An automated process of identifying high-risk roads for speed management intervention

This paper presents a geospatial process to automate the calculation of infrastructure risk rating (IRR). The process utilises various national and regional geospatial datasets to extract road features needed to calculate IRR. A comparison of the automated process outputs with manually coded IRR data of the same network resulted in a matching rate of almost 90 percent, hereby confirming the validity of the automated process. Aside from demonstrating the true potential of transport related data, this innovative approach will enable road controlling authorities to efficiently identify parts of their network where speed management intervention is most likely to reduce road trauma.

• Record URL:
  http://acrs.org.au/publications/journals/current-and-back-issues/
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18329497
• Supplemental Notes:
  • (paper to) Australasian Road Safety Conference, 2016, Canberra, ACT, Australia
• Authors:
  • Zia, H
  • Durdin, P
  • Harris, D
• Publication Date: 2016

Language

• English

Media Info

• Pagination: 43-8
• Serial:
  • Journal of the Australasian College of Road Safety
  • Volume: 27
  • Issue Number: 4
  • Publisher: Australasian College of Road Safety
  • ISSN: 1832-9497

Subject/Index Terms

• TRT Terms: Data analysis; Data collection; High risk locations; Risk assessment; Risk management; Speed
• Uncontrolled Terms: Safe systems (roads)
• Geographic Terms: New Zealand
• ATRI Terms: Crash black spot; Data analysis; Risk assessment; Risk management; Speed
• ITRD Terms: 3882: Automatic; 1055: Transport infrastructure
• Subject Areas: Data and Information Technology; Safety and Human Factors; I82: Accidents and Transport Infrastructure;

Filing Info

• Accession Number: 01618928
• Record Type: Publication
• Source Agency: ARRB
• Files: ITRD, ATRI
• Created Date: Dec 19 2016 11:53AM