An Information Renewal GNN Model for Road Traffic Accident Forecasting

Road traffic system is a complicated non-linear system, in which road traffic accident is considered as the behavioral characteristic variable, whose developmental changes have trends of increase and stronger random fluctuation. Considering this point, the authors establish a combination forecasting model (namely GNN) based on grey forecasting model and ANN. First, grey information renewal GM (1,1) is established based on GM (1,1) and used to forecast the change tendency, then BP network is applied to modify grey residuals to capture stochastic phenomenon. The results show that the dual character of the road traffic time series with trends of increase and random fluctuation can be better described. Meanwhile, with advantages of GM (1,1) and ANN, GNN has obtained better forecasting precision than single grey information renewal GM (1,1). In summary, GNN can be applied as a novel, practical and simple forecasting tool in road traffic accident forecasting.

• Record URL:
  https://doi.org/10.1061/41039(345)274
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784410394
• 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:
  • Wang, Qiuping
  • Liu, Subing
• Conference:
  • Second International Conference on Transportation Engineering
  • Location: Chengdu , China
  • Date: 2009-7-25 to 2009-7-27
• Publication Date: 2009-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1655-1660
• Monograph Title: International Conference on Transportation Engineering 2009

Subject/Index Terms

• TRT Terms: Errors; Forecasting; Information management; Neural networks; Random variables; Time series analysis; Traffic crashes
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01526875
• Record Type: Publication
• ISBN: 9780784410394
• Files: TRIS, ASCE
• Created Date: Nov 12 2013 1:39PM