In Flanders, Belgium, approximately 1,014 accident locations are considered dangerous. These dangerous accident sites, or black spots, are selected by means of historic accident records for the period 1997 to 1999. A combination of weighting values, respectively, 1 for each light injury, 3 for each serious injury, and 5 for each deadly injury (1_3_5), is used to rank and select the most dangerous accident locations. A sensitivity analysis was performed to investigate the effect on the identification and ranking of black spots as based on three different weighting value combinations, representing a different attitude toward the traffic safety problem: avoiding all accidents (1_1_1), all deadly accidents (1_1_10), and all accidents with serious or deadly injuries (1_10_10). Furthermore, effects of use of the expected number of accidents, estimated from a hierarchical Bayesian model, instead of the historic count data, to rank and select the accidents sites, were evaluated. Results show that a different attitude toward the traffic safety problem and the choice of the corresponding injury weighting values on the one hand and use of estimates instead of count values on the other hand do have important consequences for the selection and ranking of black spots. This is important not only for the number of accident locations that will receive a different ranking order but also for the effect on the type of accident locations that are selected as dangerous and accordingly for the resulting future traffic safety decisions.
In recent years, data mining researchers have developed efficient association rule algorithms for retail market basket analysis. Still, retailers often complain about how to adopt association rules to optimize concrete retail marketing-mix decisions. It is in this context that, in a previous paper, the authors have introduced a product selection model called PROFSET. 1 This model selects the most interesting products from a product assortment based on their cross-selling potential given some retailer defined constraints. However this model suffered from an important deficiency: it could not deal effectively with supermarket data, and no provisions were taken to include retail category management principles. Therefore, in this paper, the authors present an important generalization of the existing model in order to make it suitable for supermarket data as well, and to enable retailers to add category restrictions to the model. Experiments on real world data obtained from a Belgian supermarket chain produce very promising results and demonstrate the effectiveness of the generalized PROFSET model. * Tom Brijs is a research fellow of the Fund for Scientific Research Flanders. 1 PROFSET stands for PROFitability per SET because the optimization model is based on the calculation of the profitability per frequent set in order to determine the cross-selling potential between products.
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