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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/7016

Title: Profiling high-frequency accident locations using association rules
Authors: GEURTS, Karolien
WETS, Geert
Issue Date: 2003
Publisher: TRB - National Academy of Sciences
Citation: Electronic proceedings of the 82nd Annual Transportation Research Board.
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 ram, 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 characeristics at low frequency accident locations. Furthermore, these patterns are analysed and compared with frequently occurring 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 an 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 frequenly occurring accident patterns. These factors play an important role in identifying traffic safety problems. However, the most discriminating accident characteristics between high frequency accident characteristics between high frequency accident locations and low frequency accident locations are mainly related to infrastructure and location characteristics.
URI: http://hdl.handle.net/1942/7016
ISBN: 0-309-08581-0
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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