Citation

Ghadi M, Török. Transp. Res. Proc. 2017; 27: 1105-1112.

Copyright

(Copyright © 2017, Elsevier Publications)

DOI

10.1016/j.trpro.2017.12.104

PMID

unavailable

Abstract

The identification of road sections characterized by high risk accidents is the first step for any successful road safety management process, considering the limited available resources. Although researchers started to study black spot decades ago, there are many un-clarified questions in this field. In the identification process of black spots three main methods can be used: screening methods, clustering methods and crash prediction methods. Many literatures and case studies were written describing each method pros or cons. These literatures concentrate mostly on one type of road each time, although road characteristics (i.e. speed, ADT) can highly affect the success and precision of the applied method. Therefore, the most important question to be answered is which method for which road?. This question can be answered by comparing different applied methods for different road types. However the comparison of different methods is still not adequately explored areas. This article aims to compare different methods used in identifying black spot; the sliding window and the spatial autocorrelation for two types of roads differ in their average speed, where speed is one of the important road characteristics which is still not adequately explored. The result shows a preference to use the sliding window for identifying black spot in high speed roads and the lack of preference to use it in low speed roads, and vice versa for spatial autocorrelation method, following accidents distribution pattern. And a result of a weakness in applying Empirical Bayesian in high speed road is also included.

Language: en

Keywords

Accident Pattern; Black Spot; High Speed Road; Low Speed Road; Sliding Window; Spatial Autocorrelation