Providing a Method for Accident Severity Analysis Using Geospatial Clustering Functions and Decision Tree, Case Study: Qazvin-Loshan Freeway

Document Type : Research Article


1 Department of Road and Transportion, Faculty of Civil Engineering, The University Of Elmo-Sanat,Tehran, Iran

2 Assistant Professor, Department of Civil Engineering, Faculty of Engineering The University of Guilan


Spatial analysis of accidents occurred in freeways and identifying effective parameters can help researchers and authorities to improve road safety by reducing the severity of accidents. The purpose of this study is to provide a method to analyze the accident severity and determine related effective parameters in freeways based on spatial clustering functions and regression and classification tree data mining method. Proposed method was assessed in Qazvin-Loshan freeway. In this study, to study the spatial distribution of the accidents in the aforementioned axis during the period from 2011 to 2016, the spatial functions such as Getis-Ord G* autocorrelation and Kernel Density Functions were Used. The results of spatial analysis showed that the spatial gathering of accidents in most of horizontal curves was greater. According to this achievement, in the next phase of the study, in order to study the factors affecting the severity of accidents, the Regression and Classification Tree was used on accidents that occurred in the whole axis and specifically the crashes which occurred in the horizontal curves. Results of this part of the study showed that the type of accidents (overturning and falling, exit from the road, multi-vehicle collisions, etc.) and human factors are the most important factors in the severity of accidents in this axis. Relative importance coefficients for these two independent variables are 100 and 39.7 percent for the whole axis and 100 and 65.9 percent for horizontal curves. The study of the relative importance of other variables used in the proposed model showed that the geometric design, type and date of crashes are among the most effective factors in increasing the property damage only crashes in Qazvin-Loshan Freeway. This study showed that the integration of GIS functions with non-parametric data mining algorithms such as decision tree, which is capable of simultaneous modeling of quantitative and qualitative data, is an effective approach to determine the factors affecting the severity of accidents and to analyze the spatial patterns of accidents in freeways.


Main Subjects

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