Spatial analysis of two-wheeled vehicles traffic crashes: Osmaniye in Turkey

To achieve a sustainable traffic management and sustainable transportation planning in urban areas one of the tools that can be used effectively is Geographic Information Systems. In Turkey Transportation Department under Civil Engineering deals with roads and railways which are open to public such as inner city and outer city traffic arrangements with General Directorate of Security. This paper aims to analyze road traffic accident distribution according to the vehicle types involved in the accidents for urban areas in sample of Osmaniye, Turkey. While chi-square test is used for non-spatial analysis kernel density, nearest neighbor distances and K-function analysis are applied to determine the existence of clustering and hotspots. The network statistics are executed through SANET (Spatial Analysis on a NETwork) V4.1 that runs on ArcMap 10 and for better visualization and understanding the analysis 3D analysis are generated in ArcScene 10. It is realized that the accidents involving two-wheeled vehicles have a high percentage in all accidents. Also the possible reasons to use two-wheeled vehicles at a high rate in the city center are searched by Location Quotient Technique. The importance of this study is to use GIS as a management system for accident analysis with statistical analysis methods.

[1]  Trevor C. Bailey,et al.  Interactive Spatial Data Analysis , 1995 .

[2]  Margaret M. Peden,et al.  World Report on Road Traffic Injury Prevention , 2004 .

[3]  David O'Sullivan,et al.  Geographic Information Analysis , 2002 .

[4]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[5]  Christopher D. Lloyd,et al.  Spatial Data Analysis: An Introduction for GIS users , 2010 .

[6]  William Nathan Oris.  Spatial Analysis of Fatal Automobile Crashes in Kentucky , 2011 .

[7]  John Steward,et al.  The Impact of Built Environment on Pedestrian Crashes and the Identification of Crash Clusters on an Urban University Campus , 2010, The western journal of emergency medicine.

[8]  I. B. Gundogdu,et al.  Applying Linear Analysis Methods to GIS-supported Procedures for Preventing Traffic Accidents: Case Study of Konya , 2010 .

[9]  Ibrahim Yilmaz,et al.  Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar. , 2008, Accident; analysis and prevention.

[10]  Dominique Lord,et al.  Guidelines for Choosing Hot-Spot Analysis Tools Based on Data Characteristics, Network Restrictions, and Time Distributions , 2012 .

[11]  Andrew M. Isserman,et al.  The Location Quotient Approach to Estimating Regional Economic Impacts , 1977 .

[12]  Isabelle Thomas,et al.  Intra-urban location and clustering of road accidents using GIS: a Belgian example , 2004, Int. J. Geogr. Inf. Sci..

[13]  Carol A. Gotway,et al.  Statistical Methods for Spatial Data Analysis , 2004 .

[14]  A P Jones,et al.  The application of K-function analysis to the geographical distribution of road traffic accident outcomes in Norfolk, England. , 1996, Social science & medicine.

[15]  Emily Palmer Taquechel,et al.  A Spatial Analysis of the Relationship between Pedestrian Crash Events and Features of the Built Environment in Downtown Atlanta , 2009 .

[16]  Atsuyuki Okabe,et al.  SANET: A Toolbox for Spatial Analysis on a Network , 2006 .

[17]  Abdul Manan Khan,et al.  Causes of Road Accidents in Pakistan , 2012 .

[18]  V. Prasannakumar,et al.  Spatio-Temporal Clustering of Road Accidents: GIS Based Analysis and Assessment , 2011 .

[19]  Jean-Claude Thill,et al.  Comparison of planar and network K-functions in traffic accident analysis , 2004 .