Short-Term Traffic Flow Prediction Based on EMD and Artificial Neural Network

An approach to short-term traffic flow prediction based on empirical mode decomposition (EMD) and artificial neural network (ANN) is proposed. The traffic flow is decomposed into different modes by EMD, and these different modes are predicted by appropriate ANNs. The predictive traffic flow is obtained by adding up all predictive values. This method is used to predict traffic flow with the actual measurement data. The results show that the proposed method has high predictive accuracy, and is more successful than the outcome of directly using ANN prediction.

• Record URL:
  • https://doi.org/10.1061/41064(358)225
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784410646
• Supplemental Notes:
  • © 2009 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Luo, Xianglong
  • Niu, Guohong
  • Wu, Qianjiao
• Conference:
  • Ninth International Conference of Chinese Transportation Professionals (ICCTP)
  • Location: Harbin China, United States
  • Date: 2009-8-5 to 2009-8-9
• Publication Date: 2009

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1-6
• Monograph Title: ICCTP 2009: Critical Issues In Transportation Systems Planning, Development, and Management

Subject/Index Terms

• TRT Terms: Neural networks; Predictive models; Traffic flow
• Identifier Terms: Empirical Mode Decomposition
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01925507
• Record Type: Publication
• ISBN: 9780784410646
• Files: TRIS, ASCE
• Created Date: Jul 25 2024 5:12PM