The Identification and prioritization of accident-prone locations: A multi-criteria framework for analyzing traffic accidents in an urban environment

A heuristic approach

Abstract

The identification of road traffic accidents (RTAs) and the prioritization of accident-prone locations is a consolidated practice in road safety analysis on highways; however, this type of approach requires improvements in the urban environment. This work proposes a framework for identifying and prioritizing accident-prone locations in urban areas through heuristic approaches. This framework adopts three heuristic approaches chained. The first approach consists of a clustering model through the Affinity Propagation Clustering (APC) method that seeks to generate units of analysis that best correspond to the spatial distribution of accidents. The second approach identifies candidates for APLs through the spatial association between neighboring UAs with a high frequency of RTAs provided by the Local Moran's Index. Finally, UAs are classified as APLs based on temporal dynamics by identifying Change Point Patterns (CPP) that describe different frequency distributions of RTAs over time. The results present the APC as a suitable approach for identifying the distribution pattern of RTAs and easy calibration; moreover, the spatial association metrics provided clusters of APL candidates. The CPP proved to be an efficient mechanism for identifying emerging APLs classified as APLs. This versatility provided an easy-to-apply framework that requires low-complexity information to execute