Skip to main content
Article thumbnail
Location of Repository

Road Traffic Accident Simulation Modelling- A Kernel Estimation Approach

By Dr Clive, E Sabel, Dr Simon Kingham, Prof Alan Nicholson and Phil Bartie


This paper outlines the development of a method for using Kernel Estimation cluster analysis techniques to automatically identify road traffic accident ‘black spots ’ and ‘black areas’. Christchurch, New Zealand, was selected as the study area and data from the LTNZ crash database used to trial the technique. A GIS and Python scripting was used to implement the solution, combining spatial data for average traffic flows with the recorded accident locations. Kernel Estimation was able to quickly identify the accident clusters, and when used in conjunction with Monte Carlo simulation techniques, was able to identify statistically significant clusters

Topics: kernel estimation, spatial clusters, road traffic accidents, Monte Carlo simulation, spatial data
Year: 2009
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.