In Belgium, traffic safety is one of the government's highest priorities. The identification and profiling of black spots and black zones (geographical locations with high concentrations of traffic accidents) in terms of accident-related data and location characteristics must provide new insights into the complexity and causes of road accidents, which, in turn, provide valuable input for governmental actions. Association rules were used to identify accident-related circumstances that frequently occur together at high-frequency accident locations. Furthermore, these patterns were analyzed and compared with frequently occurring accident-related characteristics at low-frequency accident locations. The strength of this approach lies with the identification of relevant variables that make a strong contribution toward obtaining a better understanding of accident circumstances and the discerning of descriptive accident patterns from more discriminating accident circumstances to profile black spots and black zones. This data-mining algorithm is particularly useful in the context of large data sets for road accidents, since data mining can be described as the extraction of information from large amounts of data. The results showed that human and behavioral aspects are of great importance in the analysis of frequently occurring accident patterns. These factors play an important role in identifying traffic safety problems in general. However, the accident characteristics that were the most discriminating between high-frequency and low-frequency accident locations are mainly related to infrastructure and location.
Abstract