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Road Traffic Accident Simulation Modelling- A Kernel Estimation Approach

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

Abstract

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:10.1.1.135.5987
Provided by: CiteSeerX
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