Profiling high frequency accident locations using associations rules

Abstract

In Belgium, traffic safety is currently one of the government’s highest priorities. Identifying and profiling black spots and black zones 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 government actions. In this paper, association rules are used to identify accident circumstances that frequently occur together at high frequency accident locations. Furthermore, these patterns are analysed and compared with frequently occurring accident characteristics at low frequency accident locations. The strength of this approach lies within the identification of relevant variables that make a strong contribution towards 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. The use of this data mining algorithm is particularly useful in the context of large datasets on road accidents, since data mining can be described as the extraction of information from large amounts of data. Results show that human and behavioural aspects are of great importance when analysing frequently occurring accident patterns. These factors play an important role in identifying traffic safety problems in general. However, the most discriminating accident characteristics between high frequency accident locations and low frequency accident locations are mainly related to infrastructure and location characteristics.