Abstract
In road safety, the process of organizing road infrastructurenetwork data into homogenous entities is called segmentation.Segmenting a road network is considered thefirst and most important step in developing a safety performancefunction (SPF). This article aims to study the benefitof a newly developed network segmentation method which is based on the generation of accident groups applying K-means clustering approach. K-means algorithm has been used to identify the structure of homogeneous accident groups. According to the main assumption of the proposed clustering method, the risk of accidents is strongly influenced by the spatial interdependence and traffic attributes of the accidents. The performance of K-means clustering was compared with four other segmentation methods applying constant average annual daily traffic segments, constant length segments, related curvature characteristics and a multivariable method suggested by the Highway Safety Manual (HSM). The SPF was used to evaluate the performance of the five segmentation methods in predicting accident frequency. K-means clustering-based segmentation method has been proved to be more flexible and accurate than the other models in identifying homogeneous infrastructure segments with similar safety characteristics.
Highlights
AND LITERATURE REVIEWAn appropriate safety performance function (SPF) is considered to be one of the basic methods of road safety analysis [1]
While, trying to search for more homogeneous segments [4], other scholars have introduced other segmentation processes based on different road infrastructure attributes, i.e., speed, number of lanes, average annual daily traffic (AADT)
It was concluded that segmentation methods based on design parameters are better in developing the SPF than others since the set of high-risk sections provided by them is deemed to be well correlated with the set of locations characterized by high accident density
Summary
AND LITERATURE REVIEWAn appropriate safety performance function (SPF) is considered to be one of the basic methods of road safety analysis [1]. The SPF represents a mathematical relationship between accident frequency and other related explanatory variables for different road segments. While, trying to search for more homogeneous segments [4], other scholars have introduced other segmentation processes based on different road infrastructure attributes, i.e., speed, number of lanes, average annual daily traffic (AADT). Some scholars [5, 6] have argued that applying different lengths and start points for segmenting road network can result in different definitions of hazardous locations which in turn affect the stability of results. Koorey (2009) [7] discussed the benefit of applying variable length segments and their effect on locating high-risk road sites. Cafiso et al (2013) [8] compared the efficiency of different SPFs created from five segmentation approaches, which segmented the road based on geometric and/or traffic related attributes. It was concluded that segmentation methods based on design parameters (i.e., curvature characteristics) are better in developing the SPF than others since the set of high-risk sections provided by them is deemed to be well correlated with the set of locations characterized by high accident density
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