An Aggregation Approach to Short-Term Traffic Flow Prediction

In this paper, the authors propose an aggregation approach for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) models. The aggregation approach assembles information from weekly, daily, and hourly time series. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions resulting from the different models are used as the basis of the NN in the aggregation stage, and the output of the trained NN serves as the final prediction.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Authors:
  • Tan, Man-chun
  • Wong, S C
  • Xu, Jian-Min
  • Guan, Zhan-Rong
  • Zhang, Peng
• Publication Date: 2009-3

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 60-69
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 10
  • Issue Number: 1
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Mathematical prediction; Neural networks; Time series; Traffic flow; Traffic forecasting; Traffic models
• Subject Areas: Highways; Operations and Traffic Management; I71: Traffic Theory;

Filing Info

• Accession Number: 01142750
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: BTRIS, TRIS
• Created Date: Oct 30 2009 8:39AM