kernel density spatial function

The spatial analysis of accidents occurring in Highway with the aim of identifying theeffective parameters on the increase in the severity of accidents can be effective in decision making by professionals and decision makers to improve road safety in order to reduce the severity of road accidents.The purpose of this study is to provide a method for analyzing the severity of crashes and determining its effective factors in the freeways based on the spatial clustering functions and the data mining model of the regression tree.The proposedmethodis evaluated and tested on the Qazvin-Loshan freeway.In order to investigate the spatialdistributionofcrashes inthe axis of the study during the period of from2011to2016,the Geographic Information System’s spatial functions such as Getis-OrdG*Autocorrelation andkernel density functions have been Used.The preliminary results of spatial analysis showed that the focus of accidents in 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 Classification andRegressionTree was used on accidents occurringinthewholeaxisandspecifically in thehorizontal curveswith highcrashes.Thestudy of the relative importance of other variables in the proposed model shows that the geometric design,type of crashes and day of theaccident are the effective factors in increasing the accident severity at the Qazvin-Loshan Freeway.Inaddition,the results of modeling on horizontal curves crashes showed that the accident location,especiallytheshoulder,as well as the time of the accident,cause serious accidents.The results showedthattheintegrationofGISspatialfunctions withnon-parametricdecision-making tree-based datamining analysis,which is capable ofsimultaneous modelingofquantitativeandqualitativedata,is used to determine the factors affecting theseverityofaccidents andtospatial analyzethe patternsofaccidents usedinsuburbanarea,iseffective.