Spatial Analysis for the Classification of Prone Roads Traffic Accidents: A Systematic Literature Review

Abstract

Identifying prone road traffic accidents (PRTA) has been based on the total number of accidents data. Determining road names that have not been appropriately approved makes the data biased. Many researchers have reviewed many factors, spatial methods of analysis, and ways to improve past traffic strategies. The searching method with a systematic literature review (SLR) was conducted on seven publishers of the traffic accident classification database. They are ACM Digital Library, IEEE e-Xplore, ScienceDirect, Springer, Sage, Taylor & Francis, and Wiley, then produced 189 major relevant studies to the findings of this study. SLR is used to find the most relevant journals, research topics, trends in the field, multi-criteria spatial dataset parameters, estimation methods, trends, the best methods currently, proposed improvement methods, and the most commonly used efforts to determine in a collection of road traffic accidents. The study results obtained that multi-criteria spatial data were developed in different spatial analyses. The SLR mapping results found gaps for hybrid two types of classification methods on multi-criteria decision making (MCDM) and Spatial Multi-level Classification. The consistency test of many methods is done by the Consistency Test Method (MCT), the value of Precision-Recall Accuracy (ARC), and Site Consistency Test (SCT).