Research on Forecasting Model for Logistics Demand Based on Support Vector Machine

The paper analyzes the importance of logistics demand of forecasting and introduces the main forecasting methods both at home and abroad. Then the paper sums up core ideas and basic theories of Support Vector Machine and builds a forecasting model of logistics demand using this new theory. The paper also expounds on the analytic and applicable process of the model with the process of the parameters of calibration and correction. Thirdly, this paper builds on Beijing's logistics demand forecasting model based on the Support Vector Machine and applies LibSVM software to calculate the results. The results test the accuracy and feasibility of the model and indicate that it has a better utility value than previously supposed.

• Record URL:
  • https://doi.org/10.1061/41186(421)372
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784411865
• Supplemental Notes:
  • Copyright © 2011 ASCE
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Wang, Ying
• Conference:
  • 11th International Conference of Chinese Transportation Professionals (ICCTP)
  • Location: Nanjing , China
  • Date: 2011-8-14 to 2011-8-17
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: pp 3731-3741
• Monograph Title: ICCTP 2011: Towards Sustainable Transportation Systems

Subject/Index Terms

• TRT Terms: Calibration; Demand; Feasibility analysis; Forecasting; Logistics
• Uncontrolled Terms: Support vector machines
• Geographic Terms: Beijing (China)
• Subject Areas: Freight Transportation; Public Transportation; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01453753
• Record Type: Publication
• ISBN: 9780784411865
• Files: TRIS, ASCE
• Created Date: Nov 15 2012 12:32PM