An improved approach for association rule mining using a multi-criteria decision support system: a case study in road safety

Abstract

Purpose Road 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. Methods This 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. Conclusion An 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.

Data mining combined to the multicriteria decision analysis for the improvement of road safety: case of France

“…DM techniques are used in different research domains and areas and have provided useful results to extract the hidden knowledge for the decision makers. For instance, we can find in [11][12][13][14] several researches and studies on the application of the association rules for various problems, and in particular the problem of safety of the road traffic [15][16][17][18][19][20][21]. Besides, according to the national observatory of road safety [22][23][24], at least 350,000 people died since 1960 as a result of a road accident in France: in 1972, there were 18,034 official deaths.…”

Mining and prioritization of association rules for big data: multi-criteria decision analysis approach

“…Empirical study: road accident analysis (Table 3) in order to identify the main factors that affect road accident [26][27][28][29][30]. The variables describe characteristics related to the accident (type and cause), the driver (age, sex, and experience), vehicle (age and type), road (condition and geometry), time, season, number of injuries/death, etc.…”

A Web-Based Platform for Mining and Ranking Association Rules

“…In this demo, we (1) implement a baseline approach to extract association rules from given data, (2) propose an approach based on multiple criteria analysis to rank the extracted rules according to the decision maker's preferences, (3) demonstrate the idea of association rules mining through road accident data as a case study, where the dataset containing information on location, drivers, and the accident characteristics, the vehicles involved and victims [2], and (4) present the preliminary results of the proposed system. Our implementation will be opensource and a live demo can be found at https://youtu.be/QILaVUghlsM.…”