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There is a growing concern in traffic accident rate in recent years. Using Mashhad city(Iran second populous city) traffic accident records as case study, this paper applied the combination of geo-information technology and spatial-statistical analysis to bring out the influence of spatial factors in their formation. The aim of the study is to examine 4 clustering analyses to have a better understanding of traffic accidents patterns in complex urban network. In order to deploy the clustering technique in urban roads, 9331 point features for inner city traffic accidents during 12 months have been registered according to their x and y location in geographic information system(GIS). The mentioned areas were analyzed by kernel density estimation(KDE) using ARCMAP and two other analyses using SANET 4th edition software so that the results of network analysis can be compared with traditional KDE method. In addition, this research introduces five classifications for determining the eventfulness of the under study area based on standard deviation and to make priority in creating security in the area.The nearest neighbor and K-function output analysis consist of four curves and regarding the fact that for all fatal, injury and property damage only crashes, the observed value curve is above the 5% confidence interval. Accidents in the study region are more clustered than expected by random chance. The importance of this study is to use GIS as a management system for accident analysis by combination of spatial-statistical methods.
There is a growing concern in traffic accident rate in recent years. Using Mashhad city (Iran second populous city) traffic accident records as case study, this paper applied the combination of geo-information technology and spatial-statistical analysis to bring out the influence of spatial objectives in their formation. The aim of the study is to examine 4 clustering analyzes to have a better understanding of traffic accidents patterns in complex urban network. In order to deploy the clustering techniques in urban roads, 9331 point features for inner city during 12 months have been registered according to their x and y location in geographic information system (GIS). The mentioned areas were analyzed by kernel density estimation (KDE) using ARCMAP and two other analyzes using SANET 4th edition software so that the results of network In addition, this research introduces five classifications for determining the eventfulne ss of the under study area based on standard deviation and to make priority in creating security in the area. The nearest neighbor and K-function output analysis consist of four curves and regarding the fact that for all fatal, injury and property damage only crashes, Accidents in the study region are more clustered than expected by random chance. The importance of this study is to use GIS as a management system for accident analysis by combination of spatial-statistical methods.