Comparison of Accident Severity and Frequency Index Method in Identifying Hotspot Segments of Intercity Road Network
محورهای موضوعی : فصلنامه علمی پژوهشی سنجش از دور راداری و نوری و سیستم اطلاعات جغرافیاییHasan khaksar 1 , Seyed Ahmad Almasi 2 , Ali Asghar Goharpor 3
1 - Associate Professor in Department of Transportation Engineering and Planning, School of Civil Engineering, Islamic Azad University, North Tehran Branch
2 - Ph.D. candidate in department of Transportation Engineering and Planning, Department of Civil Engineering, Imam Khomeini International University, Qazvin, Iran,
3 - Ph.D. candidate in department of Transportation Engineering and Planning, School of Civil Engineering, Iran University of Sciense and Technology (IUST),
کلید واژه: Crash, Kernel Density Estimation (KDE), Intercity Network, Severity Index,
چکیده مقاله :
This study presents an integrated method for identifying an important segment of traffic accidents in an intercity road network. The spatial analysis method is known as network Kernel Density Estimation (KDE). The importance of critical accident rate is that it takes into account several factors including exposure rates, type of road section, variance of accident data, etc. Also, in this study, we compared the results of the road severity index with the Kernel Density Estimation method. The results of the study were obtained for two almost identical models. We found that the key points determined by the Kernel Density Estimation method reflect the severely problematic component segments, and filter out components that are not vulnerable. This approach can help transportation officials and safety professionals prioritize locations that need more safety attention.
This study presents an integrated method for identifying an important segment of traffic accidents in an intercity road network. The spatial analysis method is known as network Kernel Density Estimation (KDE). The importance of critical accident rate is that it takes into account several factors including exposure rates, type of road section, variance of accident data, etc. Also, in this study, we compared the results of the road severity index with the Kernel Density Estimation method. The results of the study were obtained for two almost identical models. We found that the key points determined by the Kernel Density Estimation method reflect the severely problematic component segments, and filter out components that are not vulnerable. This approach can help transportation officials and safety professionals prioritize locations that need more safety attention.
