Forecasting the Risk of Traffic Accidents by Using the Artificial Neural Networks

The paper is focused on the impact analysis of 12 different factors influencing the traffic accident risk on the main and national roads of Lithuania. These factors describe technical road information and road environment. The analyzed roads are divided into 341 sections. Relevant information on each road section is provided, including traffic volume, number of accidents, and factor descriptions. Afterwards the artificial neural networks aimed at forecasting the traffic accident risk are built. Calculations reveal the best Artificial Neural Network configuration which generates the best forecasting results.

• Record URL:
  https://doi.org/10.3846/bjrbe.2013.37
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1822427X
• Authors:
  • Sliupas, Tomas
  • Bazaras, Zilvinas
• Publication Date: 2013

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 289-293
• Serial:
  • Baltic Journal of Road and Bridge Engineering
  • Volume: 8
  • Issue Number: 4
  • Publisher: Vilnius Gediminas Technical University
  • ISSN: 1822-427X
  • EISSN: 1822-4288
  • Serial URL: https://bjrbe-journals.rtu.lv/index
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Crash characteristics; Crash data; Forecasting; Highway design; Neural networks; Risk assessment; Traffic crashes; Traffic volume
• Geographic Terms: Lithuania
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; I72: Traffic and Transport Planning; I81: Accident Statistics;

Filing Info

• Accession Number: 01523595
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 28 2014 10:55AM