traffic speed; Travel time
• Geographic Terms: Berlin (Germany)
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01601911
• Record Type: Publication
• ISBN: 9781467365956
• Files: TRIS
• Created Date: Jun 15 2016 12:05PM