GIS-based Road Safety Evaluation Model for Cyclist in Campus of Higher Education Mega Center

In China, many cities construct Higher Education Mega Centers (HEMC) in large-scale, but HEMC's construction is finished in a short period, many problems have no time to discuss and be left unsolved, especially in road traffic. For example, in these years, there are many road accidental injuries to the students in HEMC; most of them are cyclists, for bicycle is the main traffic vehicle of campus students. This paper summarizes four main factors that influence the cyclist safety in campus: slope gradient, road curvature, the distance approaches to the intersections and other special factors (like rainy day) that contribute to the road risk. By using of S-type function in Fuzzy Mathematics, the forward three main risk factors are quantified and normalized, and a comprehensive safety evaluation model for cyclist is established. The east campus of Sun Yat-sen University in GZ HEMC has been used as an application of the road risk distribution model. With the help of geographic information system (GIS) software, all risk values of points in road are interpolated through sample points by DTM function, and the risk DTM is overlaid on the remote sensing image of the same area. Finally a risk distribution map along road for cyclists of campus in sunny days is created, so does in rainy days. Through field based interviews, the authors can prove that risk distribution map is in accord with realistic bicycle crash locations, it can serve as a basis reference for improving cyclist safety in campus of HEMC, as well as a safety information and guidance for students.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 1127-1131
  • Monograph Title: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012)

Subject/Index Terms

Filing Info

  • Accession Number: 01568152
  • Record Type: Publication
  • ISBN: 9781467330640
  • Files: TRIS
  • Created Date: May 15 2015 12:11PM