摘要
Identifying high-crash-risk road segments provides safety specialists with an insight to better understanding of crash patterns and enhancing road safety management.The common hotspot identification methods are not robust enough to visualize the underlying shape of crash patterns since they neglect the spatial properties of crash data.Spatial traffic accidents have the tendency to be dependent,a phenomenon known as spatial autocorrelation.Values over distance are more or less similar than expected for randomly associated observations.Modeling the spatial variations can properly be explained in terms of first-and second-order properties.The first-order properties,describe the way of varying the expected value of point pattern in space which can be due to changes in the substantial properties of the local environment,while second-order effects describe the interactive effects of events explaining on how the events are interacted.Considering the discrete nature of crash data and the limited access to exact locations where crashes occur,it is likely that a continuous surface drawn from discrete points will better reflect crash density,present a more realistic picture of crash distribution.Network kernel density estimation(NKDE)is a nonparametric approach for events distributed over one-dimensional space which facilitates estimating the density at any location in the study region not just at the location where the event occurs.NKDE for road safety applications enables the extraction and visualization of crash density along roadways The application of suggested method was illustrated for Arak-Khomein rural road in Markazi province,Iran and the stability of hazardous segments by examining the resulted network estimated density during the three years of study(2006–2008)was investigated.The result of this paper helps the traffic engineers and safety specialists to determine the segments which demand more safety attentions from both transportation authorities and drivers and request assigning the resources such as budget and time.
