Abstract
PurposeRoad accidents have come to be considered a major public health problem worldwide. The aim of many studies is therefore to identify the main factors contributing to the severity of crashes.MethodsThis paper examines a large-scale data mining technique known as association rule mining, which can predict future accidents in advance and allow drivers to avoid the dangers. However, this technique produces a very large number of decision rules, preventing decision makers from making their own selection of the most relevant rules. In this context, the integration of a multi-criteria decision analysis approach would be particularly useful for decision makers affected by the redundancy of the extracted rules.ConclusionAn analysis of road accidents in the province of Marrakech (Morocco) between 2004 and 2014 shows that the proposed approach serves this purpose; it may provide meaningful information that could help in developing suitable prevention policies to improve road safety.
Highlights
Data mining is defined as a non-trivial process of identifying valid, novel, potentially useful and understandable patterns in data [1]
Many researchers [3,4,5,6,7] have studied the application of data mining techniques in the domain of road accidents through association rules mining
This paper proposes an approach to association rule mining-based Multi-criteria decision analysis (MCDA) for analyzing road accident data
Summary
Data mining is defined as a non-trivial process of identifying valid, novel, potentially useful and understandable patterns in data [1] It is a vital part of business analytics and the most important trends in information technology. The goal of the proposed approach is not to optimize road safety, but to generate insights and sufficient knowledge to enable decision makers to make the right optimization decision to avoid dangerous routes and improve road safety This approach consists of two major steps; a rules generator using the Apriori algorithm to extract association rules, and multi-criteria decision analysis to evaluate and select the interesting rules from the large set extracted
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