Interval Based Passenger Volume Forecasting Using Residual GM Model with IC Data

A method using residual gray model (GM) model (GM ( 1, 1) ) for prediction is considered to forecast passenger volume in same period of a day accurately and scientifically. GM ( 1, 1 ) model based on integrated circuit (IC) data collected by single bus route is obtained. By determining coefficients of white equation, response time sequence of bus passenger volume is determined. Using response time sequence of gray differential equation to obtain restored value, development sequence of interval based passenger volume of a single bus route is established. Taking a variety of factors which influence bus passenger volume into consideration, development sequence is revised. Finally, the revised predicted value sequence is obtained. Numerical studies are made using IC data of bus passenger volume of JINAN city, to receive bus passenger volume prediction of nearly a month. It is shown that prediction model in this paper has high prediction accuracy; as a result, provides guidance to decision - makers of bus management.

• Record URL:
  http://itswc.confex.com/itswc/WC2011/webprogram/Paper2599.html
• Availability:
  • Find a library where document is available. Order URL: http://itswc.confex.com/itswc/WC2011/webprogram/start.html
• Supplemental Notes:
  • Abstract reprinted with permission from Intelligent Transportation Society of America.
• Corporate Authors:

  ITS America

  1100 17th Street, NW, 12th Floor
  Washington, DC  United States  20036
• Authors:
  • Pan, Shuliang
  • Liu, Yuanyuan
  • Huang, Wei
  • Zhu, Lin
  • Wang, Shengjia
• Conference:
  • 18th ITS World Congress
  • Location: Orlando Florida, United States
  • Date: 2011-10-16 to 2011-10-20
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 8p
• Monograph Title: 18th ITS World Congress, Orlando, 2011. Proceedings

Subject/Index Terms

• TRT Terms: Bus transit; Forecasting; Mathematical prediction; Passenger volume; Ridership; Smart cards
• Geographic Terms: Jinan (China)
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01487244
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 8 2013 8:15PM